Pest detection and monitoring system

Abstract

A pest detection and monitoring system (1000) includes at least one pest detection device (100) having a support structure (104) mounting various components. In addition, the device (100) includes an attractant unit (112) to attract pests, a capture unit (110) disposed around the attractant unit (112) to immobilize pests, and an imaging unit (114) below the capture unit (110). Further, a collection receptacle (116) positioned below the imaging unit (114) receives immobilized pests. An illumination unit (118) proximal to the imaging unit (114) illuminates its field of view. Further, the device (100) includes a control unit with first processor and first memory unit, and communication interface. A server (200) receives transmitted data. Furthermore, a monitoring device (300) with second processor and second memory unit processes captured images to identify pest species, determine pest population parameters, and generate pest management recommendations transmitted to user devices (400) for pest management decisions.

Description

TITLE OF THE INVENTION:PEST DETECTION AND MONITORING SYSTEMCROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITYThe present application claims priority from Indian provisional application having application number 202411067444 and filed on 19thday of September, 2024.TECHNICAL FIELDThe present disclosure relates to an agricultural system. More particularly, the present disclosure relates to a pest detection and monitoring system.BACKGROUNDThis section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present disclosure that are described or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements in this background section are to be read in this light, and not as admissions of prior art. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.Agricultural practices face significant challenges from various harmful organisms that damage crops throughout the growing cycle. Traditional pest management approaches rely heavily on manual field scouting and visual inspection methods, which are labor-intensive, time-consuming, and often fail to detect pest infestations until substantial crop damage has already occurred. The timing of pest detection is critical, as delayed identification allows pest populations to multiply exponentially, making subsequent control efforts more difficult and expensive. Current monitoring practices typically involve periodic field visits by agricultural workers or consultants who must physically inspect crops across large cultivation areas, leading to incomplete coverage and missed infestations in remote field sections. Furthermore, accurate identification of pest species requires specialized expertise that may not be readily available in many agricultural regions, resulting in misidentification and inappropriate treatment selections.Existing automated monitoring systems suffer from numerous technical and operational limitations that restrict their effectiveness in real-world agricultural environments. Most available monitoring devices require connection to electrical grid infrastructure, making them impractical for deployment in remote agricultural fields where power lines are unavailable. Battery-operated devices face frequent power depletion issues, requiring regular battery replacement that increases maintenance burden and operational costs. Current monitoring systems typically employ basic detection mechanisms that cannot distinguish between different organism species, leading to non- selective capture of both harmful and beneficial organisms. The data collection capabilities of existing systems are limited to simple counting or presence / absence detection, without providing comprehensive information about pest species, population dynamics, or environmental correlations necessary for effective management decisions.The data processing and analysis capabilities of conventional monitoring systems remain rudimentary, lacking integration with advanced analytical tools or predictive modeling frameworks. Existing systems operate as isolated units without network connectivity, preventing centralized data aggregation and regional pest monitoring. The absence of real-time data transmission capabilities means that pest detection information reaches decision-makers only after significant delays, reducing the opportunity for timely intervention. Current systems fail to incorporate environmental monitoring, missing critical factors that influence pest population dynamics and outbreak risks. Additionally, existing monitoring solutions do not provide actionable measures, leaving farmers to independently determine appropriate response strategies without adequate decision support tools.In light of the foregoing discussion, there exists a need for an improved system and method which can address at least one of the above discussed requirements.SUMMARYBefore the present system and method and its components are summarized, it is to be understood that this disclosure is not limited to the system and its arrangement as described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the versions or embodiments only and is not intended to limit the scope of the present disclosure. This summary is not intendedto identify essential features of the claimed subject matter nor is it intended for use in detecting or limiting the scope of the claimed subject matter.In an example aspect of the present disclosure, a pest detection and monitoring system is disclosed. The pest detection and monitoring system includes at least one pest detection device that includes a support structure configured to mount components of the at least one pest detection device. In addition, the at least one pest detection device includes an attractant unit configured to attract pests and a capture unit disposed around the attractant unit. The capture unit is configured to immobilize pests attracted by the attractant unit. Further, the at least one pest detection device includes an imaging unit positioned below the capture unit and a collection receptacle configured to receive immobilized pests. The collection receptacle is positioned below the imaging unit to enable the imaging unit to capture images of pests collected in the collection receptacle. In addition, the at least one pest detection device includes an illumination unit positioned proximal to the imaging unit and configured to illuminate a field of view of the imaging unit. Further, the at least one pest detection device includes a power generation unit configured to generate electrical power and a power storage unit electrically connected to the power generation unit. The power storage unit is configured to store electrical power and provide power backup. Furthermore, the at least one pest detection device includes a control unit that includes one or more first processor and one or more first memory unit. The one or more first processor is configured to control operation of the at least one pest detection device. Also, the at least one pest detection device includes a communication interface operatively connected to the control unit and configured to transmit data including the captured images. In addition, the pest detection and monitoring system further includes a server configured to receive and store the data transmitted from the communication interface. Further, the pest detection and monitoring system includes a monitoring device operatively connected to the server. The monitoring device includes one or more second processor and one or more second memory unit storing instructions that, when executed by the one or more second processor, cause the monitoring device to process the captured images to identify pest species and determine pest population parameters, and generate pest management recommendations based on the identified pest species and pest population parameters. The monitoring device transmits the pest management recommendations to one or more user devices to enable pest management decisions.In another example aspect of the present disclosure, a method for pest detection and monitoring using a pest detection and monitoring system is disclosed. The method includes a step of attracting pests using an attractant unit. The method includes another step of immobilizing pests attracted by the attractant unit using a capture unit disposed around the attractant unit. The method includes yetanother step of receiving immobilized pests in a collection receptacle. The method includes yet another step of capturing images of pests collected in the collection receptacle using an imaging unit. The method includes yet another step of illuminating a field of view of the imaging unit using an illumination unit. The method includes yet another step of transmitting data including the captured images using a communication interface. The method includes yet another step of receiving and storing the data transmitted from the communication interface using a server. The method includes yet another step of processing the captured images to identify pest species and determine pest population parameters using a monitoring device operatively connected to the server. Further, the monitoring device includes one or more second processor and one or more second memory unit storing instructions that, when executed by the one or more second processor, cause the monitoring device to perform the processing. The method includes yet another step of generating pest management recommendations based on the identified pest species and pest population parameters. The method includes yet another step of transmitting the pest management recommendations to one or more user devices to enable pest management decisions.BRIEF DESCRIPTION OF FIGURESHaving thus described the disclosure in general terms, references will now be made to the accompanying figures, wherein:FIG. 1 illustrates a block diagram for a pest detection and monitoring system, in accordance with various embodiments of the present subject matter;FIG. 2 illustrates a pest detection device, in accordance with various embodiments of the present subject matter;FIG. 3 illustrates a bottom view of an imaging unit, in accordance with various embodiments of the present subject matter;FIG. 4 illustrates a bottom view of a capture unit, in accordance with various embodiments of the present subject matter; andFIG. 5 illustrates a flow chart of a method for pest detection and monitoring using the pest detection and monitoring system, in accordance with various embodiments of the present subject matter.It should be noted that the accompanying figures are intended to present illustrations of exemplary embodiments of the present disclosure. These figures are not intended to limit the scope of the present disclosure. It should also be noted that accompanying figures are not necessarily drawn to scale.DETAILED DESCRIPTIONReference will now be made in more detail to embodiments, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout the specification. In this regard, the present embodiments may have different forms and should not be construed as being limited to the descriptions set forth herein. Accordingly, the embodiments are merely described below, by referring to the figures, to explain aspects of embodiments of the present description. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. Throughout the present disclosure, the expression "at least one of a, b and c" indicates only a, only b, only c, both a and b, both a and c, both b and c, all of a, b, and c, or variations thereof.The subject matter of the present disclosure may include various modifications and various embodiments, and example embodiments will be illustrated in the drawings and described in more detail in the detailed description. Effects and features of the subject matter of the present disclosure, and implementation methods therefor will become clear with reference to the embodiments described herein below together with the drawings. The subject matter of the present disclosure may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.Hereinafter, embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. The same or corresponding elements will be denoted by the same reference numerals, and thus, redundant description thereof will not be repeated.It will be understood that although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another.An expression used in the singular may also encompasses the expression of the plural, unless it has a clearly different meaning in the context.In the following embodiments, it is to be understood that the terms such as "including," "includes," "having," "comprises," and "comprising," are intended to indicate the existence of the features or elements disclosed in the specification, and are not intended to preclude the possibility that one or more other features or elements may exist or may be added.An objective of the present disclosure is to provide a pest detection and monitoring system (1000) that enables real-time, automated pest detection and identification in agricultural fields using artificial intelligence and machine learning techniques.Another objective of the present disclosure is to generate predictive pest outbreak assessments and timely pest management recommendations by correlating pest data with environmental parameters.Yet another objective of the present disclosure is to provide a solar-powered, autonomous solution that operates without external power infrastructure, making it accessible to farmers in remote locations.Yet another objective of the present disclosure is to create a centralized monitoring network that aggregates pest data from multiple geographic locations for comprehensive pest distribution analysis.Yet another objective of the present disclosure is to enable data-driven pest management decisions that reduce crop losses and optimize pesticide usage through early detection and continuous monitoring.Referring to FIG. 1, the pest detection and monitoring system (1000) is disclosed, in accordance with various embodiments of the present subject matter. The pest detection and monitoring system (1000) includes at least one pest detection device (100), a server (200), and a monitoring device (300). The at least one pest detection device (100) is configured to detect, capture, and image pests within a specific geographic area and transmit pest data to the server (200). In some embodiments, the server (200) is connected to a plurality of pest detection devices (100) deployed across different geographic locations. The plurality of pest detection devices (100) are interconnected through the server (200), enabling comprehensive pest distribution monitoring. The monitoring device (300) is operatively connected to the server (200) and configured to process the data received from the server (200). In one example embodiment, the monitoring device (300) and the server (200) may be provided as an integral system (shown as a dotted block enclosing the monitoring device (300) and the server (200) in FIG. 1). In another example embodiment, the monitoring device (300) and the server (200) may be provided as separate systems. In an embodiment, the monitoring device (300) includes one or more second processor and one or more second memory unit storing instructions that, when executed by the one or more second processor, cause the monitoring device (300) to process captured images to identify pest species and determine pest population parameters. Furthermore, the monitoring device (300) generates pest management recommendations based on the identified pest species and pest population parameters, and transmits these recommendations to one or more user devices (400) to enable informed pest management decisions.Referring to FIG. 2, the pest detection device (100) is disclosed, in accordance with various embodiments of the present subject matter. FIG. 3 illustrates a bottom view (114a) of an imaging unit (114), in accordance with various embodiments of the present subject matter. FIG. 4 illustrates a bottom view (110a) of a capture unit (110), in accordance with various embodiments of the present subject matter. The pest detection device (100) includes a support structure (104) configured to mount various components of the pest detection device (100). The support structure (104) may be implemented as a pole, tower, frame, or any suitable mounting structure capable of supporting the device components. The support structure (104) is adapted to mount at least one of an attractant unit (112), the capture unit (110), the imaging unit (114), a collection receptacle (116), an illumination unit (118), a power generation unit (102), a power storage unit (108), a control unit, and a communication interface.The pest detection device (100) includes the attractant unit (112) configured to attract pests. For example, the attractant unit (112) includes an ultraviolet (UV) light source configured to emit UV radiation to attract nocturnal pests. In one example, the UV light source may include UV LEDs, UV lamps, or any suitable UV emitting elements. In some embodiments, the attractant unit (112) may include multiple UV LEDs for sufficient illumination to attract pests during low visibility conditions. In another example, the attractant unit (112) may include at least one of LED lamps, lure apparatus, pheromone dispensing units (124), colored visual attractants, or any combination thereof to attract different pest species. The attractant unit (112) is powered by electrical supply provided by at least one of the power generation unit (102), the power storage unit (108), or a combination thereof. The attractant unit (112) is operatively connected to the control unit, which controls activation and deactivation of the attractant unit (112) at predefined time intervals.The capture unit (110) is disposed around the attractant unit (112) and configured to immobilize pests attracted by the attractant unit (112). In one embodiment, the capture unit (110) includes an electrified mesh structure configured to deliver an electrical stimulus to immobilize pests. The electrified mesh structure provides a controlled, low-intensity electrical stimulus that temporarily stuns pests without causing permanent harm. The electrical stimulus is sufficient to immobilize the pests for a specific duration, allowing them to fall into the collection receptacle (116). The capture unit (110) may be arranged in various geometric configurations including, but not limited to, rectangular, circular, triangular, or polygonal arrangements around the attractant unit (112). The capture unit (110) is operatively connected to the control unit and a precipitation sensor (106). In some examples, the precipitation sensor (106) may correspond to a rain sensor. The control unitis configured to discontinue power supply to the capture unit (110) upon detection of a precipitation event to prevent electrical hazards.The imaging unit (114) is positioned below the capture unit (110) and configured to capture images of pests. In one embodiment, the imaging unit (114) is positioned at a lower end of the attractant unit (112) to have a clear field of view of the collection receptacle (116). The imaging unit (114) includes a high-resolution digital camera capable of capturing detailed images of pests for accurate species identification. For example, the imaging unit (114) may include image sensors with various resolution capabilities suitable for pest identification. The imaging unit (114) is triggered by the control unit to capture images at predefined intervals based on pest activity patterns and monitoring requirements.In an embodiment, the illumination unit (118) is positioned proximal to the imaging unit (114) and configured to illuminate the field of view of the imaging unit (114). The illumination unit (118) includes LED elements arranged in a geometric pattern around a periphery of the imaging unit (114). For example, the geometric pattern may include square, rectangular, circular, or any suitable arrangement that provides uniform illumination. The illumination unit (118) ensures adequate lighting for clear image capture during low-light conditions, nighttime operations, or cloudy weather. The control unit activates the illumination unit (118) based on ambient light conditions detected by light sensors or according to predefined schedules synchronized with image capture events.In some embodiments, the collection receptacle (116) is configured to receive immobilized pests and is positioned below the imaging unit (114) to enable the imaging unit (114) to capture images of pests collected therein. For example, the collection receptacle (116) may be implemented as a tray, tub, container, or any suitable receptacle for collecting pests. The collection receptacle (116) is designed with appropriate dimensions to accommodate expected pest volumes while maintaining visibility for the imaging unit (114). In some embodiments, the collection receptacle (116) includes a transparent or semi-transparent bottom surface to facilitate imaging from below. In some embodiments, the collection receptacle (116) may include drainage apertures to prevent water accumulation during precipitation events.The power generation unit (102) is configured to generate electrical power for the pest detection device (100). In one example, the power generation unit (102) includes a photovoltaic panel configured to convert solar radiation into electrical power. The photovoltaic panel may have various power ratings based on the power requirements of the pest detection device (100). The power generation unit (102) includes a charge controller configured to regulate charging of thepower storage unit (108) and prevent overcharging. The photovoltaic panel may be mounted on top of the support structure (104) or at any suitable position for optimal solar exposure. The orientation and tilt angle of the photovoltaic panel may be adjustable to maximize solar energy collection based on geographic location and seasonal variations.In an embodiment, the power storage unit (108) is electrically connected to the power generation unit (102) and configured to store electrical power and provide power backup. In one embodiment, the power storage unit (108) includes a removable battery module that can be easily replaced or serviced. For example, the power storage unit (108) may include lithium-ion batteries, lithium iron phosphate batteries, lead-acid batteries, or any suitable rechargeable battery technology. The power storage unit (108) may include a charging interface configured to receive electrical power from an external power source, enabling charging from grid electricity or portable generators when solar power is insufficient. The removable battery module design allows for battery swapping, wherein a discharged battery can be replaced with a charged battery to ensure continuous operation.In some embodiments, the control unit includes one or more first processor and one or more first memory unit. The one or more first processor is configured to control operation of the at least one pest detection device (100). The control unit manages the coordination and timing of all device components, including activation of the attractant unit (112), capture unit (110), imaging unit (114), and illumination unit (118). The one or more first memory unit is configured to store captured images, sensor data, operating parameters, and control instructions. The memory capacity may be selected based on data storage requirements and transmission frequency. The control unit executes programmed routines for automated device operation, including scheduled pest attraction cycles, image capture sequences, and data transmission protocols.In an embodiment, the communication interface is operatively connected to the control unit and configured to transmit data including the captured images to the server (200). The communication interface supports multiple communication protocols to ensure reliable data transmission under various network conditions. The communication interface is configured to transmit data via at least one of cellular network communication protocols, wireless local area network protocols, satellite communication protocols, short-range wireless communication protocols, long-range wireless communication protocols, wired communication protocols, or any combination thereof. In some examples, the pest detection device (100) may include a SIM (subscriber identity module) slot or be adapted to receive an eSIM (embedded SIM) for cellular connectivity. In an embodiment of thepresent disclosure, the communication interface implements data compression and encryption protocols to optimize bandwidth usage and ensure data security during transmission.In general, the wired communication protocols may include but are not limited to ethemet, fiber optics, USB (universal serial bus), RS-232 (serial communication), RS-485, CAN (controller area network), power-line communication, and the like. In some examples, the cellular network communication protocols may include but are not limited to 3G, 4G, 5G, 6G, LTE, GSM, CDMA, NB-IoT, and the like. In one example, the wireless local area network protocols may include but are not limited to Wi-Fi standards (IEEE 802.1 la / b / g / n / ac / ax), Wi-Fi Direct, Wi-Fi HaLow (IEEE 802.11ah), Li-Fi, and the like. In some examples, the satellite communication protocols may include but are not limited to VS AT, Inmarsat, Iridium, and the like. Generally, the short-range wireless communication protocols may include but are not limited to Bluetooth, Zigbee, NFC, RFID, and the like. In general, the long-range wireless communication protocols may include but are not limited to LoRaWAN, Sigfox, NB-IoT, and the like.In some embodiments, the pest detection device (100) includes a precipitation sensor (106) configured to detect precipitation events. For example, the precipitation sensor (106) may utilize capacitive, resistive, or optical sensing technologies to detect the presence of rainfall. Upon detection of a precipitation event, the precipitation sensor (106) sends a signal to the control unit, which is configured to discontinue power supply to the capture unit (110) and / or the attractant unit (112) to prevent electrical short circuits and equipment damage. The precipitation sensor (106) may also trigger protective mechanisms such as closing protective covers over sensitive electronic components.In some embodiments, the pest detection device (100) includes a weather monitoring station (120) configured to measure environmental parameters. The environmental parameters include at least one of temperature, humidity, rainfall, wind speed, wind direction, atmospheric pressure, or any combination thereof. The weather monitoring station (120) provides localized weather data that is transmitted along with pest data to enable correlation analysis. The weather sensors measure various parameters within their respective operational ranges with appropriate accuracy levels. The weather data is sampled at regular intervals based on monitoring requirements.In some embodiments, the pest detection device (100) includes one or more adhesive trap surfaces (122) configured to capture pests. The adhesive trap surfaces (122) utilize colored surfaces coated with non-drying adhesive to attract and capture specific pest species. The one or more adhesive trap surfaces (122) includes at least one of a first colored adhesive surface configured to attract a first group of pest species or a second colored adhesive surface configured to attract a second groupof pest species. For example, yellow-colored adhesive surfaces are particularly effective for attracting certain pest types such as whiteflies and aphids, while blue-colored adhesive surfaces are effective for attracting other pest species such as thrips. The adhesive trap surfaces (122) may have various dimensions based on deployment requirements.In an embodiment, the pest detection device (100) includes a movement mechanism configured to position the one or more adhesive trap surfaces (122) into the field of view of the imaging unit (114) at predefined intervals. For example, the movement mechanism includes an electric motor (126), which may be implemented as a servo motor, stepper motor, or any suitable motor with precise position control. The electric motor (126) is configured to rotate and / or laterally move the adhesive trap surfaces (122) to enable imaging of captured pests without requiring multiple imaging units. The movement mechanism may provide rotational or linear movement capability. The control unit coordinates the movement mechanism with the imaging unit (114) to capture images of different adhesive trap surfaces (122) sequentially. The predefined intervals for movement are based on pest capture rates and monitoring requirements.In some embodiments, the pest detection device (100) includes a pheromone dispensing unit (124) as part of or in addition to the attractant unit (112). The pheromone dispensing unit (124) utilizes synthetic pheromones that mimic insect mating signals or aggregation pheromones to attract specific pest species. The pheromones are selected based on target pest species relevant to the crops being monitored. The pheromone dispensing unit (124) may include controlled-release dispensers that maintain consistent pheromone emission rates over extended periods. The pheromone dispensing unit (124) provides highly selective pest attraction, reducing capture of non-target species and improving monitoring accuracy.In some implementations, the pest detection device (100) includes a diagnostic circuit configured to perform self-diagnostic tests on components of the pest detection device (100) upon system initialization. The diagnostic circuit systematically tests each major component including the attractant unit (112), capture unit (110), imaging unit (114), illumination unit (118), communication interface, and sensors. The diagnostic tests verify component functionality, electrical continuity, and communication protocols. The diagnostic circuit generates diagnostic status information indicating the operational status of each component, which is transmitted to the server (200). This enables remote troubleshooting and predictive maintenance, reducing downtime and service requirements. For example, the diagnostic routine may be triggered automatically upon power-on, at scheduled intervals, or remotely by the monitoring device (300).In one embodiment, the pest detection device (100) may include an internal temperature sensor configured to monitor operating temperature of electronic components. The internal temperature sensor measures temperature within the device enclosure to ensure components operate within safe temperature ranges. Temperature thresholds are defined in the control unit based on component specifications. The pest detection device (100) includes one or more cooling units configured to maintain the operating temperature within a predefined range. The cooling units may include cooling fans, heat sinks, thermal vents, or any suitable thermal management components. The cooling units are activated when internal temperature exceeds upper threshold values, preventing thermal damage to sensitive electronics. In extreme temperature conditions, the control unit may implement protective measures such as reducing operational duty cycles or temporarily suspending non-critical functions.In some embodiments, the pest detection device (100) may include a cleaning mechanism configured to automatically remove accumulated pests from the collection receptacle (116) at predefined intervals. The cleaning mechanism includes at least one of a compressed air dispensing unit or a mechanical sweeping unit driven by a servo motor. The compressed air dispensing unit releases bursts of compressed air to blow accumulated pests out of the collection receptacle (116) through discharge ports. For example, the mechanical sweeping unit may include brushes or scrapers actuated by a servo motor to physically remove pest debris. The cleaning mechanism operates at predefined intervals based on pest accumulation rates. Automatic cleaning ensures the collection receptacle (116) remains unobstructed for clear imaging and prevents pest buildup that could interfere with device operation.In an embodiment, the server (200) is configured to receive and store the data transmitted from the communication interface of the at least one pest detection device (100). Further, the server (200) may be implemented as a cloud-based server infrastructure, on-premises server system, or hybrid cloud architecture. The server may (200) include data storage systems with capacity to handle large volumes of image data, sensor data, and metadata from multiple pest detection devices (100). In some examples, the server (200) implements data management protocols including data validation, compression, indexing, and archival to optimize storage efficiency and retrieval performance. In one example, the communication interface may directly connect to the server (200). In another example, the communication interface may connect to the server (200) via at least one gateway.In general, gateway refers to a network node, device, or software solution that enables communication between two or more networks with different transmission protocols. The gatewayfacilitates the flow of data between these networks, serving as an entry and exit point. The gateway may perform protocol conversions, manage network traffic, and incorporate security features.Furthermore, the server (200) is configured to receive data from a plurality of pest detection devices (100) deployed across different geographic locations. For example, each pest detection device (100) is uniquely identified through device identifiers, enabling the server (200) to maintain device-specific data records. The server (200) aggregates the received data to create a comprehensive pest distribution database that maps pest populations across geographic regions. The database schema includes fields for pest species, population density, timestamp, geographic coordinates, environmental conditions, crop type, and other relevant parameters. In some examples, the server (200) may implement data synchronization protocols to handle intermittent connectivity and ensure data integrity when devices operate in areas with poor network coverage.Additionally, the server (200) is configured to interface with one or more external databases (500). For example, the external databases include at least one of scientific pest databases containing taxonomic information and pest biology data, historical pest occurrence databases with records of past infestations and outbreak patterns, geographic information databases with land use and topographic data, meteorological databases with weather forecasts and climate data, or any combination thereof. In some examples, the server (200) may implement application programming interfaces (APIs) to query and retrieve relevant information from external databases (500). This external data enriches the pest monitoring system's analytical capabilities and improves prediction accuracy.In an embodiment, the monitoring device (300) is operatively connected to the server (200) and includes one or more second processor and one or more second memory unit. The one or more second processor may include at least one of central processing units (CPUs), graphics processing units (GPUs), tensor processing units (TPUs), or specialized Al accelerators optimized for machine learning workloads. The one or more second memory unit stores instructions that, when executed by the one or more second processor, cause the monitoring device (300) to perform various analytical and processing functions.Furthermore, the monitoring device (300) is configured to process the captured images to identify pest species and determine pest population parameters. The processing begins with preprocessing the captured images to enhance image quality and extract relevant features. Preprocessing operations include noise reduction, contrast enhancement, color correction, and image segmentation to isolate individual pests. Feature extraction techniques identify morphologicalcharacteristics such as body shape, wing patterns, color distributions, and size parameters that are diagnostic for pest species identification.In an embodiment, the monitoring device (300) applies pattern recognition techniques to identify pest species based on extracted features using trained machine learning models. The machine learning models may include convolutional neural networks (CNNs), deep learning architectures, support vector machines (SVMs), random forests, ensemble methods, and the like. The models are developed with labeled pest image datasets from multiple geographic regions and crop types, ensuring robust performance across diverse conditions. The training datasets include annotated pest images covering various species, life stages, and viewing angles. The models achieve species identification accuracy exceeding predefined thresholds for reliable pest identification.In another embodiment, the monitoring device (300) is configured to quantify pest population density and breeding rate parameters. Population density is calculated based on pest counts extracted from images, accounting for the sampling area covered by each pest detection device (100). The monitoring device (300) applies statistical models to estimate total pest populations within geographic areas based on pest count data from the captured images, deployment density of pest detection devices (100) in the geographic area, pest species identification data, and speciesspecific reproduction rate parameters stored in the one or more second memory unit. For example, breeding rate parameters include generation time, fecundity, survival rates, development rates under different environmental conditions, and the like.Further, the monitoring device (300) is configured to validate the identified pest species by crossreferencing with data from the one or more external databases (500). Validation involves comparing identified morphological features with taxonomic descriptions, verifying geographic distribution ranges, and checking seasonal occurrence patterns. This multi-source validation improves identification confidence and helps detect potentially invasive species or unusual pest occurrences.Furthermore, the monitoring device (300) is configured to correlate the identified pest species and pest population parameters with environmental parameters to generate predictive pest outbreak measures. The correlation analysis identifies relationships between pest activity and factors such as temperature, humidity, rainfall patterns, and crop phenology. Machine learning techniques, including time series analysis and regression models, predict the likelihood and timing of future pest outbreaks. The predictive models account for pest biology, including temperature-dependent development rates, moisture requirements for breeding, and host plant preferences. The predictivepest outbreak measures include probability assessments, expected time to reach economic threshold levels, and geographic spread projections.In an embodiment, the monitoring device (300) generates pest management recommendations based on the identified pest species and pest population parameters. The recommendations are customized based on multiple factors including pest species biology, current population levels, predicted outbreak risks, crop type and growth stage, local environmental conditions, and available control methods. In one example, the pest management recommendations include at least one of pest species identification information with common and scientific names, pest population density metrics including counts per trap and estimated field populations, pest outbreak probability assessments with risk levels and time horizons, pesticide application recommendations including active ingredients and formulations, optimal treatment timing recommendations based on pest life cycles and weather conditions, integrated pest management strategies combining multiple control methods, or any combination thereof.In some embodiments, the pesticide application recommendations specify appropriate pesticides for identified pest species, considering factors such as pest susceptibility, resistance management, crop safety, and environmental impact. The recommendations include quantity specifications calculated based on pest density, treatment area, and application methods. Application timing is optimized to target vulnerable pest life stages while minimizing impacts on beneficial organisms. The recommendations may include organic and biological control options for sustainable pest management.In some embodiments, the monitoring device (300) is operatively connected to one or more e- commerce platforms. The connection enables real-time access to product availability, pricing, and supplier information. The pest management recommendations include product availability information from the e-commerce platforms, allowing users to directly procure recommended pesticides, equipment, or services. The integration streamlines the pest management workflow from detection to treatment implementation.The monitoring device (300) transmits the pest management recommendations to the one or more user devices (400) via at least one of a mobile application interface, a web-based portal interface, automated messaging services, and the like. The mobile application interface provides user- friendly access to recommendations, pest images, and monitoring data on smartphones and tablets. The web-based portal interface offers comprehensive dashboards with detailed analytics, historical trends, and report generation capabilities. For example, automated messaging services delivertime-critical alerts via SMS, email, or push notifications when pest populations exceed threshold levels or outbreak risks increase.In some examples, the user devices (400) represent stakeholders in the agricultural ecosystem, including but not limited to farmers, agricultural extension officers, crop consultants, agricultural input suppliers, research institutions, policy makers, and the like. Each stakeholder group receives customized information relevant to their needs. Farmers receive actionable recommendations for immediate pest control decisions. Extension officers access aggregated data for advisory services. Researchers utilize the comprehensive database for pest ecology studies and model development.Now referring to FIG. 5, a flow chart of a method (2000) for pest detection and monitoring using the pest detection and monitoring system (1000) is illustrated, in accordance with various embodiments of the present subject matter. The method (2000) includes multiple steps that may be performed sequentially, simultaneously, or in various combinations based on operational requirements.The method (2000) includes a step (2000a) of attracting pests using the attractant unit (112). This step involves activating the attractant unit (112) at predefined time intervals using the one or more first processor. The predefined time intervals may be set based on pest activity patterns, such as activating UV light sources during periods when nocturnal pests are most active.The method (2000) includes another step (2000b) of immobilizing pests attracted by the attractant unit (112) using the capture unit (110) disposed around the attractant unit (112). This step involves activating the capture unit (110) simultaneously with or subsequent to activation of the attractant unit (112) using the one or more first processor. The electrical stimulus delivered by the capture unit (110) is calibrated to effectively immobilize target pest species while minimizing energy consumption.The method (2000) includes yet another step (2000c) of receiving immobilized pests in the collection receptacle (116). The immobilized pests fall by gravity into the collection receptacle (116) positioned below the capture unit (110). This passive collection process continues throughout the attraction and capture cycle.The method (2000) includes another step (2000d) of capturing images of pests collected in the collection receptacle (116) using the imaging unit (114). This step involves triggering the imaging unit (114) to capture images at predefined intervals using the one or more first processor. The predefined intervals for image capture are determined based on pest accumulation rates andmonitoring requirements. Multiple images may be captured from different angles or with varying exposure settings to ensure optimal image quality.The method (2000) includes yet another step (2000e) of illuminating the field of view of the imaging unit (114) using the illumination unit (118). This step involves activating the illumination unit (118) based on ambient light conditions using the one or more first processor. Light sensors detect ambient light levels, and the illumination unit (118) is activated when light levels fall below predefined thresholds for clear imaging.The method (2000) includes yet another step (2000f) of transmitting data including the captured images using the communication interface. In some examples, this step may involve compressing image data to optimize bandwidth usage, encrypting data for security, and transmitting via available communication protocols. The data transmission may occur immediately after image capture or be scheduled during periods of optimal network availability.The method (2000) includes yet another step (2000g) of receiving and storing the data transmitted from the communication interface using the server (200). The server (200) validates received data for completeness and integrity, stores data in appropriate database structures, and triggers processing workflows for image analysis.The method (2000) includes yet another step (2000h) of processing the captured images to identify pest species and determine pest population parameters using the monitoring device (300) operatively connected to the server (200). In some examples, this processing may include preprocessing images to enhance quality, extracting morphological features, applying trained machine learning models for species identification, and calculating population metrics.The method (2000) includes yet another step (2000i) of generating pest management recommendations based on the identified pest species and pest population parameters. The recommendations are formulated by analyzing current pest status, predicting future trends, identifying appropriate control measures, and customizing advice based on local conditions and available resources.The method (2000) includes yet another step (2000j) of transmitting the pest management recommendations to one or more user devices (400) to enable pest management decisions. In some examples, the transmission includes formatting recommendations for different delivery channels, prioritizing information based on urgency, and ensuring receipt confirmation for critical alerts.The method (2000) may further include additional steps of generating electrical power using a power generation unit (102). Solar radiation is converted to electrical power during daylight hours,with excess power stored for nighttime operation. The power generation is monitored to ensure adequate energy availability for all device functions.The method (2000) includes storing electrical power and providing power backup using a power storage unit (108) electrically connected to the power generation unit (102). The power storage unit (108) maintains charge levels within predefined thresholds to maximize battery lifespan while ensuring reliable operation.The method (2000) includes controlling operation of the at least one pest detection device (100) using the control unit including one or more first processor and one or more first memory unit. The control unit executes programmed routines, responds to sensor inputs, manages power distribution, and coordinates all device operations.The method (2000) further includes detecting precipitation events using a precipitation sensor (106) and discontinuing power supply to the capture unit (110) upon detection of a precipitation event by the precipitation sensor (106) using the one or more first processor. This safety feature prevents electrical hazards and equipment damage during rainfall.In some embodiments, the method (2000) includes capturing pests using one or more adhesive trap surfaces (122), wherein different colored surfaces target specific pest groups. Further, the method further includes rotating or laterally moving the adhesive trap surfaces (122) into the field of view of the imaging unit (114) at predefined intervals using a movement mechanism including an electric motor (126). This enables comprehensive monitoring of multiple trap surfaces with a single imaging unit (114).In an embodiment, the method (2000) further includes correlating the identified pest species and pest population parameters with environmental parameters to generate predictive pest outbreak measures using the one or more second processor. The environmental parameters are measured using the weather monitoring station (120). The predictive pest outbreak measures are added to the pest management recommendations to provide forward-looking guidance.The method (2000) includes a step of storing the captured images and sensor data in the one or more first memory unit. In some examples, data is organized with metadata including timestamps, geographic coordinates, device identifiers, and environmental conditions for efficient retrieval and analysis.The method (2000) includes another step of transmitting the data via the at least one of cellular network communication protocols, wireless local area network protocols, satellite communication protocols, short-range wireless communication protocols, long-range wireless communicationprotocols, wired communication protocols, or any combination thereof using the communication interface. The communication interface selects optimal protocols based on availability and signal strength.Inan embodiment, the method (2000) includes receiving data from the plurality of pest detection devices (100) deployed across different geographic locations using the server (200), and aggregating the received data to create the comprehensive pest distribution database using the server (200). This aggregation enables landscape-level pest monitoring and regional outbreak detection.The method (2000) includes a step of interfacing with one or more external databases (500) using the server (200). For example, the external databases may include at least one of scientific pest databases, historical pest occurrence databases, geographic information databases, meteorological databases, or any combination thereof. Further, the method includes another step of validating the identified pest species by cross-referencing with data from the one or more external databases (500) using the one or more second processor.In some embodiments, the method (2000) may include preprocessing the captured images to enhance image quality and extract features, applying pattern recognition techniques to identify pest species based on extracted features using trained machine learning models developed with labeled pest image datasets from multiple geographic regions and crop types, and quantifying pest population density and breeding rate parameters.In some embodiments, the method (2000) may include a step of operatively connecting the monitoring device (300) to the one or more e-commerce platforms, including product availability information from the e-commerce platforms in the pest management recommendations, and transmitting the pest management recommendations to the one or more user devices (400) via at least one of the mobile application interface, web-based portal interface, automated messaging services, or any combination thereof.In some embodiments, the method (2000) may include another step of performing self-diagnostic tests on components of the at least one pest detection device (100) upon system initialization using the diagnostic circuit, transmitting diagnostic status information to the server (200), monitoring operating temperature of electronic components using an internal temperature sensor, and maintaining the operating temperature within a predefined range using one or more cooling units.The method (2000) includes automatically removing accumulated pests from the collection receptacle (116) at predefined intervals using the cleaning mechanism. The cleaning mechanismmay include at least one of the compressed air dispensing unit or the mechanical sweeping unit driven by the servo motor.In an embodiment, the method (2000) includes determining the total pest population within a geographic area using the one or more second processor based on pest count data extracted from the captured images, deployment density of the at least one pest detection devices (100) in the geographic area, pest species identification data, and species-specific reproduction rate parameters stored in the one or more second memory unit.The method (2000) includes generating real-time alerts when pest population parameters exceed predefined threshold values stored in the one or more second memory unit using the one or more second processor, enabling bidirectional communication between the monitoring device (300) and the at least one pest detection device (100), and allowing remote configuration and control of operating parameters of the at least one pest detection device (100).It should be understood that the steps of the method (2000) described herein may be performed in different sequences than described, certain steps may be performed multiple times, and additional steps not explicitly described may be included. Furthermore, any of the disclosed steps may be performed multiple times based on operational requirements and monitoring objectives. The method (2000) is flexible and adaptable to various agricultural contexts, pest species, and geographic regions.The pest detection and monitoring system (1000) employs various predefined values, thresholds, and configurable parameters that may be adjusted based on specific deployment requirements, pest species, crop types, and geographic regions. These parameters are provided as non-limiting examples to illustrate the flexibility and adaptability of the system.The term "predefined" as used throughout this disclosure refers to configurable parameters that may be set based on operational requirements. For example, time intervals may be predefined based on pest activity patterns, seasonal variations, or monitoring objectives. Image capture intervals may be predefined based on pest density, monitoring phase, or resource constraints. Cleaning intervals may be predefined based on environmental conditions and pest accumulation rates.The term "threshold" refers to configurable limits that trigger specific system actions. For example, pest population thresholds may be set to trigger alerts at various levels from early warning to critical outbreak stages. Environmental thresholds may trigger changes in monitoring frequency or device protection modes. Light intensity thresholds may determine illumination unit activation.These configurable parameters enable the pest detection and monitoring system (1000) to be optimized for diverse agricultural applications, from small-scale farms to large commercial operations, across different climatic zones and for various crop-pest combinations. The system's adaptive configuration ensures effective pest monitoring while minimizing resource consumption and maximizing operational efficiency.In operation, the pest detection and monitoring system (1000) provides comprehensive automated pest surveillance across agricultural fields. When deployed in an agricultural setting, for example in a crop field or orchard, the at least one pest detection device (100) is mounted via the support structure (104) at strategic locations determined by field characteristics and crop distribution patterns. The power generation unit (102) continuously harvests solar energy during daylight hours, converting solar radiation into electrical power that is stored in the power storage unit (108) to ensure uninterrupted operation throughout day and night cycles. The control unit, utilizing the one or more first processor executing instructions stored in the one or more first memory unit, coordinates the synchronized operation of all device components according to programmed schedules aligned with pest activity patterns.Further, during active monitoring periods, typically corresponding to peak pest activity times such as dusk or dawn, the control unit activates the attractant unit (112) to emit attractant signals, for instance ultraviolet light or pheromone emissions, drawing pests from the surrounding agricultural area toward the pest detection device (100). As attracted pests approach and contact the capture unit (110) disposed around the attractant unit (112), they are immobilized through the electrical stimulus or other capture mechanism, causing them to fall into the collection receptacle (116) positioned below. At predefined intervals, which may be hourly, daily, or based on pest accumulation rates, the control unit triggers the imaging unit (114) to capture high-resolution images of the accumulated pests in the collection receptacle (116), simultaneously activating the illumination unit (118) to ensure optimal lighting conditions for clear image capture regardless of ambient light levels.Furthermore, the captured images along with associated metadata, including timestamp, geographic coordinates, and environmental parameters measured by the weather monitoring station (120), are transmitted via the communication interface to the server (200) using available network protocols. The server (200) aggregates this data stream from multiple pest detection devices (100) deployed across the agricultural landscape, creating a comprehensive real-time pest surveillance network. The monitoring device (300), through its one or more second processor executing analytical algorithms stored in the one or more second memory unit, processes thereceived images to identify specific pest species using trained machine learning models, quantifies pest population parameters including density and distribution patterns, and correlates this information with environmental and historical data to generate predictive outbreak assessments. Based on this multi-factorial analysis, the monitoring device (300) formulates customized pest management recommendations, which may include, but not limited to, specific intervention strategies, optimal treatment timing, resource requirements, neighboring field alert notifications for coordinated area-wide pest control, trap crop placement recommendations to divert pests from main crops, regional pest migration alerts warning of incoming pest populations from neighboring areas, weather-based spray windows, quarantine zone establishment for invasive species detection, and the like, and transmits these actionable insights to the one or more user devices (400) of farmers, agricultural consultants, or farm managers, enabling them to make timely, data-driven pest management decisions that protect crop yields while optimizing resource utilization.

[0001] The pest detection and monitoring system (1000) of the present disclosure provides the following non-limiting technical advantages:• The system operates without human intervention for pest detection, image capture, and data transmission, reducing labor requirements and ensuring consistent monitoring coverage.• Solar-powered operation with battery backup eliminates dependency on external power infrastructure, enabling deployment in remote agricultural areas.• Single imaging unit with motor-driven movement mechanism for multiple trap surfaces reduces hardware complexity and system costs while maintaining comprehensive monitoring.• Automatic cleaning mechanism maintains device functionality without user intervention, ensuring consistent performance and reduced maintenance requirements.• Support for various communication protocols ensures reliable data transmission across diverse geographic regions without requiring specific network infrastructure.• Internal temperature monitoring with active cooling and precipitation detection ensures reliable operation in extreme weather conditions.• AI / ML-powered analysis correlates pest populations with environmental conditions to provide early outbreak warnings, enabling proactive pest management.Direct connection to e-commerce platforms and delivery of customized recommendations through multiple interfaces creates a complete pest management solution from detection to treatment.In light of the above mentioned advantages and the technical advancements provided by the disclosed method and / or the pest detection and monitoring system (1000), the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the pest detection and monitoring system (1000) itself as the claimed steps and the constructional features of the pest detection and monitoring system (1000) provide a technical solution to a technical problem.It should be understood that embodiments described herein should be considered in a descriptive sense only and not for purposes of limitation. Descriptions of features or aspects within each embodiment should typically be considered as available for other similar features or aspects in other embodiments. While one or more embodiments have been described with reference to the figures, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the scope of the present disclosure as defined by the following claims, and equivalents thereof.

Claims

We Claim:

1. A pest detection and monitoring system (1000), comprising: at least one pest detection device (100) comprises: a support structure (104) configured to mount components of the at least one pest detection device (100), an attractant unit (112) configured to attract pests, a capture unit (110) disposed around the attractant unit (112), wherein the capture unit (110) is configured to immobilize pests attracted by the attractant unit (112), an imaging unit (114) positioned below the capture unit (110); a collection receptacle (116) configured to receive immobilized pests, wherein the collection receptacle (116) is positioned below the imaging unit (114) to enable the imaging unit (114) to capture images of pests collected in the collection receptacle (116) an illumination unit (118) positioned proximal to the imaging unit (114) and configured to illuminate a field of view of the imaging unit (114), a power generation unit (102) configured to generate electrical power, a power storage unit (108) electrically connected to the power generation unit (102), wherein the power storage unit (108) is configured to store electrical power and provide power backup, a control unit comprises one or more first processor and one or more first memory unit, wherein the one or more first processor is configured to control operation of the at least one pest detection device (100), and a communication interface operatively connected to the control unit and configured to transmit data including the captured images; a server (200) configured to receive and store the data transmitted from the communication interface; and a monitoring device (300) operatively connected to the server (200), wherein the monitoring device (300) comprises:one or more second processor and one or more second memory unit storing instructions that, when executed by the one or more second processor, cause the monitoring device (300) to: process the captured images to identify pest species and determine pest population parameters, and generate pest management recommendations based on the identified pest species and pest population parameters, wherein the monitoring device (300) transmits the pest management recommendations to one or more user devices (400) to enable pest management decisions.

2. The pest detection and monitoring system (1000) as claimed in claim 1, wherein the capture unit (110) comprises an electrified mesh structure configured to deliver an electrical stimulus to immobilize pests, and wherein the attractant unit (112) comprises at least one of an ultraviolet light source (112), a pheromone dispensing unit (124), or a combination thereof.

3. The pest detection and monitoring system (1000) as claimed in claim 1, wherein the at least one pest detection device (100) comprises: a precipitation sensor (106) configured to detect precipitation events, and wherein the one or more first processor is configured to discontinue power supply to the capture unit (110) upon detection of a precipitation event by the precipitation sensor (106).

4. The pest detection and monitoring system (1000) as claimed in claim 1, wherein the at least one pest detection device (100) comprises: one or more adhesive trap surfaces (122) configured to capture pests, wherein the one or more adhesive trap surfaces (122) comprises at least one of a first colored adhesive surface configured to attract a first group of pest species or a second colored adhesive surface configured to attract a second group of pest species; and a movement mechanism comprises an electric motor (126) configured to rotate or laterally move the one or more adhesive trap surfaces (122) into the field of view of the imaging unit (114) at predefined intervals.

5. The pest detection and monitoring system (1000) as claimed in claim 1, wherein the imaging unit (114) is positioned at a lower end of the attractant unit (112), and wherein theillumination unit (118) is arranged in a geometric pattern around a periphery of the imaging unit (114).

6. The pest detection and monitoring system (1000) as claimed in claim 1, wherein the power storage unit (108) comprises: a removable battery module; and a charging interface configured to receive electrical power from an external power source, and wherein the power generation unit (102) comprises a photovoltaic panel configured to convert solar radiation into electrical power and a charge controller configured to regulate charging of the power storage unit (108).

7. The pest detection and monitoring system (1000) as claimed in claim 1, wherein the at least one pest detection device (100) comprises: a weather monitoring station (120) configured to measure environmental parameters comprise at least one of temperature, humidity, rainfall, wind speed, wind direction, atmospheric pressure, or any combination thereof.

8. The pest detection and monitoring system (1000) as claimed in claim 7, wherein the one or more second processor is configured to correlate the identified pest species and pest population parameters with the environmental parameters to generate predictive pest outbreak measures and adds it to the pest management recommendations.

9. The pest detection and monitoring system (1000) as claimed in claim 1, wherein: the one or more first memory unit is configured to store the captured images and sensor data; and the one or more first processor is configured to: activate the attractant unit (112) at predefined time intervals, activate the capture unit (110) simultaneously with or subsequent to activation of the attractant unit (112), trigger the imaging unit (114) to capture images at predefined intervals, and activate the illumination unit (118) based on ambient light conditions.

10. The pest detection and monitoring system (1000) as claimed in claim 1, wherein:the communication interface is configured to: transmit the data via at least one of cellular network communication protocols, wireless local area network protocols, satellite communication protocols, short- range wireless communication protocols, long-range wireless communication protocols, wired communication protocols, or any combination thereof; and the server (200) is configured to: receive data from a plurality of pest detection devices (100) deployed across different geographic locations, aggregate the received data to create a comprehensive pest distribution database, and interface with one or more external databases (500), wherein the external databases comprise at least one of scientific pest databases, historical pest occurrence databases, geographic information databases, meteorological databases, or any combination thereof.

11. The pest detection and monitoring system (1000) as claimed in claim 10, wherein the one or more second processor is configured to validate the identified pest species by crossreferencing with data from the one or more external databases (500).

12. The pest detection and monitoring system (1000) as claimed in claim 1, wherein the instructions stored in the one or more second memory unit, when executed by the one or more second processor, further cause the monitoring device (300) to: preprocess the captured images to enhance image quality and extract features; apply pattern recognition techniques to identify pest species based on extracted features using trained machine learning models developed with labeled pest image datasets from multiple geographic regions and crop types; and quantify pest population density and breeding rate parameters.

13. The pest detection and monitoring system (1000) as claimed in claim 1, wherein the pest management recommendations generated by the one or more second processor comprise at least one of pest species identification information, pest population density metrics, pest outbreak probability assessments, pesticide application recommendations and quantity specifications, optimal treatment timing recommendations, integrated pest management strategies, or any combination thereof.

14. The pest detection and monitoring system (1000) as claimed in claim 13, wherein: the monitoring device (300) is operatively connected to one or more e-commerce platforms; the pest management recommendations include product availability information from the e-commerce platforms; and the monitoring device (300) transmits the pest management recommendations to the one or more user devices (400) via at least one of a mobile application interface, a web-based portal interface, automated messaging services, or any combination thereof.

15. The pest detection and monitoring system (1000) as claimed in claim 1, wherein the at least one pest detection device (100) comprises: a diagnostic circuit configured to perform self-diagnostic tests on components of the at least one pest detection device (100) upon system initialization and transmit diagnostic status information to the server (200); an internal temperature sensor configured to monitor operating temperature of electronic components; and one or more cooling units configured to maintain the operating temperature within a predefined range.

16. The pest detection and monitoring system (1000) as claimed in claim 1, wherein the one or more second processor is configured to determine a total pest population within a geographic area based on: pest count data extracted from the captured images; deployment density of at least one pest detection devices (100) in the geographic area; pest species identification data; and species-specific reproduction rate parameters stored in the one or more second memory unit.

17. The pest detection and monitoring system (1000) as claimed in claim 1, wherein: the one or more second processor is configured to generate real-time alerts when pest population parameters exceed predefined threshold values stored in the one or more second memory unit; andthe pest detection and monitoring system (1000) is configured to enable bidirectional communication between the monitoring device (300) and the at least one pest detection device (100), allowing remote configuration and control of operating parameters of the at least one pest detection device (100).

18. A method (2000) for pest detection and monitoring using a pest detection and monitoring system (1000), the method (2000) comprising: attracting pests using an attractant unit (112); immobilizing pests attracted by the attractant unit (112) using a capture unit (110) disposed around the attractant unit (112); receiving immobilized pests in a collection receptacle (116); capturing images of pests collected in the collection receptacle (116) using an imaging unit (114); illuminating a field of view of the imaging unit (114) using an illumination unit (118); transmitting data including the captured images using a communication interface; receiving and storing the data transmitted from the communication interface using a server (200); processing the captured images to identify pest species and determine pest population parameters using a monitoring device (300) operatively connected to the server (200), wherein the monitoring device (300) comprises one or more second processor and one or more second memory unit storing instructions that, when executed by the one or more second processor, cause the monitoring device (300) to perform the processing; generating pest management recommendations based on the identified pest species and pest population parameters; and transmitting the pest management recommendations to one or more user devices (400) to enable pest management decisions.

19. The method (2000) as claimed in claim 18, comprises: generating electrical power using a power generation unit (102); storing electrical power and providing power backup using a power storage unit (108) electrically connected to the power generation unit (102); andcontrolling operation of the at least one pest detection device (100) using a control unit comprises one or more first processor and one or more first memory unit.

20. The method (2000) as claimed in claim 18, comprises: detecting precipitation events using a precipitation sensor (106); and discontinuing power supply to the capture unit (110) upon detection of a precipitation event by the precipitation sensor (106) using the one or more first processor.

21. The method (2000) as claimed in claim 18, comprises: capturing pests using one or more adhesive trap surfaces (122), wherein the one or more adhesive trap surfaces (122) comprises at least one of a first colored adhesive surface configured to attract a first group of pest species or a second colored adhesive surface configured to attract a second group of pest species; and rotating or laterally moving the one or more adhesive trap surfaces (122) into the field of view of the imaging unit (114) at predefined intervals using a movement mechanism, and wherein the movement mechanism comprises an electric motor (126).

22. The method (2000) as claimed in claim 18, comprises: correlating the identified pest species and pest population parameters with environmental parameters to generate predictive pest outbreak measures using the one or more second processor, and wherein the environmental parameters are measured using a weather monitoring station (120); and adding the predictive pest outbreak measures to the pest management recommendations.

23. The method (2000) as claimed in claim 19, comprises: storing the captured images and sensor data in the one or more first memory unit; activating the attractant unit (112) at predefined time intervals using the one or more first processor; activating the capture unit (110) simultaneously with or subsequent to activation of the attractant unit (112) using the one or more first processor; triggering the imaging unit (114) to capture images at predefined intervals using the one or more first processor; andactivating the illumination unit (118) based on ambient light conditions using the one or more first processor.

24. The method (2000) as claimed in claim 18, comprises: transmitting the data via at least one of cellular network communication protocols, wireless local area network protocols, satellite communication protocols, short-range wireless communication protocols, long-range wireless communication protocols, wired communication protocols, or any combination thereof using the communication interface; receiving data from a plurality of pest detection devices (100) deployed across different geographic locations using the server (200); aggregating the received data to create a comprehensive pest distribution database using the server (200); interfacing with one or more external databases (500) using the server (200), wherein the external databases comprise at least one of scientific pest databases, historical pest occurrence databases, geographic information databases, meteorological databases, or any combination thereof; and validating the identified pest species by cross-referencing with data from the one or more external databases (500) using the one or more second processor.

25. The method (2000) as claimed in claim 18, comprises: preprocessing the captured images to enhance image quality and extract features; applying pattern recognition techniques to identify pest species based on extracted features using trained machine learning models developed with labeled pest image datasets from multiple geographic regions and crop types; and quantifying pest population density and breeding rate parameters.

26. The method (2000) as claimed in claim 18, comprises: operatively connecting the monitoring device (300) to one or more e-commerce platforms; including product availability information from the e-commerce platforms in the pest management recommendations; andtransmitting the pest management recommendations to the one or more user devices (400) via at least one of a mobile application interface, a web-based portal interface, automated messaging services, or any combination thereof.

27. The method (2000) as claimed in claim 18, comprises: performing self-diagnostic tests on components of the at least one pest detection device (100) upon system initialization using a diagnostic circuit; and transmitting diagnostic status information to the server (200); monitoring operating temperature of electronic components using an internal temperature sensor; and maintaining the operating temperature within a predefined range using one or more cooling units.

28. The method (2000) as claimed in claim 18, comprises: determining a total pest population within a geographic area using the one or more second processor based on: pest count data extracted from the captured images; deployment density of at least one pest detection devices (100) in the geographic area; pest species identification data; and species-specific reproduction rate parameters stored in the one or more second memory unit.

29. The method (2000) as claimed in claim 18, comprises: generating real-time alerts when pest population parameters exceed predefined threshold values stored in the one or more second memory unit using the one or more second processor; enabling bidirectional communication between the monitoring device (300) and the at least one pest detection device (100); and allowing remote configuration and control of operating parameters of the at least one pest detection device (100).

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