IOT-based, large language model-integrated smart irrigation and phytosanitary management system

Abstract

Present invention relates to an innovative system wherein loT sensors, image processing devices, large language models (LLM) and a central device operates in an integrated manner to optimize agricultural irrigation processes and monitor plant health. System aggregates the data coming from sensors measuring environmental conditions and image processing devices analyzing the plant images, and analyses them via large language models, and finally formulates customized irrigation strategies. Thanks to its adaptive design, it adapts in real-time to changes in sensor data and plant conditions and optimizes the irrigation amount and timing. In addition, it enables users to access the system over mobile application and therefore offers an effective solution in terms of sustainability and efficiency in agricultural production.

Description

[0001] IOT-BASED, LARGE LANGUAGE MODEL-INTEGRATED SMART IRRIGATION AND PHYTOSANITARY MANAGEMENT SYSTEM

[0002] Technical Field

[0003] Present Invention relates to a system which comprise an integration of Internet of Things (loT)-based controller device, large language models (LLM), image processing devices and sensors in order to optimize irrigation strategies in agriculture and monitor the plant health. Present Invention is designed to increase agricultural yield, facilitate efficient use of water resources and support plant development, in particular. This system which integrates Artificial Intelligence (Al), loT technologies and image processing methods is used in the field of agricultural technology and smart agricultural applications.

[0004] State of Art

[0005] Use of technology in agriculture is in constant evolution in order to meet needs of humanity in increasingly sustainable and efficient way. However, technological innovations in this field cannot always find an ultimate solution to existing problems. Conventional irrigation methods causes substantial losses in terms of effective use of the water resources as they are not fully compatible with environmental data. Although modern technologies have provided solution to these problems to some degree via loT-based sensor systems, artificial intelligence algorithms and image processing methods, these technologies are subject to limitations individually.

[0006] It is common to use loT technology for irrigation methods in agricultural fields. These systems have sensors which measure environmental factors such as temperature, humidity, light amount and soil moisture. Data received from sensors are generally aggregated in a central system which then sends signals to irrigation systems. For instance, irrigation is automatically triggered once soil moisture drops below certain threshold. However, these systems are exposed to some basic limitations:

[0007] • Static Rules: loT sensor data are generally processed based on the preset threshold values. This hinders responses to dynamic changes in the environmental conditions and to individual needs of plants.

[0008] • Applications Customized to Plant Species: Existing systems have no capability to formulate irrigation strategies customized to plant species. For instance, water needs vary depending on the plant species, however, existing systems does not sufficiently consider these differences.

[0009] • Lack of Feedback: loT-based systems does not provide feedback on the plant health or growth status. Irrigation is optimized solely based on environmental values.

[0010] There has recently been significant advances in imaging processing technologies in terms of plant health monitoring and early diagnosis of diseases. These systems are able to identify plant stress level by analyzing leaf, fruit or stem condition of the plants. For instance, conditions such as water stress or nutritional deficiency can be detected using hyper spectral imaging technology. However, the findings from image processing in these systems are generally not integrated directly with irrigation systems. Therefore, plant health is generally assessed using manual methods.

[0011] Artificial Intelligence and machine learning algorithms has enabled more robust data analysis in agriculture. Particularly, deep learning models are used in phytosanitary, yield estimates and irrigation management. However, Al systems are currently exposed to following limitations:

[0012] • Data Collection and Preparation: A large amount of correctly-labeled data are necessary for Al algorithms to function effectively. This is usually timeconsuming and costly.

[0013] • Lack of Customization: Al algorithms are generally optimized for general agricultural applications, and provide limited customization to a specific farm or plant species. • Lack of Interpretation Power: Existing Al systems generally analyzes the data, however have limited capability of interpreting results in a user-friendly way and providing suggestions.

[0014] Large language models provide effective results in text-based data analytics and decision-making processes. However, these models are not yet commonly used in agricultural production processes. LLMs have the capability of converting into text, analyzing and interpreting the data relating to environment and plant health. If used in the agricultural applications, these models have following advantages:

[0015] • Multidimensional Data Analytics: Capable of analyzing combined data from loT sensors, image processing devices and user inputs.

[0016] • User-Friendly Outputs: Capable of converting analysis results into text and providing farmers with suggestions.

[0017] • Real-Time Decision Making: Capable of processing data dynamically and creating daily irrigation strategies.

[0018] However, there are limited studies on the integration of LLMs to the agriculture. Existing technologies are focused only on one data source and therefore cannot offer an integrated solution.

[0019] A People's Republic of China patent document, nr. CN202311818688A, describes a general smart farmer guidance method and system based on a large language model. This system uses LLM technology to solve problems of farmers. In this embodiment of the art, using this system for the data on how much water should be fed to the plant is not only a solution for automated irrigation but also requires all data relating to environment to be collected and introduced to the system. And this does not offer a feasible solution for agricultural irrigation management.

[0020] Present invention offers a comprehensive solution to aforementioned inefficiencies, and aims to provide a novel solution in agricultural technologies. By integrating both loT and artificial intelligence technologies, it aims to optimize phytosanitary and irrigation processes to increase productivity in agricultural production. As a result, a development in relevant technical field is required due to disadvantages described above, and underperformance of the existing solutions.

[0021] Aim of Invention

[0022] Present invention aims to provide an innovative system which meets the needs described above, eliminates the disadvantages of the existing methods used in agricultural irrigation, and has advanced technological functionalities.

[0023] The main aim of the present invention is to create a system which formulates dynamic and optimized irrigation strategies using large language models (LLM) by integrating environmental data and phytosanitary data collected from loT sensors. The system also factors in plant morphological characteristics to support sustainable irrigation methods and improves efficiency with real-time updates.

[0024] Present invention comprises two basic components. First component is a central system which collects environmental data and manages irrigation strategies. Second component is an image processing system which collects plant images and sends plant health and water stress data to the central device. Central system analyzes the data coming from both loT sensors and image processing device and controls the irrigation system.

[0025] Image processing system comprises an image collection devices consisting of camera and microprocessor to record physical properties of the plants from different angles, and an image processing device where all images are stored. This device processes the images it collects on a cloud-based or local system, and derives plant health and water stress data. These data are transmitted to central system and integrated to the irrigation decisions.

[0026] Central system aggregates environmental data received from loT sensors (temperature, humidity, light amount, precipitation) and plant data received from image processing device, and converts them to text that can be interpreted by large language model (LLM). This text also contains user inputs such as plant species, planting time and location. LLM analyzes these data and creates the most suitable irrigation strategy which is then applied by central device. System can adapt to daily changes in plant and environmental conditions and continuously updates irrigation strategies. Thus, it can determine the most suitable water amount and irrigation times for each plant. In addition, system ensures instant adaptation to changes in plant health and environmental conditions thanks to its dynamism, increasing the agricultural yield.

[0027] Present invention effectively estimates the water need of the plant thanks to integration of basic components described above and contributes to agricultural productions thanks to optimized irrigation strategies. This system facilitates more effective use of the water resources and protection of the plant health, offering an innovative solution to the agriculture.

[0028] Detailed Description of Figures

[0029] Figure 1. A schematic representation of the inventive system

[0030] Description of References:

[0031] 1. Central device

[0032] 2. Sensor

[0033] 3. Irrigation system

[0034] 4. Large language model

[0035] 5. Image processing device

[0036] 6. Cloud Layer

[0037] 7. Camera system

[0038] K. End user

[0039] Detailed Description of Invention

[0040] This invention is designed to optimize agricultural irrigation processes, monitor plant health and offer a sustainable agricultural management system. System comprises integration of various devices and software layers. Structural components and functioning of the invention is described in detail below.

[0041] Inventive system and elements used in the method, and its functions are as follows:

[0042] Central device (1) is the main control and processing unit of the system, and collects data coming from end user (K), loT sensors (2) and image processing device (5). This device determines irrigation strategies by integrating environmental conditions (temperature, humidity, light intensity, precipitation) and plants' morphological data. Central device also includes in the process the data such as plant species, planting time and location input by user. It processes data necessary for large language model (4) and makes them interpretable for text-based analytics. Central device (1) is in constant communication with irrigation system (3) and transmits irrigation signals to it.

[0043] Central device (1) is a printed circuit board which contains a microprocessor and other electronic components which enable this processor run. It has a built-in wireless communication unit as it can communicate with other devices wirelessly or through a wire. It operates according to algorithm developed for the system.

[0044] Sensors (2) continuously monitors environmental conditions and feeds data to central device (1). System is equipped with a number of sensors (2) which measure temperature, humidity, light intensity and precipitation. Data collected via these sensors enables irrigation system (3) adapt dynamically to the environmental changes.

[0045] Irrigation system (3) supplies water to the plants according to the commands transmitted by central device (1). This system contains electronic boards and mechanical systems. This system operates according to irrigation method to be applied by end user. Electronic system is capable of opening / closing water valves which, latter, can be commercially available types. Mechanical system comprises components of irrigation system applied by end users on their agricultural fields. System uses a large language model (4) that is similar to ChatGPT and analyzes data fed by sensors and image processing device as well as user inputs and formulates the most suitable irrigation strategy. Large language model (4) processes text-based data created by central device (1) and offers the most efficient irrigation strategy. In this way, irrigation strategies can be customized to each plant species and environmental conditions.

[0046] Image processing device (5) comprises one or more camera system (7) which collect images of the plant from different angles. This device analyzes morphological properties of the plant and transmits data regarding plant health and water stress to central device (1). Image processing device (5) extracts data regarding health and water stress from the images it has received. In extracting these data, it can get help from cloud system (6).

[0047] Image processing device (5) is a printed circuit board which contains a microprocessor and other electronic components which enable this processor run. It has a built-in wireless communication unit as it can communicate with other devices wirelessly or through a wire. It operates according to algorithm developed for the system.

[0048] Cloud Layer (6) executes the tasks that require high processing power among image processing tasks of image processing device (5). This layer is controlled by image processing device and used whenever it is needed.

[0049] Camera system (7) is a component used to collect the images of a plant from different angles. This camera provides the images necessary to obtain morphological data of the plant which is then transmitted to image processing device (5). System uses multiple cameras to support more accurate analytics.

[0050] End user (K) controls the system via a mobile application. User may input in the central device (1) the data such as plant species, planting time and location. Via mobile application, user can also monitor irrigation systems and access the data regarding the plant health. Thanks to this new invention which resulted from the entire process described above, we developed a new decision-making mechanism for irrigation systems, which is further optimized with actual plant data derived from morphological properties of the plants using image processing method. This provides a more productive, adaptive and environment-friendly solution for sustainable agricultural practices and automated irrigation systems.

[0051] Industrial Embodiment of Invention

[0052] This invention has potential for a wide range of application to develop the automated and optimized irrigation systems in the agriculture. The system provided by the invention can be used in both outdoor agriculture and indoor agriculture. As it optimizes the irrigation processes based on plant species, environmental conditions and plant health, it offers economic benefits to agricultural producers, such as water and energy savings.

[0053] As a result, present invention provides a feasible solution to both small scale farms as well as large agricultural facilities as it offers advantages to the agriculture sector, such as sustainability, efficiency and ease of use. As this system can easily adapt to the existing agricultural infrastructures and offer a user-friendly structure, it is easier to integrate it to the industry.

Claims

CLAIMS1. A smart irrigation and phytosanitary management system, comprising;— at least one sensor (2) which measure environmental conditions,— an image processing device (5) which collects plant images and analyzes morphological properties of the plant from these images and determine plant health and water stress,— a central device (1) which convert data from user (K) inputs, sensors (2) and image processing device (5) to text for text-based analysis and issues commands to irrigation system (3),— a large language model (4) to determine irrigation strategies on the basis of sensor (2) data, user (K) inputs, and image processing results,— an irrigation system (3) which supplies water to plants in accordance with the determined irrigation strategies,— a cloud layer (6), controlled by image processing device (5), wherein analysis requiring high processing power is executed,— a mobile application interface which enable users (K) to integrate plant species, planting time and location data to the system and monitor irrigation strategies.

2. The smart irrigation and phytosanitary management system according to Claim 1 , characterized by a structure to support environmental sustainability which• optimizes irrigation strategies in real time by instantly adapting to changes in sensor (2) data, user (K) inputs and image processing (5) results,• determines water need of the plant accurately by analyzing data related to plant health and water stress,• comprise at least one camera (7).

3. An agricultural irrigation management method which comprise the steps of• collection of data such as temperature, humidity, light amount and precipitation via at least one sensor (2) which measure environmental conditions,• determination of morphological properties of plant and water stress for plant health via an image processing device (5) which collects plant images,• aggregation and conversion of the user inputs (K) and data sent by sensor data (2) and image processing device (5) into a text that can be interpreted by large language model at the central device (1),• analysis of said text by large language model and formulation of the irrigation strategies based on the plant species, environmental conditions and user inputs,• delivery of the commands to irrigation system (3) in line with strategies formulated by central device (1),• real-time adaptively updating of the irrigation processes,• continuous monitoring of the plant health and water stress conditions via image processing device (5) and cloud layer (6).• and users' access to the system via mobile application and visualization of the plant water need, irrigation strategies, and environmental data.

Images