62 results about "Crop loss" patented technology

The invention relates to a multi-modal fusion corn harvester header height adaptive adjustment method and system, and belongs to the technical field of automation control, the method comprises the following steps: using a binocular camera to collect crop images, and preprocessing the crop images to obtain a depth map; the image is input into a YOLOV8n model, and coordinates of a center point at the bottom of a bounding box of the corncob are obtained through model operation; performing plane fitting on the depth map to obtain a ground height; calculating a corn ear height initial value based on the ground height and the center point coordinate; vibration data of working of the harvester is collected to perform vibration compensation on the initial height value to obtain a height correction value; and dynamically adjusting the header height of the harvester by adopting a dynamic fuzzy PID algorithm based on the correction value. According to the method, the target detection model and the dynamic fuzzy PID algorithm are combined, so that the problems of low visual detection precision and control response lag of existing agricultural machinery in a complex field scene are solved, and crop loss in harvesting operation is effectively reduced.

The invention discloses a ground-air integrated agricultural detection system and method, and relates to the technical field of agricultural detection. Farmland multi-dimensional data are collected through the ground-air sensor, and the farmland environment is comprehensively monitored. Based on the soil moisture dynamic change and the crop root activity data, obtaining a root moisture utilization efficiency index; identifying crop disease symptoms and pest population distribution, predicting a pest diffusion trend, and obtaining a pest propagation risk index; the soil erosion risk index is obtained by analyzing the soil particle loss, the surface runoff intensity and the vegetation coverage change. The early warning module comprehensively evaluates the root system moisture utilization efficiency index, the disease and pest propagation risk index and the soil erosion risk index to obtain a farmland detection index, an early warning mechanism is set, and early warning information is generated when the farmland detection index triggers an early warning condition. Farmland conditions can be monitored in real time, a scientific management basis is provided, the intelligent level of agricultural management is improved, and resource waste and crop loss are reduced.

A crop loss monitoring method includes detecting crop loss in a measurement area when the measurement area corresponds to an unharvested area of a field and generating pre-harvest crop loss sensor data based thereon. The method further includes detecting crop loss in the measurement area when the measurement area is behind a distal end of a header of a harvester and generating harvesting crop loss sensor data based thereon. The method further includes identifying a pre-harvest crop loss value based on the pre-harvest crop loss sensor data, identifying a harvesting crop loss value based on the harvesting crop loss sensor data and the pre-harvest crop loss value, and generating one or more control signals based on at least one of the pre-harvest crop loss value or the harvesting crop loss value.

A crop loss monitoring method includes detecting crop loss in a measurement area, the measurement area corresponding to an unharvested area of a field. The method further includes identifying a pre-harvest crop loss value based on the crop loss detected in the measurement area. The method further includes generating one or more control signals based on the pre-harvesting crop loss value.

The invention relates to the technical field of farmland pest real-time detection, in particular to a farmland pest real-time detection device, method and system based on an unmanned aerial vehicle, and the device comprises the unmanned aerial vehicle, a flight control system, a remote controller, a camera, a camera remote controller, an airborne computer, a 4G communication module and a ground workstation. A detection result is transmitted to a ground work station in real time through the 4G communication module, so that the real-time performance of data is ensured, the lagging of information processing during pest outbreak is reduced, and pest diffusion caused by information delay is avoided; according to the method, pests are automatically identified based on an improved YOLOv10 network model, a weight file is deployed on an airborne computer of an unmanned aerial vehicle and real-time detection is carried out, images can be directly processed in the flight process, a detection result can be rapidly obtained, information lag is avoided, it is ensured that prevention and control measures can be taken in time when pests burst out, and the control efficiency is improved. Therefore, crop loss is reduced.

The invention discloses a crop rainstorm disaster quantitative risk assessment method based on a CNN-LSTM hybrid neural network, and the method comprises the steps: obtaining multi-source data, carrying out the standardization processing, and carrying out the time-space superposition to construct a three-dimensional feature matrix; based on a CNN-LSTM hybrid neural network model, respectively extracting a spatial feature vector and a time sequence feature vector of the disaster situation data, completing fusion to obtain a fused feature vector, and outputting a risk probability and loss intensity; and continuing to comprehensively consider the crop loss caused by the risk probability and the loss intensity, completing the construction of a rainstorm disaster-crop coupling loss function, and finally generating the crop rainstorm disaster risk probability, the main crop loss intensity and the rainstorm disaster-crop coupling total loss based on the intelligent grid rainfall and wind speed forecast data. According to the method, the quantitative loss of crops is estimated by integrating rainstorm disaster-crop coupling dynamic response, and effective transformation from static disaster assessment to dynamic multi-source fusion response assessment is realized.

The present disclosure includes devices methods and systems for detecting lost crop in a work machine operation. The method may include receiving ground speed data from one or more speed detection systems. The method may include receiving crop condition data from one or more feed data sensors. The method may include determining one or more crop condition states based at least in part on one or more of the ground speed data and the crop condition data. The method may include determining lost crop based at least in part on the one or more crop condition states. The method may include adjusting one or more combine operation parameters based at least in part on the determination of lost crop.

The invention discloses a facility agriculture voice interaction system based on a large model, and relates to the technical field of voice interaction. Comprising a voice signal acquisition and enhancement module, a voice recognition and semantic standardization processing module, a feature extraction and emergency degree quantification module, an intelligent evaluation and emergency instruction judgment module and a priority scheduling and response execution module, voice signals are continuously monitored through a multi-microphone array of the wearable terminal, the direction of a voice source is enhanced by using a far-field beam forming technology, the signals are processed through a time domain / frequency domain noise reduction algorithm, and finally voice streams are cached in real time with millisecond-level sampling precision. Through the multi-microphone array and far-field beam forming technology, voice capture is optimized, noise is reduced, real-time voice recognition and semantic analysis of the edge server are combined, the priority of emergency instructions is quantified, it is ensured that resources are rapidly scheduled to process emergency tasks, the agricultural production efficiency is improved, and crop losses are reduced.

The invention relates to the technical field of agriculture, and discloses a rice and wheat combine harvester cleaning device intelligent regulation and control method based on rice and wheat attributes, and the method comprises the steps: standardizing a field operation environment, a cleaning loss rate, an impurity rate and operation parameters, and building a historical data case library; according to rice and wheat varieties and attributes input by a user, sorting operation parameters in historical cases are matched through similarity retrieval; automatically adjusting the angle of an air distribution plate, the rotating speed of a fan and the opening degree of an upper screen by taking the matching parameters as initial settings; monitoring the operation data in real time, if the impurity rate is abnormal, adjusting the parameters to a normal range, and storing the final parameters and the operation data into a case library for subsequent retrieval; according to the intelligent regulation and control method for the cleaning device of the rice and wheat combine harvester based on the rice and wheat attributes, it can be guaranteed that the cleaning loss rate and the cleaning impurity rate are kept within the normal range at the same time, the operation efficiency of the combine harvester is improved, and crop losses are reduced.

The invention discloses an economic evaluation system for regional economy, which relates to the technical field of regional economic evaluation and comprises a data acquisition module, an image preprocessing module, a feature extraction and model construction module, a risk evaluation module, a shooting frequency regulation and control module and an efficient identification module. Farmland areas are divided into high-risk, low-risk and non-risk states according to the disease and pest risk levels, and the accuracy and effectiveness of prevention and control are improved. High-risk areas are prevented and controlled in a centralized mode, low-risk areas are monitored regularly, non-risk areas are managed conventionally, crop loss and production cost are effectively reduced, and the environment is protected. Based on the shooting frequency of intelligent regulation and control, the monitoring frequency of the high-resolution camera is dynamically adjusted, the resource utilization is optimized, the accuracy of disease and pest prevention and control is improved, the operation cost is reduced, the sustainable development of agricultural production is ensured, and an efficient, economical and environment-friendly management scheme is provided.

A harvester header height keeping structure comprises a header serving as a harvesting part of a harvester, a height adjusting device used for adjusting the height of the header above the ground, a control module used for driving the height adjusting device to adjust the height of the header above the ground in real time according to feedback information, and a horizontal measuring device used for monitoring the inclination degree of the harvester relative to the ground. The angle sensor is used for monitoring the inclination degree of the header relative to the harvester, and the control structure is used for inputting and modifying the preset header height. The height of the header is intelligently and automatically adjusted, so that the harvesting efficiency is improved, and the crop loss is reduced.

The present invention discloses a forward deviation correction control method for ridge and ditch operation of a vegetable transplanter, which relates to the technical field of deviation correction evaluation, and comprises the following steps: using a GPS system, a visual sensor and a laser radar to obtain transplanter status information and operating environment information, wherein the transplanter status information includes the current position, speed and steering angle of the transplanter, and the environmental information includes the surrounding soil conditions and ridge and ditch shapes. The present invention identifies the potential impact of different soil conditions and ridge and ditch shapes on the operation of the transplanter through a comprehensive analysis of soil moisture, color, roughness, and the depth, width and shape of the ridge and ditch, and dynamically adjusts the deviation correction strategy, so that the transplanter can operate stably in different farmland environments, whether in flat cultivated land or in undulating hilly areas, it can maintain efficient operating performance and reduce operation interruptions and crop losses caused by terrain changes.

The utility model discloses a flat plate type chassis of an agricultural unmanned trolley, which belongs to the technical field of agricultural vehicle parts, aims at solving the problem that the loss rate of crops is increased due to the fact that the agricultural vehicle generates large vibration in the driving process, and comprises a chassis frame, and two front wheels are rotatably arranged at the bottom of the chassis frame close to the front end. Two rear wheels are rotatably mounted at the positions, close to the rear end, of the bottom of the chassis frame, the cushioning mechanisms, the transverse cushioning springs and the longitudinal cushioning springs are matched, under the action of the pipe sleeves and the damping rods, vibration borne by the box body can be buffered, the situation that the vibration is transmitted into the storage box from the chassis can be effectively reduced, and the storage box is convenient to use. By means of the technical scheme, the vehicle can run more stably, the stability and safety of crops in the transportation process are improved, the crops can be prevented from being excessively squeezed, collided and damaged in the transportation process, and especially for easily-damaged fruits and vegetables, the shape and quality of the fruits and the vegetables can be kept, and the loss rate is reduced.

The invention discloses a rice field robot cluster remote cooperative control system based on edge computing, and relates to the field of program control, and the system comprises a master control module which serves as a central control end, issues a control instruction to distributed edge nodes, and receives state feedback; the crop identification module is deployed at an edge node and acquires attribute data of crop types, growth stages and distribution density of each region of the target rice field through an image sensor; the environment acquisition module is used for being deployed at an edge node and acquiring rice field meteorological data and soil environment data in real time; the crop growth environment and the machine state are monitored and responded in real time, when an abnormal environment event is detected, crop identification data can be rapidly integrated to generate an influence event and evaluate an influence coefficient, and a nearby automatic machine is immediately matched and scheduled for intervention, so that the timeliness of operation is ensured, crop loss can be effectively prevented, and the working efficiency is improved. And the efficiency of agricultural production is improved.

The disclosure generally relates to methods and systems for crop damage assessment using semantic reasoning. Conventional techniques using only specific data either individually or in a combination may result in bias and may not accurately estimate the crop damage, due to diversity in each of the natural calamities. The present disclosure solves the technical problems in the art using domain ontologies and a semantic reasoning over the spatio-temporal data for the automatic assessment of the crop damage due to the natural calamities. The present disclosure establishes automated crop loss assessment using trigger-based analysis of plurality of sources like satellite-based earth observations, weather observations, social media posts and news articles, for obtaining a spatio-temporal data. Then the spatio-temporal data is reasoned over the domain knowledge graph, using the semantic reasoning technique, for the crop damage assessment.

The invention relates to a device (2) for harvesting stalky stem crops, having a support frame (4) and a plurality of picking units (6) arranged next to one another, such as, for example, a corn header. Each of the picking units (6) has picking plates (12) which laterally delimit a picking gap (10). In order to keep the crop losses in the region of the picking gap (10) low, the invention proposes that the picking plates (12) have, along the picking gap (10), a coating (54) with a flexible material having a material hardness in the range of 60 – 80 Shore A.

The invention relates to a device (2) for harvesting stalky stem crops, comprising a supporting frame (4) and a plurality of picking units (6) arranged next to one another, such as, for example, a corn header. The picking units (6) are covered by covering hoods (6). In order to keep the crop losses in the transition region between the covering hoods (6) and the conveying devices (14) low, the invention proposes that the outwardly pointing surfaces of the wear protection strips (26) have a flexible material with a material hardness in the range of 60 – 100 Shore A.

The present invention relates to a multimodal fusion method and system for adaptively adjusting the height of a corn harvester's header, belonging to the field of automated control technology. The method comprises the following steps: using a binocular camera to capture crop images and preprocessing them to obtain a depth map; inputting the images into a YOLO V8n model, and obtaining the coordinates of the bottom center point of the corn ear's bounding box through model calculations; performing plane fitting on the depth map to obtain the ground height; and calculating an initial value for the corn ear's height based on the ground height and the center point coordinates; collecting vibration data from the harvester's operation, performing vibration compensation on the initial value to obtain a height correction value; and dynamically adjusting the harvester's header height using a dynamic fuzzy PID algorithm based on the correction value. The present invention combines a target detection model with a dynamic fuzzy PID algorithm to address the problems of low visual detection accuracy and delayed control response in existing agricultural machinery in complex field scenarios, effectively reducing crop losses during harvesting operations.

The invention discloses a selenium sand melon growth monitoring management method and system based on Internet of Things, and particularly relates to the technical field of data management. A planting area is divided into a plurality of monitoring areas, sensor data are collected in real time, the matching condition of the data and a historical range and the overlapping degree of data of multiple land parcels are analyzed, and the possible problem of equipment binding errors or serial number confusion is recognized; when the data overlapping degree is high, an operation instruction sent by the management system and an equipment feedback signal are recorded, and the fluctuation condition of the equipment response success rate is analyzed; according to the method, data overlapping and response success rate fluctuation are integrated, the accuracy of system operation execution is evaluated, when the accuracy is low, control logic and decision threshold values are dynamically adjusted by predicting an accuracy coefficient in a fixed time period, so that large-area crop loss caused by decision errors is effectively avoided, the accuracy and reliability of system operation are improved, and the system performance is improved. And high efficiency and stability of agricultural production are guaranteed.

The invention belongs to the field of artificial intelligence, and relates to a farmland planting data analysis method, which comprises the following steps: when a survey and loss assessment signal is received, acquiring a plurality of farmland planting images in real time, and preprocessing all the farmland planting images to obtain a plurality of images to be subjected to loss assessment; the images to be subjected to loss assessment are sequentially input into a preset loss assessment model for loss analysis, and multiple pieces of initial crop loss data output by the loss assessment model are obtained; performing screening operation on all the initial crop loss data to obtain target loss data; and performing claim analysis based on the target loss data to obtain a crop claim value. The invention further provides a farmland planting data analysis device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the collected farmland planting image can be stored in a block chain. The method solves the problems of low claim settlement efficiency and insufficient claim settlement accuracy in the planting insurance claim settlement process.

An agricultural machine for reducing crop loss during a harvesting operation comprises: front ground engaging mechanisms coupled to a front axle; a chassis supported above the ground by the front ground engaging mechanisms; a cutting head positioned forward of the front ground engaging mechanisms and configured to harvest crop material at a work location; an image sensor; and a controller operatively coupled to the image sensor. The image sensor captures images of a field of view encompassing an area rearward of the front axle. The controller receives data corresponding to the images from the image sensor, determines the amount of grain shown in the images, and adjusts an operating characteristic of the agricultural machine based on the determined amount of grain shown in the images of the field of view.

This disclosure includes devices, methods, and systems for detecting crop loss in a combine harvester. The method may include receiving forward speed data from one or more speed detection systems. The method may include receiving crop condition data from one or more feed sensors. The method may include determining one or more crop condition states, at least partially, based on one or more of the forward speed and crop condition data. The method may include determining crop loss, at least partially, based on one or more of the crop condition states. The method may include adjusting one or more combine harvester operating parameters, at least partially, based on the determination of crop loss.

A crop loss monitoring procedure involves detecting crop loss within a measurement area when that area corresponds to an unharvested portion of a field, and generating crop loss sensor data pre-harvest based on this detection. The procedure further involves detecting crop loss within the measurement area when that area is located behind a remote end of a harvester header, and generating crop loss sensor data during harvest based on this detection.The procedure further includes identifying a crop loss value before harvest based on the crop loss sensor data before harvest, identifying a crop loss value at harvest based on the crop loss sensor data at harvest and the crop loss value before harvest, and generating one or more control signals based on at least one of the crop loss value before harvest or the crop loss value at harvest.

A crop loss correction system receives one or more crop loss sensor signals that are indicative of crop lost by a harvesting machine. A correction component receives context information from a set of context sensing components to identify a context of the harvesting machine. The correction component corrects the crop loss sensor signals, based upon the context information, to obtain a corrected loss signal indicative of the sensed crop loss, corrected based on the mobile machine context. The corrected loss signal is output to an output device.

This invention relates to the field of analytical detection technology, and more particularly to a flexible methyl salicylate gas sensor and its preparation method. The preparation method involves prepolymerizing polypyrrole with methyl salicylate template molecules and methacrylic acid functional monomers in an organic solvent, followed by adding a crosslinking agent and an initiator to carry out a polymerization reaction to obtain a copolymer. After removing the methyl salicylate template molecules, a MIP-PPy material is obtained. The MIP-PPy material is then mixed with a dichloromethane solution of polyvinyl chloride and coated onto an electrode surface. This sensor can rapidly and accurately detect the content of methyl salicylate released by plants, achieving stable and reliable in-situ detection of methyl salicylate in plants. This technology allows for early detection of plant diseases and prevention of infection spread, thus preventing crop losses.

Systems and methods are provided for monitoring crop loss associated with a header arrangement operably couplable to an agricultural harvesting machine. A sensor response is received from a sensing element of a sensor arrangement provided as part of or otherwise associated with a sample unit, the sensor response being dependent on material constituents present within the sample unit, and where the sample unit is operably coupled to one or more functional components of the header arrangement. A measure of crop constituents provided by the sample unit is determined based on the sensor response and used to determine a loss metric for an agricultural operation performed by the header arrangement. Operation of one or more operable components of or otherwise associated with the header arrangement and / or the agricultural harvesting machine is then controlled in dependence on the determined loss metric.

The invention belongs to the technical field of crop disease early warning, and particularly relates to an intelligent early warning system and method for crop diseases, and the method comprises the steps: obtaining the information of crops in a current farmland, and building a farmland visual model; detecting the disease condition of the current farmland through farmland monitoring equipment, and generating a disease information feature set; the method comprises the following steps: collecting farmland related meteorological data, processing a disease information feature set, establishing a disease propagation analysis model, analyzing disease related factors, and establishing a farmland disease influence factor database; analyzing the disease development of the current farmland according to the farmland disease influence factor database, evaluating the disease risk of the grids without diseases in the farmland, feeding back the judgment result to the farmland visualization model, and warning related personnel. The accuracy of predicting the disease propagation trend and the disease occurrence position under the complex and changeable meteorological conditions is improved, crop loss caused by prevention and control time delay is avoided, and the disease prevention and control effect is improved.

Disclosed herein are biocontrol compositions against plant fungal pathogens and methods of use thereof for the prevention or reduction of crop loss or food spoilage. The biocontrol composition may comprise at least one microbe, or a secondary metabolite of the at least one microbe, with anti-fungal or anti-pathogenic activity. The methods and compositions disclosed herein may prevent or inhibit the growth of a variety of different pathogens, including pathogens of the genus Penicillium. The biocontrol compositions may be applied to a plant, a seed, or a produce thereof or to a packaging material used to transport or store the produce.

The application provides an auxiliary positioning system and a profiling cutterbar, and belongs to the technical field of agricultural machinery, and comprises a connecting rod type profiling device, a floating tension spring adjusting device, an auxiliary positioning system, a cutter angle self-adaptive adjusting device and a drag reduction sliding shoe device. The application can realize self-adaptive floating profiling and automatic positioning of the cutterbar to the ground, has a cutter inclination angle self-adaptive adjusting function, can realize the ground profiling of the existing alfalfa harvester cutterbar, solves the problems of cutter entering the soil, uneven stubble, large power consumption and poor feed quality during the operation of the harvester, and saves the cost. The connecting rod type profiling cutterbar utilizes the kinematic characteristics of the connecting rod mechanism, is beneficial to the ground following profiling and positioning of the cutterbar, reduces the forward resistance, can reduce the cutting height, reduce the crop loss, ensure the stability of the cutting height, prevent the cutter from touching the soil, and improve the adaptability of the mower to the operation terrain.