240 results about "Precision agriculture" patented technology

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a task allocation and conflict resolution system and method for multi-unmanned aerial vehicle collaborative operation, and provides the following scheme: dividing an initial operation area and generating a response weight by constructing a crop growth state map and a three-dimensional plot model; based on path planning and resource adaptation, a flight route is dynamically generated, and the crop state and the unmanned aerial vehicle state are monitored in real time; when adjustment conditions are met, a multi-dimensional dynamic task evaluation model is constructed, and task migration and conflict decoupling are completed in combination with particle swarm optimization and an autonomous negotiation mechanism. The method is suitable for a precision agriculture scene, the unmanned aerial vehicle path dynamic scheduling in the operation area and the high-priority area precision coverage are realized, and the operation efficiency and the resource cooperation capability are improved.

The invention relates to the crossing field of precision agriculture and artificial intelligence technology, in particular to a soil component detection system based on machine learning and infrared spectroscopy, which is characterized in that a soil spectrum is acquired on site through a portable Fourier infrared spectrometer, and after pretreatment, feature vectors are constructed by fusing climate, soil and crop data; the deep learning decision-making module extracts spectral features by using one-dimensional convolution, fuses multi-dimensional information through an attention mechanism, synchronously outputs a fertilization scheme, crop suitability scores and soil improvement measures by a multi-task learning sub-network, and finally, verifies by combining an agronomic knowledge base so as to obtain a fertilization result. According to the method, a comprehensive decision report containing a quantitative fertilization formula, a crop suitability sequence and a soil improvement scheme is generated, precise agricultural guidance is realized, spectral features are automatically extracted by adopting a one-dimensional convolutional neural network, and a multi-modal data fusion and multi-task learning framework is combined, so that the system achieves relatively high precision in the aspect of soil nutrient prediction.

The invention relates to the technical field of precision agriculture monitoring, and discloses a precision agriculture monitoring system based on adaptive wireless communication. The system comprises a multi-source environment acquisition module, a communication protocol adaptation module and a crop state evaluation module. The multi-source environment acquisition module acquires soil humidity data, illumination intensity data and meteorological parameter data through wireless sensor nodes deployed in a farmland area, and performs timestamp synchronization on the data to ensure the consistency of the data in time dimension. And the communication protocol adaptation module dynamically selects a wireless transmission frequency band according to the synchronous data volume, allocates communication time slots based on the data priority, and generates adaptive transmission channel parameters so as to cope with the communication requirements in the data volume fluctuation and complex environment. The crop state evaluation module receives the transmission channel parameters, analyzes the real-time environment data flow, and divides the current crop growth state into a growth standard state, a growth lag state or a growth fluctuation state. The system provides support for fine management of agricultural production.

The invention discloses a digital twin-driven water and fertilizer real-time dynamic balance transfer system, which comprises a sensing and data acquisition module for monitoring moisture, salinity, pH value, illumination and rainfall meteorological data of soil in real time through various sensors deployed in a farmland; and the digital twinborn modeling and simulation module is used for constructing a digital twinborn model of the farmland based on the real-time data collected by the perception and data acquisition module. According to the invention, by arranging the perception and data acquisition module, the digital twin modeling and simulation module, the intelligent decision-making and regulation module, the execution and feedback module, the historical data tracing and system optimization module, the expansion module, the networking module and the edge-cloud cooperative computing architecture, the farmland environment and the crop growth condition can be monitored in real time; the digital twinborn modeling and simulation module constructs a digital twinborn model of a farmland based on real-time data, simulates a soil environment and crop growth, and optimizes a water and fertilizer proportioning strategy.

The invention provides a farmland soil multi-parameter real-time monitoring system and method based on the Internet of Things. The system comprises a sensor module, a sensor collaborative scheduling module, a data transmission module, a data processing and storage module, an intelligent decision module, a user interaction module and a power supply module. Soil monitoring data, meteorological satellite information and historical yield records are fused, deep learning, a decision tree and a nutrient balance algorithm are combined, an accurate fertilization and irrigation scheme is generated, maintenance decision optimization is achieved, the water and fertilizer utilization efficiency is improved, and resource waste caused by extensive management is reduced; through dynamic sampling, data cross validation, sensor fault self-diagnosis and multi-source data fusion, the system can make a decision, effectively solves the defects of an existing system in the aspects of monitoring precision, data reliability, equipment endurance and decision refinement, and provides an efficient and reliable monitoring and management scheme for precision agriculture.

The invention belongs to the technical field of rice panicle shielding image data processing, and provides a rice panicle shielding image restoration method and system based on a generative adversarial network, and the method comprises the steps: collecting rice panicle images at different angles and under different illumination conditions to construct a data set, employing a target detection model to carry out the positioning of a rice panicle region, and classifying the shielding types, and extracting a visible area of the rice spike by adopting a semantic segmentation network, and repairing a sheltered area under a generative adversarial network framework to obtain a complete rice spike image. The method is suitable for complex scenes such as leaf shielding, inter-panicle mutual shielding and mixed shielding, texture details and structure consistency of the repaired image can be guaranteed, acquisition of complete phenotype information of the rice panicles is achieved, and reliable data support is provided for rice yield estimation and precision agricultural management.

The invention relates to the technical field of image classification, in particular to an edge-cloud collaborative rice disease monitoring method and system for precision agriculture, and the method comprises the steps: constructing a rice disease recognition network; a squeezing-incentive attention module and a multi-scale convolution-space attention module are introduced into a backbone network and a neck network of the rice disease recognition network; replacing standard convolution in the backbone network and the neck network with deep separable convolution; training the improved and optimized rice disease recognition network to obtain a lightweight student model; soft label distillation loss is constructed based on prediction distribution output by a pre-trained deep teacher model and a student model; a total loss function is constructed in combination with cross entropy loss, training of student models is guided through a back propagation algorithm, and a rice disease lightweight recognition model is obtained and used for recognizing rice diseases. Through edge end deployment of a lightweight model obtained through distillation, rapid reasoning of a high-precision model is realized on low-power-consumption edge equipment.

The invention relates to the technical field of precision agriculture and grape cultivation, and discloses a vineyard fertilization method based on unmanned aerial vehicle multi-source remote sensing data, and the method comprises the steps: obtaining a canopy multi-spectral image and three-dimensional structure data through an unmanned aerial vehicle; carrying out image preprocessing and finely extracting a grape canopy region; fusing the extracted spectral vegetation index, texture features and three-dimensional structure features, and constructing a multi-source feature vector; constructing and optimizing a nitrogen nutrition inversion model through a machine learning algorithm by utilizing actually measured nitrogen nutrition parameters; generating a nitrogen content distribution diagram based on a model inversion result, calculating the nitrogen deficiency amount and the recommended dressing pure nitrogen amount of each space unit in combination with critical nitrogen concentration diagnosis and target yield, and converting the nitrogen deficiency amount and the recommended dressing pure nitrogen amount into the use amount of a foliage spraying working solution; and generating a variable fertilization prescription map, and converting the variable fertilization prescription map into a nozzle flow control instruction executable by the unmanned aerial vehicle to realize on-demand accurate variable fertilization. According to the invention, closed-loop management from nitrogen nutrition monitoring to variable rate fertilization is realized, and the nitrogen fertilizer utilization efficiency and the fertilization accuracy are improved.

The invention discloses a farmland furrow three-dimensional point cloud data correction method and system based on a laser radar, and relates to the technical field of three-dimensional image processing based on the laser radar, in particular to a three-dimensional image geometric correction technology. The problems that original point cloud data directly obtained under the dynamic operation condition of an existing agricultural machine has geometric distortion, and the shape of a furrow cannot be truly reflected are solved; meanwhile, the problems that furrow quality evaluation seriously depends on low-efficiency manual means, the adaptability of an existing automatic technical scheme to an unstructured field environment is insufficient, and robust, real-time and high-precision perception of a real three-dimensional shape of a furrow cannot be achieved are solved. According to the method, dynamic tilt correction is carried out through real-time acquisition of point cloud and agricultural machine attitude information, boundary feature points are extracted, boundary lines are fitted, and a three-dimensional point cloud set of a single furrow is reconstructed by fusing a self-adaptive clustering algorithm of spatial prior. The method is suitable for the fields of farmland tillage and soil preparation quality detection, precision agricultural management, agricultural machinery intelligent operation and the like under the dynamic operation condition of agricultural machinery.

The invention relates to a precision agriculture monitoring system and method based on multispectral imaging. The system and method are applied to real-time monitoring of crop physiological parameters and variable fertilization decision making. The system comprises an unmanned aerial vehicle imaging module, an edge computing unit and a cloud analysis server. The unmanned aerial vehicle module is provided with a multispectral filter wheel, a three-axis holder and an RTK positioning device and is used for acquiring a high-resolution crop image; the edge calculation unit integrates a radiation correction module, an image splicing module and a canopy segmentation module to realize on-site preprocessing; the cloud server runs a deep learning model and a feature fusion mechanism, outputs estimation of parameters such as nitrogen, chlorophyll and moisture, and generates a high-resolution fertilization prescription map. In the aspect of the method, dynamic monitoring of the nitrogen content of crops is realized through route planning, data synchronization, radiation normalization, multi-source feature fusion and time sequence prediction. The system supports online updating and ground verification of the model, has high precision, low delay and large-area operation capability, and is suitable for intelligent agriculture and precise fertilization scenes.

The invention discloses an orchard unmanned aerial vehicle multispectral image noise restoration method based on a generative adversarial network, and relates to the technical field of multispectral image noise restoration, and the method comprises the steps: synthesizing a noisy sample containing stripes, photon particles, thermal noise, Rayleigh, shielding, compression artifacts, overexposure and waveband crosstalk on a noise-free reference image through a conditional generative adversarial network; a crosstalk matrix T obtained through optical calibration is introduced; based on the canopy / soil / sky semantic partition, main-cooperation-subdivision level superposition is implemented; generating hierarchical labels by taking energy conservation consistency and thermal noise correlation as thresholds; a branch and region self-adaptive repair module corresponding to classification is arranged in the image-to-image generative adversarial network, and joint loss training containing T regularization is adopted; and outputting the repaired image subjected to physical consistency checking. The method gives consideration to visual quality and spectral fidelity, inhibits cross-band pollution, improves the stability of indexes such as NDVI and NDRE, and is suitable for precision agricultural monitoring.

The invention relates to a rice character parameter inversion method combining an optical-microwave collaborative model and a variational auto-encoder. The method comprises the following steps: acquiring multiband optical reflectivity, a microwave backscattering coefficient and observation geometric information of a research area; inputting the data into a multi-mode encoder, and encoding the data into probability distribution of a potential space; an optical and microwave physical model is used as a differentiable decoder, potential variables sampled from a potential space are received, and remote sensing data are simulated and generated; a self-supervised learning normal form is adopted, and end-to-end training is carried out on the whole frame by minimizing a composite loss function; a trained encoder is used as a rapid inversion model, vegetation biological physicochemical parameters are extracted from new remote sensing data, rapid inversion of the parameters is achieved, and uncertainty of the parameters is quantified. The rapid and accurate vegetation character parameter inversion method is established by fusing multi-modal data, a physical model and a deep learning technology, important reference is provided for rice growth monitoring and precision agriculture, and the method has popularization prospects.

The invention discloses a multi-degree-of-freedom programmable deep scarification mechanism and a control method thereof, belongs to the technical field of agricultural machinery, and aims at solving the problems that an existing deep scarification machine tool is fixed in movement track, single in operation mode, lack of multi-dimensional space movement capacity and lack of an intelligent closed-loop control system and the like. The mechanism comprises an initial pitching adjusting mechanism, a transverse translation mechanism, an operation depth adjusting mechanism, a posture fine adjusting mechanism, a shovel body rotating mechanism and a control system, and all the mechanisms are in hierarchical connection to form a collaborative operation system. The initial pitching adjusting mechanism is connected with the transverse translation mechanism, and the operation depth adjusting mechanism is connected below the transverse translation mechanism and can adjust the mechanism position and the tilling depth; the posture fine adjustment mechanism is of a four-hydraulic-cylinder space parallel connection structure and can drive the subsoiler to achieve multi-dimensional posture adjustment in cooperation with the shovel body rotating mechanism. The operation mode of the subsoiler can be programmable and reconstructed, the subsoiler can adapt to a complex soil environment, the traction resistance is reduced, the soil crushing quality and the operation precision are improved, and support is provided for precision agriculture.

The invention discloses a rice nutrient accurate monitoring and fertilization decision-making system based on multi-source data fusion, particularly relates to the field of precision agriculture, and is used for solving the problems of nutrient monitoring deviation and inaccurate fertilization caused by spectrum saturation in a rice vigorous growing period. The method comprises the following steps of: acquiring red edges and near-infrared reflection from multiple angles, synchronously acquiring texture and structure images, fusing to generate an anti-saturation characteristic cube, outputting a peak period characteristic packet by phonological transition point space-time segmentation, eliminating saturation response to construct a separable nitrogen trace spectrum, and calculating the nitrogen trace spectrum. The method is realized by calculating an energy concentration ratio and a water layer confluence indicating quantity in a situation constraint graph, outputting a calibration coefficient to adjust a prescriptional graph, and finally combining a historical track and a microtopography check drop point to output an execution prescriptional graph, so that the peak period diagnosis precision is improved, excessive or insufficient fertilization is avoided, and efficient nutrient management is realized.

The invention belongs to the technical field of crop classification, and particularly discloses a corn and peanut crop classification method based on a Sentinel-2 satellite image and deep learning, which uses a DB-NET model to classify corn and peanut crops from a Sentinel-2 image, focuses on solving the problem of accurate identification of the corn and peanut crops, and realizes accurate estimation of a planting area based on a classification result. The innovation of the method is reflected in three aspects: in the technical level, three methods of reelif-F, RF and CST are introduced to carry out feature optimization so as to optimize model input, in the method level, an attention-guided feature fusion mechanism and a double-branch up-sampling feature fusion model are constructed, and in the application level, a complete technical chain from classification to decision support is established. Through system design, high-precision classification and area estimation of corn and peanut crops are finally realized, and reliable technical support is provided for practical application of precision agriculture.

The invention discloses a forsythia suspensa growth vigor monitoring method based on unmanned aerial vehicle remote sensing, and particularly relates to the technical field of unmanned aerial vehicle remote sensing monitoring. S2, calculating a vegetation index; s3, obstacle recognition; s4, evaluating the growth vigor; and S5, grade drawing. According to the method, a structured data set is constructed through integration of multispectral data and space coordinates, a high-resolution vegetation index map is generated in a pixel-by-pixel calculation mode, non-vegetation obstacles are accurately recognized by combining band ratio analysis and threshold judgment, vegetation index change trends are analyzed through time series data, and phenological period standard interval deviation is calculated. Spatial growth trend grade distribution is generated through mapping evaluation coefficients, the processing logic realizes closed-loop linkage of data acquisition and analysis, eliminates interference of non-target ground features, improves the accuracy of monitoring results, establishes a dynamic evaluation mechanism, and provides reliable basis for precise agricultural management.

The invention discloses a tilling depth detection method and system based on three-dimensional data, and relates to the technical field of precision agriculture, and the method comprises the steps: building nonlinear mapping based on a corrected attitude angle and a lifting arm angle, updating the nonlinear mapping through extended Kalman filtering, obtaining a high-precision real-time attitude angle, and obtaining a tilling depth detection result; and constructing an initial tilling depth model based on the high-precision real-time attitude angle, and fusing the target tilling depth and the LiDAR depth to obtain a target tilling depth value. By fusing data of multiple sensors such as LiDAR, RGB-D and IMU, adopting complementary filtering and an RANSAC algorithm to correct an attitude angle and improve attitude estimation precision, a point cloud density and signal-to-noise ratio adaptive weight mechanism is introduced, fusion of target tilling depth and visual depth is optimized, robustness and accuracy of tilling depth detection under a complex field condition are improved, and the method is suitable for large-scale popularization and application. The problem that a traditional method is insufficient in adaptability to mechanical dynamic changes and terrain slopes is effectively solved.

The invention discloses a self-adaptive drip irrigation control system and method fused with a soil solution optimal model, and belongs to the field of agricultural irrigation. The system integrates a layered soil and soil solution monitoring module, a weather and crop growth monitoring module, a data acquisition and management module, an irrigation and fertilization execution module and an operation feedback module. Through deep-buried multi-layer soil sensing and solution collection, real-time monitoring of key parameters such as moisture and nutrients of different soil layers is achieved, and a dynamic target is set in combination with crop growth requirements and meteorological data. The system adopts a soil solution optimal model, performs dynamic balance analysis on monitoring data and target parameters, introduces a multi-factor linkage and layered regulation strategy, intelligently generates an optimal water and fertilizer scheme and accurately executes the optimal water and fertilizer scheme. Automatic recording and quality feedback are achieved in the whole process, model self-learning and scheme optimization are supported, the crop nutrient utilization rate is increased, resource waste is reduced, and efficient and intelligent precision agricultural water and fertilizer management is achieved.

The invention relates to the technical field of unmanned aerial vehicle cooperative control, and particularly discloses a dynamic task allocation and anti-collision method for multi-unmanned aerial vehicle cooperative plant protection operation, which realizes initial task allocation through dynamic task priority map generation and distributed bidding negotiation, and predicts potential collision points in combination with a kinematic model. A virtual elastic channel width adjusting strategy is adopted to carry out path fine adjustment so as to solve conflicts; aiming at the job coverage change caused by path adjustment, the system automatically identifies an omitted or newly added area as a new task for bidding distribution again, so that the comprehensive coverage is ensured; meanwhile, the weights of task allocation and conflict avoidance are dynamically adjusted based on the global operation progress, so that the unmanned aerial vehicle cluster focuses on rapid efficiency promotion in the initial operation stage and focuses on safe and fine ending in the later operation stage. According to the method, the resource utilization efficiency, the flight safety and the operation robustness are remarkably improved, and the method is suitable for precise agricultural plant protection requirements in a complex dynamic environment.

The invention relates to the field of precision agriculture and artificial intelligence, in particular to a crop fertilizer efficiency regulation and control method and device based on a time sequence spectrum and a medium. The method comprises the following steps: firstly, constructing a multi-temporal image sequence fusing spectral change and environmental information, introducing a depth recognition model with a time sequence convolution and attention fusion mechanism, transmitting the multi-temporal image sequence to the depth recognition model, carrying out spectral mutation point calibration on spectral time sequence data in the multi-temporal image sequence, and carrying out depth recognition on the spectral mutation point; the calibrated spectrum mutation point is used as a guide item of a time attention module, so that the model is endowed with sensitivity to physiological state change; the deep recognition model achieves current nutrition state scoring, future trend prediction and variable fertilization suggestion, and outputs prediction confidence interval and waveband channel importance interpretation information. The deep learning model and the feedback mechanism are combined, a multi-output structure, the prediction confidence perception capability and the self-adaptive optimization function are achieved, and the intelligent level of crop nutrient management can be improved.

The invention discloses a crop physiological status real-time diagnosis system and method based on multi-modal sensing, and belongs to the technical field of intelligent agriculture and agricultural information. The system comprises a multi-mode sensing module, a data fusion processing module and an intelligent diagnosis and decision module. The multi-mode sensing module synchronously collects crop morphology, physiology and environment data; the data fusion processing module performs preprocessing and feature extraction on heterogeneous data; the intelligent diagnosis and decision module realizes multi-source information cross validation through a special diagnosis model, and generates a stress distribution diagram and a variable operation prescription; the method is based on the system, and leap-over diagnosis from appearance to mechanism is realized through multi-modal data acquisition, fusion processing and intelligent diagnosis; according to the invention, the industrial problem of high misjudgment rate of a single information source is solved, early, accurate and in-situ diagnosis of the crop physiological status is realized, and an intelligent equipment solution integrating perception and decision is provided for precision agriculture.

The invention discloses a reverse transcriptase, wherein a coding sequence of the reverse transcriptase is derived from a Rattus norvegicus genome. The protein sequence of the protein comprises a protein sequence of a wild type Rattus norveicus, or a protein sequence which is subjected to engineering modification on the basis of the wild type Rattus norveicus, or a protein sequence which is subjected to engineering modification on the basis of the wild type Rattus norveicus. Furthermore, the protein sequence of the gene is as shown in any one of SEQ No.1-55. In a rice genome site and a human HEK 293T cell genome site, the system shows efficient editing efficiency of a guide editor. The invention provides a series of efficient reverse transcriptase components which can be carried on a guide editing system, important bottom-layer technical support is provided for research of point mutation in a genome, creation of a special site disease model and correction of genetic mutation sites, and the reverse transcriptase component has a wide application prospect in the field of precision medicine and precision agriculture breeding.

The invention discloses a vegetation coverage index algorithm and system based on an unmanned aerial vehicle. The algorithm comprises the following steps: S1, data acquisition and multi-source data preprocessing; s2, dynamic environment correction and multi-source data fusion; s3, red edge enhanced vegetation index calculation and terrain correction; s4, vegetation coverage intelligent prediction and precision verification; and S5, result visualization and decision support: generating a vegetation coverage spatial distribution map and a statistical report, and supporting a resource management decision. By integrating the unmanned aerial vehicle, the multispectral imaging sensor, the dynamic environment correction model and the data fusion algorithm, efficient and high-precision technical support is provided for precision agriculture, ecological resource management and disaster monitoring.

The utility model discloses an automatic cleaning device for a precision agriculture water drip irrigation tape, and relates to the technical field of drip irrigation tapes. The drip irrigation belt comprises an installation body and a drip irrigation belt body, the outer surface of the drip irrigation belt body is fixedly communicated with a plurality of evenly-distributed irrigation heads, the outer surface of the installation body is symmetrically and fixedly connected with fixing plates, the outer surfaces of the fixing plates are fixedly connected with inclined scraping plates, and the outer surface of the installation body is symmetrically and fixedly connected with connecting plates. The outer surface of the connecting plate is fixedly connected with a first motor, one end of an output shaft of the first motor is fixedly connected with a rotating rod, the outer end of the rotating rod is rotatably connected with the outer surface of the connecting plate, the outer surface of the rotating rod is fixedly connected with a first transmission wheel, the end, away from the first motor, of the connecting plate is fixedly connected with an arc-shaped plate, and a rotating roller is rotatably connected into the arc-shaped plate; the utility model solves the problems that the outer surface of the drip irrigation tape can be corroded by soil after long-term use and the irrigation emitter can enter the soil to cause blockage.

The application relates to the technical field of precision agriculture monitoring, and discloses a precision agriculture monitoring system based on adaptive wireless communication. The system comprises a multi-source environment acquisition module, a communication protocol adaptation module and a crop state evaluation module. The multi-source environment acquisition module obtains soil humidity data, illumination intensity data and meteorological parameter data through wireless sensor nodes arranged in farmland areas, and synchronizes the timestamps of the data to ensure the consistency of the data in the time dimension. The communication protocol adaptation module dynamically selects a wireless transmission frequency band according to the synchronous data volume, allocates communication time slots based on data priority, and generates adaptive transmission channel parameters to cope with the communication requirements under the conditions of data volume fluctuation and complex environment. The crop state evaluation module receives the transmission channel parameters, analyzes real-time environment data streams, and divides the current crop growth state into a growth standard state, a growth lag state or a growth fluctuation state. The system provides support for fine management of agricultural production.

The invention relates to the technical field of tobacco plant identification and counting, and discloses an unmanned aerial vehicle tobacco plant identification and counting method and system based on improved deep learning, which combines technologies of image classification, image segmentation, feature point extraction, machine learning, target detection and the like with refined intelligent identification of tobacco. According to the method, high-precision image recognition and analysis are realized by utilizing the efficient feature extraction and calculation capability of the method, point plant counting can be accurately performed, the efficiency and precision of agricultural production are greatly improved, the problem of calculation complexity of a traditional method in large-scale high-resolution image processing is solved, and powerful technical support is provided for precision agriculture. Therefore, the invention provides an unmanned aerial vehicle tobacco plant identification and counting method based on improved deep learning target detection and multi-dimensional post-processing, and high-precision and robust detection and counting of tobacco plants in large-scale tobacco field orthoimages are realized by constructing a three-stage assembly line of image preprocessing, improved target detection and multi-dimensional post-processing.

The present application belongs to the field of low-altitude unmanned aerial vehicle remote sensing and precision agriculture technology, and discloses a crop canopy coverage high-throughput acquisition method and system for unmanned aerial vehicle remote sensing. The method quantitatively describes the visual feature pattern between the crop canopy and the soil background from the high-resolution RGB image; the support vector machine method is used to automatically mine the association mapping between the category semantics and the visual feature pattern, to obtain the preliminary extraction result of the crop canopy and the soil background, and to generate the single-time-phase crop canopy mask image; the dynamic weed removal method of the RGB image time sequence is used to optimize all single-time-phase crop canopy mask images of the crop growth, to respectively count the peanut canopy pixel cluster number and the soil pixel cluster number in each plot in each plot, and to complete the accurate estimation of the crop canopy coverage. The present application provides a basic technical means for the fields of agricultural precision management, crop high-throughput phenotype monitoring and digital breeding, etc.

The application relates to a preparation method of a biosensor. The application relates to a preparation method of a portable pesticide detection biosensor based on acetylcholinesterase (AChE)@poly(allylamine hydrochloride)-hydrogen bond organic framework (PAH-HOF) and application of the biosensor in on-site rapid chlorpyrifos detection, and belongs to the technical field of biosensors. According to the method, AChE is combined with poly(allylamine hydrochloride) (PAH) to change the surface charge and promote self-assembly of the AChE and the hydrogen bond organic framework (HOF), so that an AChE@PAH-HOF composite material with high catalytic activity, high embedding rate and high stability is formed; then the material is integrated into a water-retaining glycerol-sodium alginate hydrogel (G-SA) matrix to construct a disc-shaped sensor. The sensor can be attached to the surface of plants and used for long-term in-situ monitoring of organophosphorus pesticides. In combination with an image processing algorithm, the system realizes accurate tracking of the chlorpyrifos degradation process on the surface of tomato plants, and the detection limit reaches 1 ng / mL. The application provides non-invasive and low-cost technical support for crop health management and sustainable precision agriculture.

The invention discloses an inter-plant light supplement lamp intelligent control system based on PWM dimming and wireless networking, and relates to the technical field of plant light supplement illumination control, and during operation of the system, the system comprises a central management server, a wireless communication gateway, an environmental data acquisition unit, an intelligent ventilation light supplement terminal, a distributed power supply management unit and a mobile monitoring terminal. The server generates a PWM control instruction based on the environmental parameters and issues the PWM control instruction to the intelligent terminal through the wireless gateway; and the terminal adjusts the light intensity of the LED array by using the PWM driving unit, and synchronously controls the active ventilation device to improve the microenvironment between plants. Through deep integration of PWM dimming and wireless networking, gridding management and control of the greenhouse environment are realized, the photosynthetic efficiency of the middle and lower parts of plants is effectively improved, the gas diffusion rate is enhanced, the system wiring cost and energy efficiency loss are reduced, and precise agricultural production is realized.

The application discloses a rice nutrient precision monitoring and fertilization decision system based on multi-source data fusion, and particularly relates to the field of precision agriculture, and is used for solving the problems of nutrient monitoring deviation and inaccurate fertilization caused by spectral saturation during the rice vigorous growth period, and is achieved by the following steps: collecting red edge and near-infrared reflection from multiple angles, synchronously acquiring texture and structure images and fusing to generate an anti-saturation feature cube, outputting a vigorous growth period feature package by spatiotemporal segmentation at the phenological transition point, constructing a separable nitrogen trace spectrum by eliminating saturated response, outputting a prescription front graph by adjusting the balance coefficient under the calculation of energy concentration and water layer confluence indicators in a context constraint graph, and finally outputting an execution prescription graph by combining historical trajectories and micro-terrain to check the landing point, so that the vigorous growth period diagnosis precision is improved, over-fertilization or under-fertilization is avoided, and efficient nutrient management is realized.