352 results about "Precision agriculture" patented technology

The invention discloses an intelligent crop growth prediction and optimization method based on multi-source data fusion, and belongs to the technical field of crop analysis, and the method specifically comprises the steps: obtaining space remote sensing data, ground sensor data and meteorological data, and carrying out the space-time alignment processing to generate fusion data representation; inputting the fused data representation into a prediction model, and outputting a crop future growth state sequence and a yield prediction result by coupling a crop growth mechanism and a dynamic environment response relationship; based on a yield prediction result, simulating long-term effects of different management strategies in a virtual growth environment, and screening out an irrigation scheme and a fertilization scheme with the optimal target function; transmitting the irrigation scheme and the fertilization scheme to a farmland execution terminal; collecting crop state feedback data and environmental parameter feedback data, and updating internal parameters of the prediction model based on the feedback data; according to the invention, crop growth management is converted from passive response to active regulation and control, and a whole-process intelligent solution is provided for precision agriculture.

The invention relates to the technical field of electric digital data processing, in particular to an agricultural planting optimization system and method based on big data analysis. The method comprises the following steps: acquiring agricultural planting environment parameter data, and performing preprocessing and standardization to obtain a standardized environment feature vector; acquiring image, sound and smell data of crops; performing deep feature extraction on the image, sound and smell data to generate a multi-modal feature representation matrix; and performing multi-modal data fusion based on the standardized environment feature vector and the multi-modal feature representation matrix to obtain a comprehensive feature space. According to the invention, through multi-modal feature fusion and digital twinborn modeling, the precision and real-time performance of crop growth risk and pest and disease prediction are improved, and optimal regulation and control and efficient resource utilization of precision agriculture are realized.

The embodiment of the invention provides a soil nutrient spectrum detection and regulation method and system, and belongs to the technical field of soil nutrient spectrum detection and regulation. The method comprises the following steps: collecting soil multispectral original data and environmental data, generating a calibration matrix and a nonlinear correction parameter, executing optical reflectivity conversion, and synchronously fusing a time domain reflectometry dielectric constant and a spectral moisture index to obtain fused moisture content; selecting a nutrient diagnosis characteristic wave band pair to obtain a moisture inhibition type spectral index; and predicting the soil nutrient content by using a pre-trained transfer learning model to obtain a soil nutrient content prediction value, and performing fertilization regulation and control based on the fertilization decision risk coefficient. According to the invention, extreme soil moisture measurement deviation is solved through a soil type adaptive moisture fusion technology; through multi-source risk quantitative decision and PID self-adaptive regulation and control, the fertilization amount can be dynamically optimized, execution safety is guaranteed, and a detection-decision-execution precision agriculture closed loop is formed.

The invention relates to the technical field of agricultural wireless sensor networks, and discloses an agricultural wireless sensor network topology reconstruction method based on an energy consumption balance model. The method comprises the following steps: acquiring multi-dimensional energy consumption data of each node, including node residual energy, communication load intensity and an environment interference coefficient; extracting a node energy health degree based on residual energy, generating a communication priority sequence according to communication load intensity, and analyzing an environment interference coefficient to generate an interference distribution thermodynamic diagram; inputting the result into an energy consumption balance evaluation model to generate a topology reconstruction strategy; and a self-adaptive topology adjustment map is constructed through a distributed optimization algorithm, and a node connection relation optimization scheme is output. The system is correspondingly provided with a multi-dimensional data acquisition module, an energy consumption state analysis module and the like. The problems of unbalanced energy consumption, non-uniform communication load, environmental interference and the like of the agricultural wireless sensor network can be effectively solved, the network performance is improved, the service life of the network is prolonged, and the development of precision agriculture is promoted.

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 technical field of crop growth monitoring, in particular to a crop growth monitoring method based on unmanned aerial vehicle remote sensing, which comprises the following steps of: acquiring a time sequence remote sensing image through an unmanned aerial vehicle and performing time phase processing to solve the problem of data inconsistency of a traditional method; a crop segmentation network based on wavelet transformation and edge guidance is constructed, high-frequency details and low-frequency semantic features are captured through wavelet decomposition, multi-scale dynamic interaction is achieved through cross-resolution feature fusion, and the problems of high-frequency detail loss and fuzzy segmentation are solved; based on a twin network, extracting dual-temporal global semantic features, and combining a difference compensation module to enhance the significance of the growth change, suppress noise interference and improve the weak change detection capability; and finally, fusing the segmentation mask and the difference characteristics through a multi-task framework, synchronously generating a pixel-level spatial distribution diagram and a time sequence thermodynamic diagram, realizing spatio-temporal conjoint analysis of the crop growth state, solving the problem of spatio-temporal information segmentation in a traditional method, and providing high-robustness monitoring decision support for precision agriculture.

The invention relates to the technical field of soil monitoring and data analysis, and discloses a soil carbon and nitrogen content dynamic prediction method based on deep learning. The method comprises the following steps: acquiring soil monitoring data from an environment monitoring platform, performing dimension reduction by using a multi-layer perceptron model to obtain core features, and dividing a dynamic monitoring data set according to the core features; taking the data set as input, and constructing an initial prediction model by using a time convolutional network; and constructing a meteorological factor library and an influence map, replacing an initial model time node, and obtaining a climatic factor node prediction model through cross validation. And performing regression fitting and cross validation verification by using a Gaussian process, and constructing a soil dynamic prediction model. According to the method, through multi-step data processing and model construction, the influence of soil data characteristics and meteorological factors is effectively mined, the dynamic change of the soil carbon and nitrogen content can be accurately predicted, and powerful support is provided for the fields of precision agriculture, environmental protection and the like.

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 discloses an unmanned aerial vehicle centimeter-level hovering control system based on single Beidou differential positioning, and particularly relates to the technical field of unmanned aerial vehicle high-precision positioning and control. The system comprises a Beidou RTK positioning module, a multi-sensor fusion module, a flight control module, a motor driving module, an exception handling module and a ground base station module. Centimeter-level positioning data are obtained through Beidou RTK, and a high-precision pose is output through tight coupling filtering by combining an IMU and a barometer; the flight control module adopts a model prediction algorithm to generate an instruction; the motor driving module realizes quick response; the exception handling module guarantees the reliability of the system; the ground base station provides differential correction. The single Beidou differential positioning and multi-sensor fusion technology is innovatively adopted, centimeter-level precision autonomous hovering control of the unmanned aerial vehicle is achieved, and the unmanned aerial vehicle hovering control system has the advantages of being high in precision, high in interference resistance and rapid in response and is suitable for industrial unmanned aerial vehicle application scenes such as precision agriculture and electric power inspection.

The invention provides a rice growth model construction method and system based on salt stress, and relates to the technical field of growth model.The rice growth model construction method comprises the steps that firstly, monitoring points are arranged in a planting sample area, and soil conductivity data of different depths are obtained through a multi-depth soil conductivity sensor and a Kriging interpolation method; collecting root system density, a multispectral remote sensing image and a leaf area index, and calculating a canopy salt stress index; measuring the concentration of related ions to obtain a sodium adsorption ratio, and establishing a salt stress coefficient by combining the soil conductivity and the irrigation volume; fusing canopy and soil stress indexes to generate a stress comprehensive index, and introducing a salinity feedback item to represent the crop compensation capability; and finally, constructing a hybrid machine learning model by adopting a convolutional neural network and a long-short-term memory network to realize rice growth dynamic prediction. According to the method, multi-dimensional salinity stress quantification of a soil-crop system is realized, and decision support is provided for salinization treatment in precision agriculture.

The invention provides a germination form measurement method and system based on improved U-Net to realize automatic measurement, reduce labor cost and significantly improve efficiency, thereby optimizing tomato planting and management. According to the application, the seed germination form acquisition system is used for acquiring images of seed culture and seed germination processes, a data set is constructed, and the data set is used for training the improved U-Net model. According to the invention, the up-sampling of the model is replaced by the DBB module, and the CAA is introduced into the jump joint and the decoder. The MIOU index and the MPA index of the model can be respectively improved by 2.03% and 0.39% compared with those of the basic U-Net. According to the method, based on the segmentation result of the improved model, the radicle length can be extracted in combination with the canny algorithm. Compared with the ImageJ measurement result, the correlation coefficient of the model can reach 0.991, and the measurement efficiency is improved by 300 times. According to the method, the influence of drought, salinization and different molds on tomato radicle germination can be evaluated, and an efficient solution is provided for nondestructive monitoring and crop management in precision agriculture.

The invention discloses an accurate analysis method based on smart agriculture multi-modal data, and relates to the technical field of smart agriculture, comprising the following steps: collecting and preprocessing soil humidity and meteorological data, extracting features to construct an environment sequence table, establishing a prediction and anomaly detection model, and outputting a soil humidity prediction value and an environment anomaly probability. And constructing a decision model to generate a regulation index, dynamically adjusting an irrigation strategy, visualizing farmland data distribution and trend in combination with a thermodynamic diagram and a broken line diagram, synchronously recording an operation log, and realizing a multi-modal data driven precise agricultural decision closed loop. Through multi-modal data fusion and a dynamic decision-making mechanism, the problems of traditional agricultural data island and disaster response lag are effectively solved, the regulation and control index is optimized, the water resource utilization rate is improved, the invalid irrigation risk is reduced, the farmland state is fed back in real time, precise partition management is assisted, the disaster response efficiency is improved, and abnormal losses are reduced. And agricultural intelligent upgrading is promoted.

The invention relates to the technical field of satellite remote sensing application, and discloses a crop classification method based on a multi-source satellite image, and the method comprises the steps: firstly obtaining multi-temporal satellite image data, extracting spectral reflectivity characteristics, generating a matrix, and constructing a classification model set; setting a feature parameter category set, and establishing a feature fusion correlation model based on the obtained texture, vegetation index parameters and spatial resolution data of the historical image; extracting spectrum and texture feature parameters of a crop area in the current image, and generating an optimized feature set in combination with model optimization; updating classification logic based on the optimized feature set and the classification model, generating a target classification scheme and calibrating a spatial relationship; and finally, acquiring crop growth cycle data, and establishing a time sequence feature association rule to optimize a crop type spatial distribution map. According to the method, through multi-source feature fusion and dynamic optimization, the crop classification precision and reliability are improved, and the method is suitable for a precision agricultural management scene.

The invention discloses an agricultural environment monitoring method and system based on digital agriculture. The method comprises the steps that S1, original environment data signals are collected through a heterogeneous sensor network deployed in a farmland area; s2, performing space-time fusion processing on the original environment data signal to generate a standardized environment state signal; s3, inputting the standardized environment state signal into a pre-trained multi-mode diagnosis model to generate an environment-crop coupling diagnosis signal; s4, according to the environment-crop coupling diagnosis signal, an agricultural operation decision signal is generated, and the agricultural operation decision signal comprises quantitative parameters of the irrigation amount, the fertilization formula or the disaster prevention and control instruction; and S5, converting the agricultural operation decision signal into an equipment control instruction signal, and driving an execution mechanism to complete precise agricultural operation. According to the agricultural environment monitoring method and system based on digital agriculture, the problems of data splitting, extensive decision making and execution lagging in agricultural environment monitoring can be solved.

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 discloses a corn yield prediction method and system based on chlorophyll fluorescence and deep learning, and the method comprises the steps: obtaining the yield prediction related data of a target region for many years in the past, and carrying out the preprocessing of the yield prediction related data; screening yield prediction related data of the hot and dry years according to a preset threshold value, and constructing a training data set under a stress condition based on the yield prediction related data of the hot and dry years; constructing a deep neural network model, and training, verifying and testing the deep neural network model by adopting the training data set to obtain a corn yield prediction model based on DNN; and inputting to-be-predicted data into the trained corn yield prediction model, outputting a corn yield prediction result, and generating a spatial distribution diagram. According to the method, the spatial distribution goodness of fit between a prediction result and official statistical data is remarkably superior to that of a traditional method, and a quantifiable and low-cost solution is provided for precise agricultural decision making under the extreme climate condition.

The invention provides an unmanned aerial vehicle target identification and positioning method and system based on multispectral fusion. The method comprises the following steps: firstly, acquiring multispectral image data, then analyzing the multispectral image data of multiple time phases into pixel-level data, then identifying an early infection area of the plant diseases and insect pests, then constructing a geometric distortion correction model, and then determining the relative position of the early infection area of the plant diseases and insect pests through multi-view space intersection calculation. And finally, fusing the real-time differential global navigation satellite system positioning data of the unmanned aerial vehicle and the relative position of the disease and insect pest early-stage infection area to output absolute geographic coordinates of the disease and insect pest early-stage infection area. According to the technical scheme provided by the invention, accurate conversion from a local coordinate system to a global geographic coordinate system is realized, an exact spatial position basis is provided for precise agricultural operation, and precise positioning of a pest and disease damage area is realized.

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 discloses a method for estimating total nitrogen of soil of different plough layers based on a field in-situ spectrum, and relates to the technical field of crop growth monitoring. Collecting soil samples of different plough layers in a field, and obtaining in-situ spectral data of soil; performing indoor analysis on the collected soil sample, and determining the total nitrogen content, the soil texture type and the porosity; classifying the soil samples according to the soil texture types, and respectively establishing correlation models between the total nitrogen contents of the soil with different textures and the in-situ spectral data; introducing a northern eagle optimization algorithm to improve a random forest and a generalized regression neural network to improve the prediction capability of the correlation model on the total nitrogen content of the deep soil; and constructing a comprehensive monitoring model by combining a surface spectrum inversion result and a soil vertical variation equation, so as to realize indirect high-precision estimation of the total nitrogen content of the soil in different plough layers. And a theoretical basis is provided for future soil total nitrogen deep monitoring model research, and a technical support and a theoretical decision are provided for rapid acquisition of deep soil nutrients in 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 discloses a soil oxygen dynamic prediction method for precision agriculture, and belongs to the technical field of soil oxygen prediction, and the method comprises the following steps: S1, collecting key parameters of different soil depths in a research area, and S2, calculating a soil oxygen diffusion coefficient according to the key parameters of different soil depths in the research area; s3, calculating the respiration rate of the soil root system; s4, constructing a soil oxygen consumption model; and S5, carrying out iterative calculation to obtain soil oxygen concentration distribution at different moments and different depths. The method can solve the problem that the oxygen concentration of the soil is influenced by complex factors such as moisture, temperature, porosity, root respiration and microbial activity, and provides a scientific basis for precise agricultural irrigation, soil health assessment and oxygenation irrigation strategy optimization.

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 agriculture, in particular to a large-model-driven agricultural knowledge graph analysis method and system, and aims to realize standardized processing of multi-source heterogeneous data through a three-stage preprocessing process and combine with a field adaptive large-model training technology so as to realize the large-model-driven agricultural knowledge graph analysis method and the large-model-driven agricultural knowledge graph analysis method and the large-model-driven agricultural knowledge graph analysis system. A special system containing 2000 + entity types of crops / diseases / farming operation and the like is constructed. In the entity extraction link, the large model zero sample learning ability is utilized, novel agricultural entities can be automatically recognized, the entity recognition accuracy is improved by 35% compared with a traditional method, and particularly in cross-modal alignment of pest and disease damage images and text description, feature vector Euclidean distance minimization is achieved through a ResNet50-BERT fusion model, and the alignment precision reaches 92% or above. The dynamic updating mechanism captures three core periodicals and policy documents in real time on the basis of web crawlers, the monthly updating frequency of the knowledge graph is improved to four times in combination with an incremental updating algorithm, the timeliness and integrity of agricultural knowledge are ensured, and technical guarantee is provided for precise agricultural data management.

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 belongs to the technical field of agricultural irrigation, and discloses a precision agriculture water and fertilizer integrated distribution method, which comprises the following steps: S1, data acquisition; s2, data processing and analysis; s3, formulating a water and fertilizer formula; s4, water and fertilizer distribution and conveying The method has the beneficial effects that by comprehensively collecting farmland soil, weather and crop growth data, a crop growth and water and fertilizer demand model is constructed, and accurate prediction and scientific formula making of water and fertilizer demands are achieved; by utilizing a drip irrigation and intelligent irrigation control system, water and fertilizer distribution is accurately regulated and controlled, the resource utilization efficiency is remarkably improved, waste is reduced, healthy growth of crops is promoted, and the yield and quality are improved; in addition, due to precise management, use of chemical fertilizers is reduced, environmental pollution is relieved, the ecological environment is protected, the labor intensity of farmers is reduced due to the intelligent management characteristic, and the production efficiency and flexibility are improved.

The invention provides a multi-source data fusion variable fertilization method and device, and belongs to the field of agricultural production technology and agricultural remote sensing application, and the method comprises the following steps: data acquisition and preprocessing: collecting historical data, ground sampling data, ground sensor data and remote sensing images; spatial data processing and consistency correction; variable fertilization model construction: constructing a fertilization amount and yield effect curve, constructing a yield and growth vigor index VI distribution diagram, and further constructing a variable fertilization model representing the relationship between the fertilization amount and the growth vigor index VI; the spatial heterogeneity quantification and zoning comprises the following steps: quantifying the spatial heterogeneity of environmental factors on fertilization requirements by using a geographic detector, and dividing a region into a plurality of sub-regions; and differential fertilization strategy optimization: generating a dynamic adjustment coefficient, and determining a final fertilization amount in combination with the fertilization amount recommended by the variable fertilization model. The crop growth condition can be described more comprehensively and accurately, and accurate agricultural management can be guided.

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 the field of organic fertilizer manufacturing, in particular to a preparation method of a humic acid-containing water-soluble organic fertilizer with chelated medium trace elements for synergism, and the preparation method comprises the following steps: S1, adding modified humic acid into deionized water, heating to 40-50 DEG C, stirring for 25-35 minutes, adding hydrated ferrous sulfate, introducing compressed air, and stirring for reaction for 30-50 minutes to obtain a chelating solution and the like; according to the invention, the environmental adaptability of humic acid is improved through molecular structure modification, a composite chelation system is constructed to enhance element stability, a multistage chelation sequence is optimized to prevent ion antagonism, and a physical barrier is innovated to realize a core process path of intelligent controlled release. The functional limitation of a traditional fertilizer in a complex agricultural ecological system is effectively broken through, the nutrient utilization efficiency and agricultural sustainability are remarkably improved, and the fertilizer has wide application prospects in soil remediation, precision agriculture and resource-saving planting systems.

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.