2389 results about "Farm machine" patented technology

The invention provides a soybean planting fertilization management system, and relates to the technical field of management systems.Soil physicochemical properties, a meteorological microenvironment, a plant growth state and agricultural machinery operation data are collected in real time through a field sensor network and an unmanned aerial vehicle multispectral imaging technology, and a farmland dynamic portrait is constructed; the intelligent decision-making unit uses a deep learning model and a GLCM texture analysis technology to dynamically identify plant nutrient deficiency pathological characteristics, determines a nutrient deficiency type in combination with an element concentration threshold and triggers early warning; the scheme decision module dynamically optimizes the nitrogen-phosphorus-potassium ratio through a reinforcement learning algorithm, generates a field-level fertilization prescription map by using Kriging interpolation, recommends the optimal fertilization opportunity and dosage, and the precise execution module is linked with a variable fertilizer applicator to realize integrated precise application of water and fertilizer. The user interaction module supports remote monitoring and intervention of farmers through a GIS visual interface and a mobile terminal APP, can dynamically adapt to environmental changes and crop requirements, and significantly improves the nutrient utilization efficiency.

The invention provides an automatic driving agricultural machinery marshalling operation control method and system, and relates to the technical field of agricultural machinery control, and the method comprises the steps: determining the function type and number configuration of needed agricultural machinery according to the farming task type, farmland scale and terrain parameters; based on the agricultural machinery operation capability parameter set and the farmland space features, generating agricultural machinery cooperation groups; planning an operation path containing space-time collision avoidance constraint for each agricultural machine group, and establishing a time sequence scheduling strategy matched with the task load; based on farmland environment data, agricultural machinery working condition data and operation quality data which are acquired in real time, agricultural machinery marshalling topology and operation parameters are dynamically reconstructed, through dynamic grouping, intelligent path planning and real-time state feedback and reconstruction, the flexibility, efficiency and robustness of agricultural machinery collaborative operation are remarkably improved, and the development requirements of precision and intelligence of modern agriculture are met.

The invention relates to the technical field of agricultural machinery automation control, and discloses an agricultural machinery operation path intelligent planning method based on Beidou positioning. The method comprises the following steps: firstly, acquiring Beidou positioning data and a farmland geographic data set, and eliminating positioning noise by using a dynamic filtering algorithm to obtain a calibrated agricultural machine track sequence; performing rasterization processing on the farmland geographic data to generate a digital elevation model, and dividing a job priority region; generating an initial operation path by adopting a hybrid optimization algorithm, and dynamically adjusting the path based on real-time operation state information; and finally, a path conflict detection module is combined with obstacle coordinates to calculate a collision probability, and a final agricultural machine operation path is output. According to the method, various factors are comprehensively considered, real-time dynamic adjustment of the path can be realized, path conflicts are effectively avoided, the operation precision and efficiency of agricultural machinery are improved, energy consumption and equipment loss are reduced, and agricultural mechanization and intelligentization development is promoted.

The invention relates to the technical field of intelligent monitoring, and particularly discloses an agricultural machine remote monitoring system based on big data and a block chain technology, and the system carries out the image analysis of an agricultural machine sowing operation image collected by a camera through a computer vision technology based on deep learning. And image similar information among the positions of the agricultural machinery seeding operation image is excavated to represent the seeding uniformity of the agricultural machinery, so that whether the agricultural machinery seeding is uniform or not is intelligently judged. Therefore, remote monitoring and evaluation of the operation quality of the agricultural machine can be realized, and abnormal problems in the operation of the agricultural machine can be found and solved in time, so that the efficiency and quality of agricultural production are improved.

The invention relates to the technical field of agricultural machinery, and discloses a straw returning strip rotary tillage, ridging and pressing device which comprises a lightweight connecting frame serving as an installation foundation of the device and integrally designed to be hollowed out; the laser radar is embedded in the front end of the lightweight connecting frame and is used for scanning the advancing direction and providing topographic data; according to the device, straw returning, rotary tillage, ditching, sowing and pressing are completed at a time, integrated operation is achieved, the time consumed by multiple times of land entering operation of a traditional agricultural machine is shortened, the efficiency is improved by 30%-40%, and through laser radar terrain scanning and sensor feedback, the working efficiency is improved by 30%-40%. The rotary tillage depth, the ditching depth and the pressing pressure are automatically adjusted, the manual intervention time is shortened, terrain self-adaption is achieved, and the device has the advantages of being high in practicability and capable of achieving terrain self-adaption.

The invention discloses a field unstructured road real-time segmentation system and method for agricultural machinery autonomous navigation, particularly relates to the field of agricultural machinery autonomous navigation, and is used for solving the problems of high precision and low delay of real-time segmentation of a driving area and an obstacle in a field unstructured road environment. The image stability under complex illumination is improved through brightness balance, and texture and contour features are extracted through lightweight multi-scale coding; texture deflection and contour focusing are normalized into probability distribution, direction consistency is analyzed based on modal competition fusion, direction embedding is dynamically adjusted, and deviation is avoided; guiding branch activation of a BiSeNet decoder by the direction perception tensor, and outputting a driving area probability graph and an edge heat graph; and continuous frame consistency filtering is used for eliminating instantaneous fluctuation, a stable segmentation mask and a boundary position vector are generated, and the path stability and operation continuity of navigation control are ensured.

The invention discloses an agricultural machine operation track completion and path reconstruction method, system and device and a medium, and belongs to the technical field of intelligent agriculture, the method comprises the steps that multi-source data is collected and preprocessed, and the multi-source data comprises GNSS positioning data, IMU data and agricultural implement image data of an agricultural machine; on the basis of multi-source data, through speed features, IMU signals and image features, agricultural machine operation state identification and confidence scoring are carried out on the original operation track; according to the operation state of the agricultural machine, an effective operation track in the original operation track is reserved, and missing section length identification is carried out on the effective operation track; and on the basis of the confidence of the missing segments and a length selection interpolation complementation mode or a path reconstruction mode, sequentially filling the missing segments of the effective operation track. According to the method, high completeness and high reliability of the agricultural machinery operation track are ensured by identifying the effective operation state and performing differential complementation on the track missing section.

The invention discloses an unmanned agricultural machine path navigation method based on farmland environment perception judgment, and relates to the technical field of path navigation, and the method comprises the steps: continuously perceiving farmland topographic relief features, gradient changes, and agricultural machine current attitude and wheel speed information before and during the operation of an agricultural machine; identifying a terrain disturbance potential risk area based on the sensing data, and predicting the offset and rollover risk value of the terrain fluctuation to the agricultural machine trajectory; according to the method, a pre-correction model fusing terrain prediction data and agricultural machinery dynamic characteristics is called, a pre-correction path containing the terrain compensation amount is generated, and navigation control parameters are adjusted in advance, so that the pre-correction path containing the terrain compensation amount is actively generated by sensing terrain fluctuation and predicting risk and offset in advance; the problem of causal malposition of risk prior to control is avoided from the source.

The invention provides an automatic navigation and positioning method for agricultural equipment, and the method comprises the steps: obtaining the root system residue distribution data of a previous crop and the soil hardening difference, building a correlation model of the crop root system residue distribution and the soil hardening difference, and determining a target tillage depth plan for the sowing of a next crop according to the correlation model; the operation track and depth parameters meeting the soil texture improvement requirements are applied to an automatic navigation and positioning system of agricultural equipment, and the operation process of the agricultural equipment is automatically controlled and dynamically adjusted; and based on the adjusted operation track and depth parameters, analyzing the long-term influence of an automatic navigation and positioning system of agricultural equipment on soil texture improvement in different growing seasons by tracking soil quality indexes, crop yields and root system distribution.

The invention belongs to the technical field of agricultural machinery track classification, and particularly relates to an agricultural machinery track data classification method and device, electronic equipment and a storage medium. The method comprises the following steps: carrying out preprocessing and feature conversion operation on original agricultural machine GNSS trajectory data to obtain continuous and uniform time sequence trajectory data; the original agricultural machine GNSS trajectory data comprises a plurality of discrete trajectory points, and the category of the trajectory points comprises a field trajectory and a road trajectory; a trajectory classification model comprising a feature extraction module and a classification head is constructed, the time sequence trajectory data serve as training samples to train the trajectory classification model, the feature extraction module is trained in a comparative learning mode, and the classification head is trained through cross entropy loss; and deploying the trained trajectory classification model, and classifying agricultural machinery trajectory data by using the trained trajectory classification model. According to the invention, a contrast learning framework fused with the dynamic negative sample queue is designed, the classification accuracy and generalization ability are improved, and the method is suitable for track classification tasks in various complex scenes.

The invention provides a remote sensing image-based in-field agricultural machine driving efficiency suitability evaluation method. The method comprises the steps of S1, extracting fine field attribute information based on a high-resolution remote sensing image; s2, extracting field obstacle information and field terrain information based on the field attribute information; s3, constructing a key evaluation index set influencing the running efficiency of the agricultural machinery in the field; s4, index weighting based on a geographical weighted attention mechanism; s5, establishing an in-field agricultural machine driving efficiency suitability evaluation model, and positioning key negative impact indexes; and S6, grading the suitability level of the agricultural machine driving efficiency based on a natural breakpoint method. The invention further provides a system for evaluating the suitability of the running efficiency of the agricultural machinery in the field based on the remote sensing image. According to the method, the field-level key index set is constructed, the geographical weighted regression and the attention mechanism are innovatively combined, the regional difference and the field characteristics are fully considered, and the scientificity and the practicability of evaluation are greatly improved.

The invention provides an agricultural machinery multi-source data fusion and collaborative operation method based on edge calculation. The method comprises the following steps: step 1, dividing a multi-source sensor group into three units to collect data; 2, constructing a data fusion and collaborative operation model through four parts of space-time calibration, dynamic weight data fusion, a real-time decision engine and multi-agricultural machinery collaborative operation; 3, realizing practical application of the data fusion and collaborative operation model in the step 2 through edge-cloud collaboration; and 4, performing test detection on the data fusion and collaborative operation model. According to the invention, real-time collection and fusion of agricultural machine multi-source data are realized through an edge computing architecture, and the data transmission delay is significantly reduced; the heterogeneous computing architecture design greatly reduces the power consumption of edge nodes, and meets the long-time operation requirement; and the edge-cloud collaboration mechanism supports incremental updating and exception verification of model parameters, so that the reliability and the expandability of the model are ensured.

The invention provides a multi-dimensional data fusion soil fertilizer application system and method, and relates to the technical field of soil fertilizer application, and the system comprises a data collection module which is used for collecting multi-source original data including soil, weather, crops and fertilizer in real time; the data preprocessing module is used for preprocessing the multi-source original data; the data fusion module is used for generating a comprehensive index reflecting the soil fertility state and the crop demand; the fertilization decision module is used for generating an optimal fertilization scheme; the execution control module is used for controlling the variable fertilizer applicator and the drip irrigation equipment to execute precise fertilization operation; and the effect monitoring and feedback module is used for evaluating the decision effect and optimizing the nutrient leaching model. According to the invention, through multi-objective optimization based on the nutrient leaching model and the genetic algorithm, the fertilizer cost, the environmental risk and the agricultural machinery operation constraint are considered while the fertilizer requirement rule of crops is met, and the optimal fertilization scheme considering both the economical efficiency and the ecological property is output.

The invention relates to the field of agricultural fertilization, in particular to a corn straw returning-to-field and fertilization operation path collaborative scheduling optimization method, which comprises the following steps: performing standardization processing on farmland multi-source original data to obtain a field piece unit standardization feature vector; performing imbalance degree analysis on the feature dimension distance component to obtain a feature contribution balance factor; performing activeness evaluation on neighborhood membership distribution of the field units to obtain a spatial membership smoothing factor; obtaining adaptive distance measurement by combining a feature contribution balance factor and a space membership smoothing factor; and performing hardening processing on the final membership matrix to obtain an agricultural machinery path collaborative scheduling instruction set, thereby solving the problems of misalignment of a zoning result, fragmentation of a management boundary and unreasonable agricultural machinery path scheduling in an abnormal straw accumulation region based on a standard FCM clustering algorithm in the prior art.

The invention relates to a navigation control method and device of agricultural machinery, electronic equipment, a medium and the agricultural machinery, and the method comprises the following steps: acquiring a positioning point coordinate, course information, real-time vehicle speed and yaw rate information of the agricultural machinery at the current moment, and calculating the navigation of the agricultural machinery according to the positioning point coordinate, course information, real-time vehicle speed and yaw rate information at the current moment; calculating transverse deviation, course deviation and estimated values of the front wheel turning angle between the agricultural machine and a preset target path point according to the geometrical relationship, calculating an expected value of the front wheel turning angle at the next moment of the current moment according to the transverse deviation, the course deviation and the estimated values of the front wheel turning angle, and calculating the expected value of the front wheel turning angle at the next moment according to the expected value of the front wheel turning angle at the next moment. The agricultural machine is controlled to advance. By means of the method, the expected value of the front wheel rotation angle at the next moment is optimized, so that accurate control over advancing of the agricultural machine is achieved, the path tracking accuracy of the agricultural machine is remarkably improved, deviation errors in the operation process can be effectively reduced, and it is ensured that the agricultural machine accurately travels according to the preset path.

The invention relates to the technical field of steel for agricultural machinery, in particular to low-yield-strength impact-resistant steel for a rotary blade of agricultural machinery and a manufacturing method of the low-yield-strength impact-resistant steel. Cr, Nb, B and Re are used for alloying, meanwhile, the technological parameters such as casting blank heating, rough and finish rolling descaling pressure, the rolling temperature, the reduction rate, the coiling temperature and the cooling speed are controlled, and the prepared steel for the agricultural machine rotary blade is low in yield strength and good in punch forming performance; the yield strength of the agricultural machine rotary blade part produced by using the composite material is 300-400MPa, the tensile strength is 700-800MPa, and the ductility is greater than or equal to 18%; after heat treatment, the yield strength is 1400 MPa or above, the tensile strength is 1900 MPa or above, the ductility is larger than or equal to 10%, the impact energy AKv at the temperature of 20 DEG C is larger than or equal to 25 J, the hardness is 53 HRC or above, the wear-resisting life is prolonged by two times or above, and the service life is obviously prolonged.

The invention belongs to the technical field of electric digital data processing, and more specifically relates to an agricultural machinery behavior identification method fusing interpolation enhancement and depth spatial-temporal feature modeling. The method comprises the following steps: acquiring GNSS trajectory data of an agricultural machine and preprocessing the trajectory data; performing data enhancement on the road points in the data set by adopting a local interpolation method based on DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering; designing a set of spatial distribution feature extraction method; performing feature extraction on the trajectory data after data enhancement by combining motion feature extraction and spatial distribution feature extraction; introducing a variational auto-encoder VAE to carry out potential feature extraction on the initial features; then, a ResBiLSTM network is adopted to carry out space-time modeling; and finally, an end-to-end classification decision is realized through a linear classifier, so that intelligent identification of the operation behavior of the agricultural machine is realized. According to the method, two key problems in the existing track identification technology are solved, namely, data distribution is seriously unbalanced, and the spatial distribution characteristics of track points are often neglected.

A farming machine configured for identifying incorrectly configured plant identification models in farming machines is disclosed. The farming machine includes a control system configured to monitor the performance of a plant identification model to determine if it is appropriately configured for detecting plants in the field. To do so, the control system accesses images of plants in the field and applies a first plant identification model to the images to identify a probability plants in the images are a first class of plants. The control system applies a verification model to determine if plants in the field are the second class of plants (rather than the first) based on the determined probability. The farming machine applies a second plant identification model to the images to identify a second class of plants, and treats plants in the field identified as the second class of plant.

The invention provides a causal algorithm-based plant nutrition management model construction method and system, and the method comprises the steps: collecting the historical planting data of a target plant, and determining and screening a plurality of key factors in plant nutrition management; constructing a causal relationship set among the plurality of key factors based on a causal relationship model; and training and correcting the causal relationship model by using the historical planting data to predict the nutritional status of the target plant under different conditions. According to the method, the model is constructed based on the causal relationship model, so that the optimal proportion and time scheme of plant nutrition is calculated, the fertilization guidance suggestions are generated, a user is helped to avoid the phenomenon that the model generation content does not conform to reality in the planting research process, the model illusion is reduced, and meanwhile the generalization ability of the model in a planting scene is improved. The method can be applied to planting industry model training, improves the accuracy of fertilization guidance, and can be used for agricultural machinery design guidance.

The invention discloses a vision-based agricultural unmanned aerial vehicle terrain following method and device. The method comprises the steps of S1, performing off-line adversarial training on a GAN network; s2, fusing infrared and visible light images obtained in real time by using a trained GAN network generator to enhance terrain information; s3, estimating the crop canopy distance by using the depth estimation model; s4, taking the obtained crop canopy depth map Dp as a constraint of an objective function, and comprehensively considering other constraint conditions to construct the objective function so as to optimize trajectory planning; s5, optimizing the objective function by adopting a genetic algorithm, and taking an optimized curve as a flight path of the unmanned aerial vehicle to realize terrain following flight; the system can be applied to crop monitoring, pesticide spraying and the like, is particularly suitable for agricultural operation in complex terrains such as hills and mountainous regions, improves the production efficiency and the operation quality, and promotes informatization and intelligentization development of agricultural machinery equipment in China.

The invention discloses a fertilization and topdressing intelligent decision-making system and method based on rice open space sensing data. The method comprises the following steps: acquiring agricultural condition data through a multi-source data acquisition module formed by an unmanned aerial vehicle multispectral camera, a ground sensor network and a meteorological station; generating a standardized data set by adopting a space-time fusion algorithm; the CNN-LSTM hybrid model is used for realizing rice growth prediction, disease, pest and weed identification and maturity evaluation; dynamically generating a base fertilizer / topdressing prescription map, a pesticide application scheme and an agricultural machine path plan in combination with an agricultural rule base; and the intelligent agricultural machine is linked through the Internet of Things interface to execute accurate operation. Millimeter-level sensing of the farmland state, variable control over fertilizer application and pesticide application and autonomous execution of agricultural machine operation are achieved, and compared with a traditional mode, the fertilizer utilization rate is increased by 35% or above, the pesticide application amount is reduced by 40%, and the comprehensive yield is increased by 15-20%.

The present invention discloses a self-propelled cyperus chinensis combine harvester, belonging to the field of agricultural machinery technology. The harvester comprises a vehicle body, the front end of which is connected to a digging platform; a conveyor plate chain is provided on the digging platform, the rear end of which is connected to an elevator chain mechanism fixedly connected to the vehicle body; the rear end of the elevator chain mechanism extends into the front portion of a screen drum; a rotatable shaft is provided at the center of the screen drum; a plurality of grass guide blades and a plurality of throw ropes are connected to the shaft; a reciprocating vibrating screen is connected to the vehicle body below the screen drum; the outlet end of the berry elevator is located above a storage bin fixedly connected to the vehicle body; and a plurality of small sieve holes are formed on the collecting plate, the chain belt of the conveyor plate chain, and the chain belt of the elevator chain mechanism. While the space of the self-propelled vehicle body is limited, the present invention includes a five-stage screening mechanism, which has a strong soil screening capacity and can effectively screen out clods of soil carried by cyperus chinensis roots, thereby improving the efficiency and quality of cyperus chinensis harvesting.

The invention provides an unmanned agricultural machine obstacle avoidance operation path optimization method and device in a farmland scene, and relates to the field of automatic driving and path planning, and the method comprises the steps: finding a collision risk strip through detecting whether there is an intersection point between an unmanned agricultural machine operation strip and an obstacle expansion range in a farmland; based on key parameters such as the machine tool size and the minimum turning radius of the agricultural machine, the minimum influence range of the obstacle repulsive force effect is restrained, and it is ensured that the obstacle avoiding path meets the actual obstacle avoiding requirements of the agricultural machine and the mounted machine tool of the agricultural machine in space; local obstacle avoidance path planning is carried out based on an artificial potential field method, the distance relation between sampling points on an operation strip and the center of an obstacle substance is introduced, deflection angle adjustment is carried out on the repulsive force direction in combination with the obstacle influence radius, and therefore potential field distribution is dynamically optimized. According to the method, the operation requirement, the path smoothness, the path continuity and the operation efficiency can be considered, and the application value of the unmanned agricultural machine in the complex environment in the field is improved.

A dynamic wind-resistant flexible photovoltaic power generation mount system, in which multiple photovoltaic panels are mounted astride a same load-bearing cable and connected in series to form a flat-plate-type photovoltaic power generation suspension cable. Each photovoltaic panel can sway with the wind around the load-bearing cable. The present application overcomes the defects of existing static wind-resistant technologies, pioneers a new dynamic wind-resistant approach, reduces wind resistance, and achieves an exceptionally strong wind-resistant effect. The present application allows for an increased span, reduces the number of pile foundations, and minimally interferes with large-scale agricultural machinery operation. The movement speed of shadows is rapid, so that crop photosynthesis is not affected, and instead, crop growth is stimulated and agricultural yield and income are increased.

The invention discloses an unmanned agricultural machine path tracking preset performance control method and system, and the method comprises the steps: collecting the pose information of an unmanned agricultural machine, building an unmanned agricultural machine path tracking error kinematic model, and designing a finite time preset performance function based on the unmanned agricultural machine path tracking error kinematic model. The constraint is converted through nonlinear mapping; based on the Lyapunov stability theory, adopting a backstepping method to recursively design an actual control law, and according to the actual control law, performing inverse transformation on the input of the unmanned agricultural machine tracking control system in the error kinematics model of the unmanned agricultural machine tracking control system to obtain the front wheel steering angle of the unmanned agricultural machine. According to the method, convergence is completed within finite time, and transient control performance and steady control performance of a path tracking control system are considered.

The invention discloses an agricultural machinery operation data anomaly detection method based on an adversarial neural network. The method comprises the steps of data acquisition, preprocessing, LK-GAN network model construction, adversarial training and model optimization, anomaly detection and data transmission and visualization. According to the method, the GAN generator is used for simulating distribution of normal data, and abnormal data are identified through the discriminator; the long LSTM is introduced into the generator and the discriminator to enhance the capturing capability of time sequence features, and the feature extraction capability of the generator is improved in combination with a KAN network and an attention mechanism, so that the anomaly detection precision is improved. Meanwhile, the original data and the detection result are transmitted to the cloud platform in real time and are visually displayed on a client interface, so that the timeliness of anomaly detection is enhanced, and reliable technical support is provided for intelligent agriculture.

The invention discloses an agricultural machinery high-precision automatic driving intelligent control system, which belongs to the technical field of agricultural machinery vehicle automatic driving and comprises a data acquisition module, a path planning module, a control module and an instruction execution module. The data acquisition module is used for acquiring a dynamic data set in real time and transmitting the dynamic data set to the path planning module, and the path planning module is used for receiving and analyzing the dynamic data set and generating an optimal path; the control module is used for synchronously receiving the optimal path, the real-time vehicle body posture dynamic data and the environment change data and generating a control instruction set; the instruction execution module receives the control instruction set, synchronously adjusts the steering angle and the running speed of the agricultural machine, generates actual execution parameters and feeds back the actual execution parameters to the control module. Through arrangement of the control module, the data acquisition module, the path planning module and the instruction execution module, the collision hidden danger caused by insufficient environmental adaptability of a traditional fixed track algorithm is solved.

The invention relates to the technical field of digital agriculture, in particular to a farmer portrait generation method and system based on GIS pattern spots. According to the farmer portrait generation method based on the GIS pattern spots, a farmland plot is locked in real time through a satellite map with the precision of 0.5 m, and farmer production behaviors are collected and analyzed in real time so as to generate a digital farmer portrait. According to the farmer portrait generation method and system based on the GIS pattern spots, decision-making precision can be accurate to a window period of 3 days, meanwhile, early warning of plant diseases and insect pests can be achieved 14 days in advance, the fuel cost of agricultural machinery is reduced, meanwhile, environmental pollution is reduced, supervision efficiency is improved, and great convenience is provided for modern agricultural development.

The invention provides an energy management method, an energy management system and an energy management terminal for a fuel hybrid all-electric drive tractor. And after completion, a generator and a power battery are controlled according to the tractor operation required power and the SOC value of the power battery which are monitored in real time in the whole operation process of the extended-range hybrid power electrically-driven tractor, and cyclic charging and discharging control operation is carried out, so that energy management of the whole operation process of the tractor is carried out. The invention provides an energy management strategy under an all-electric driving framework in which a driving system and a power system are completely decoupled and no mechanical connection is provided aiming at a framework combined by a range extender, a power battery and an all-electric driving system in combination with a vehicle range extending technology and an agricultural machinery application scene; energy management control of the whole process of starting, starting, acceleration, rated power operation and overload operation of the whole vehicle is achieved, and efficient utilization of energy and cyclic charging and discharging control of a power battery can be achieved.

The invention provides a farmland operation path planning method and device, electronic equipment and a storage medium. The method comprises the following steps: firstly, determining node coordinates of each boundary node of a farmland to be operated based on a pre-constructed farmland node map; and then obtaining the operation width of the agricultural machine, and determining the operation starting position of the to-be-operated farmland based on the node coordinates and the operation width. Coordinates of the position of the agricultural machine are obtained, and a moving path from the position of the agricultural machine to the operation starting position is planned; and then planning a full-coverage operation path of the farmland to be operated based on the node coordinates, the operation width and the operation line spacing of the agricultural machine. And finally, combining the moving path and the full-coverage operation path to obtain a final farmland operation path. By means of the method, the moving path from the position of the agricultural machine to the operation starting position and the full-coverage operation path of the farmland to be operated can be planned, so that the automatic operation path of one agricultural machine in one or more gathered farmlands is achieved, and it is ensured that no missing area exists.