1601 results about "Growing region" patented technology

The present invention relates to the technical field of agricultural irrigation. Disclosed are an intelligent irrigation strategy formulation method and system. The method comprises: determining the current irrigation strategy on the basis of a preset irrigation decision-making model and a Q-learning intelligent agent and according to the current agricultural state; on the basis of the current irrigation strategy, performing irrigation simulation on a target growing region to obtain a corresponding irrigation reward value and the next agricultural state, wherein the irrigation reward value is determined on the basis of the crop yield, the crop water demand, and the annual economic cost after irrigation, the current agricultural state, the current irrigation strategy, the irrigation reward value, and the next agricultural state form an empirical four-tuple, and a plurality of empirical four-tuples form an experience pool; and randomly sampling the empirical four-tuple from the experience pool as a training sample to train the Q-learning intelligent agent so as to obtain an optimal irrigation Q-value table which is used for determining a corresponding optimal irrigation strategy on the basis of any agricultural state of the target growing region. The present invention can efficiently obtain an accurate irrigation strategy.

The invention relates to the technical field of agriculture, in particular to a corn stress resistance environment monitoring and management method, which comprises the following steps of S1, collecting environment data of a corn planting area in real time; s2, transmitting the environment data to a data center through a wireless network; s3, preprocessing the environment data transmitted to the data center; s4, establishing an adversity stress assessment model based on the preprocessed environmental data; s5, evaluating the stress resistance of the corn under different environmental stresses; s6, predicting the type and strength of adversity stress which will occur in the future and the influence of the adversity stress on the corn, and forming stress response prediction; and S7, generating a corn growth management decision suggestion. According to the invention, through real-time environment monitoring and accurate adversity stress assessment, in combination with growth characteristics and weather prediction of corn, irrigation, fertilization and pest control strategies are dynamically adjusted, so that accurate management of corn growth is realized, stress resistance is improved, resource utilization is optimized, and yield is increased.

The invention relates to the technical field of agricultural big data processing, and discloses a winter wheat yield prediction method based on XLSTM and assimilation crop growth model, comprising the following steps: data acquisition and data preprocessing; extracting a winter wheat planting area; performing leaf area index inversion; correlation analysis and feature extraction; establishing a crop growth model based on a WOFOST model; performing data assimilation based on ensemble Kalman filtering; establishing a time sequence data set; establishing a yield prediction model based on the extended long-short-term memory neural network; and model prediction: inputting the current remote sensing image and the meteorological data of the future time period into a yield prediction model, and outputting a yield prediction value of the winter wheat in the target area. According to the method, by integrating and fusing multi-source data and combining complex models and algorithms, the problem that fusion of the multi-source data and the complex relation between the multi-source data and the yield are not fully considered due to the fact that only a single feature is concerned or the models are not fully considered is solved.

The invention discloses an automatic trajectory planning and navigation system of a traditional Chinese medicinal material excavating machine, relates to the technical field of traditional Chinese medicinal material excavating, and aims to solve the problems that the efficiency is low during current angelica sinensis excavating, and angelica sinensis plants are different in size due to different growth environments and characteristics, so that the excavating efficiency is low. During digging, the root hair and stem leaves of the angelica sinensis are easily damaged. Comprising a planting area calibration module used for dividing an angelica sinensis planting area and planting point positions in combination with terrain, an image acquisition module used for acquiring growth images of stems and leaves of angelica sinensis in real time, a planting environment data acquisition module used for acquiring planting environment parameters in real time, and a root hair three-dimensional distribution analysis module used for analyzing the root hair distribution condition of angelica sinensis. According to the method, the digging strategy aiming at the maturity is formulated, so that the digging efficiency and quality of the angelica sinensis are remarkably improved, damage to root hairs, stems and leaves of the angelica sinensis due to improper manual operation is avoided, and the technical problems that the efficiency is low and the angelica sinensis is easily damaged are solved.

The invention provides double-arm picking equipment for shelf-type planted shiitake mushrooms and an operation method thereof. The double-arm picking equipment comprises a moving platform, the upper portion of the moving platform is connected with a chassis, and a lifting device, a shiitake mushroom storage device and a shiitake mushroom picking device are arranged above the chassis. Through mechanical automatic mushroom stick fixing and mushroom picking, intelligent identification, picking and placing of mushroom sticks on a shelf and identification and picking of mushrooms on the mushroom sticks can be carried out at the same time, planting areas can be traversed automatically, the mushroom picking speed is increased, and labor and time cost is saved. Three-dimensional scanning is carried out through the depth camera vision system, the position and size of the mushroom sticks and the maturity of mushrooms can be accurately recognized, and it is ensured that the quality of picked mushrooms is high. The lifting device adjusts the height of the mechanical arm and cooperates with a visual system, picking and mushroom stick processing are more accurate, damage to mushrooms and mushroom sticks is reduced, mushroom picking automation is achieved, operators do not need to put physical labor heavily, and the workload is relieved.

The invention discloses an ecological monitoring method and system for vegetation restoration of degraded grassland, and relates to the technical field of ecological monitoring, and the method comprises the steps: collecting environment data of the degraded grassland, extracting environment features, and calculating the contribution degree of the environment features; establishing a plant type list according to the contribution degree of the environmental characteristics, and optimally selecting plant types and planting proportions from the plant type list to form a plant planting scheme; planning a planting path based on the plant planting scheme for precise planting; and displaying and storing the plant planting scheme and the planting path in a database. According to the method, the relevance and adaptability between plant planting and the environment are greatly improved, the plant survival rate is improved, the plants are adapted by matching the degraded grassland planting area, the plant planting path is planned, and the plant planting efficiency is improved.

The invention relates to the technical field of ecological restoration, in particular to a mine waste dump ecological restoration method which comprises the following steps: dividing a waste dump into planting areas, installing detection equipment in each planting area, and monitoring various indexes of soil and environment in real time; a soil conditioner is customized and produced according to soil physicochemical property analysis; arranging an irrigation system for each planting area, and planting grass seeds and shrubs according to climate and soil conditions; strip-shaped ditches are dug between the adjacent planting areas, and prefabricated buried ecological restoration modules are arranged in the strip-shaped ditches to form planting grooves. By implementing the ecological restoration scheme, the physical and chemical properties of the soil are remarkably improved, the heavy metal content is remarkably reduced and is lower than the national soil environment quality standard limit value, and soil pollution is effectively eliminated. In addition, the proportion of soil aggregate structures is increased from 15% to 30%, the water retention capacity of the soil is remarkably improved, and more suitable environmental conditions are provided for vegetation growth.

The invention discloses a melon vegetable virus disease identification method and system constructed based on a sensor array. The method comprises the following steps: generating a dynamic thermodynamic diagram by collecting insect activity data of a target planting area, constructing a virus disease propagation distribution diagram in combination with an entomophilous propagation probability model, and deploying an initial sensor array according to the virus disease propagation distribution diagram; plant physiological parameters and environmental data are obtained in real time through multiple sensors, a virus disease recognition model fused with entomophilous activity characteristics is constructed, and early diagnosis of diseases is achieved; and evaluating the efficiency of the sensor array based on a recognition result, and dynamically adjusting the array layout by adopting a self-adaptive optimization algorithm to form an optimized network with balanced monitoring precision and resource efficiency. The virus disease detection sensitivity and accuracy are remarkably improved, the monitoring cost is reduced, and a digital solution is provided for prevention and control of diseases of melon crops.

The invention relates to the technical field of agricultural production, and in particular to a sensor-based real-time monitoring system for the growth status of apricot and plum. A plurality of sensors are arranged to collect key growth status data in an apricot and plum planting area in real time. A data processing module is used to process the growth status data. A growth model construction module uses a long short-term memory network to construct an apricot and plum growth status prediction model, and predicts the growth trend, health status and potential risks of the apricot and plum in real time. An abnormality detection and early warning module compares the real-time data with the model prediction result, identifies abnormal situations in time and issues an early warning to a user. An irrigation and fertilization decision module generates an optimal irrigation, fertilization and environmental regulation strategy. A control execution module automatically regulates related equipment. A visualization module displays monitoring data, prediction results, early warning information and decision strategies. The invention significantly improves the economic benefits and sustainability of apricot and plum planting.

The invention relates to the technical field of agricultural datamation, in particular to an agricultural meteorology intelligent decision support method and system combined with multi-source heterogeneous data, and the method comprises the following steps: S1, obtaining and processing the multi-source heterogeneous data: obtaining agricultural multi-source heterogeneous data through official and monitoring equipment, and preprocessing the multi-source heterogeneous data; s2, data extraction and fusion: extracting multi-source heterogeneous data according to relevance; the method integrates meteorological, soil and crop multi-dimensional data through the space-time diagram neural network, combines the dynamic adjacency matrix and the attention mechanism, effectively captures the space-time correlation characteristics of the farmland area, solves the problem of data island of a traditional method, improves the disaster recognition precision, and improves the disaster recognition accuracy through introducing a transfer learning mechanism and a federated learning framework. Rapid migration of a main production area model to a new planting area is achieved, meanwhile, farm node data are encrypted and aggregated through a parameter server, collaborative modeling is completed on the premise of guaranteeing privacy of agricultural enterprises, and the risk of sensitive data leakage is avoided.

The invention relates to the technical field of remote sensing monitoring equipment, and discloses tropical agricultural ecology remote sensing monitoring equipment which comprises an unmanned aerial vehicle platform, a remote sensing unmanned aerial vehicle is arranged on the unmanned aerial vehicle platform, and a monitoring sensor assembly is loaded on the remote sensing unmanned aerial vehicle. The monitoring sensor assembly comprises a high-resolution camera device, a hyperspectral sensor, a thermal infrared sensor and a laser radar, a data receiving module, a data processing module and a display module are arranged in the unmanned aerial vehicle platform, and the monitoring sensor assembly is mainly used for obtaining soil images and data information. The data processing module can judge soil ecological information according to images and data, and the monitoring sensor assembly and the data processing module are matched to obtain the erosion degree and fertility condition of soil in the planting area, the moisture content, the soil type, the moisture evaporation condition and the slope and direction information of the soil. The growth state of plants in a planting area and the ecological environment of soil are monitored, and producers are better assisted in formulating targeted agricultural management measures.

The invention discloses a greenhouse seedling raising environment monitoring control system based on data analysis, relates to the technical field of seedling raising environment control, and solves the technical problems that in the prior art, a greenhouse planting area cannot be reasonably planned, and mixed planting and independent planting cannot be performed for different types of seedlings. Specifically, a seedling overall planning unit marks various types of seedlings as planting main bodies, divides a greenhouse area into a plurality of planting areas, analyzes and matches the planting main bodies to be planted in real time with the planting areas to obtain required environmental factors of the planting main bodies and supply environmental factors of the planting areas, and matches the required environmental factors with the supply environmental factors to obtain a planting main body and a planting main body; if matching is qualified, a matched planting combination is set; otherwise, a non-matched planting combination is set; and performing mixed planting analysis on the matched planting combination, and planning a seedling planting area after the analysis is completed.

The invention provides a prediction and management method and system suitable for pollen multi-dimensional information, and the method comprises the steps: carrying out the statistics of historical item data of a display region, inputting the data into a plurality of first-stage models in an initial prediction model, carrying out the training, obtaining an intermediate prediction result, carrying out the combination, inputting the combined data into a second-stage model, and carrying out the training, and generating a pollen prediction model; receiving a plurality of allergy factors set by the user side, acquiring real-time data of the display area based on the allergy factors, inputting the real-time data into the pollen forecasting model to obtain pollen concentration data, and inputting the pollen concentration data into the pollen health influence model for calculation to obtain a pollen influence logarithm; when the pollen influence logarithm is greater than a preset influence value, generating an initial diffusion region corresponding to each planting region based on an allergen display strategy and the planting region of each allergy factor in the display region; and receiving area data of the display area, adjusting the initial diffusion area to determine an allergen area, generating early warning information based on the real-time position information of the user side and the allergen area, and sending the early warning information to the user side.

The invention discloses a traditional Chinese medicine planting site selection evaluation method, system and device based on big data and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring a traditional Chinese medicine category of to-be-planted traditional Chinese medicine and soil data and meteorological data of a plurality of candidate planting areas; based on the plant growth parameters corresponding to the traditional Chinese medicine categories, establishing a planting area evaluation standard; performing evaluation processing on the soil data and the meteorological data of each candidate planting area according to a planting area evaluation standard to obtain an environmental adaptability index; receiving the multispectral image of each candidate planting area, determining the vegetation coverage and water and soil loss rate of each candidate planting area, and calculating an environmental risk index; and determining corresponding site selection priorities in combination with the environmental adaptability indexes and the environmental risk indexes, and taking the candidate planting area with the highest site selection priority as a target site selection area of the to-be-planted traditional Chinese medicine. By implementing the technical scheme provided by the invention, the accuracy of traditional Chinese medicine planting site selection evaluation can be improved.

The invention discloses a water storage and drainage system capable of keeping slope stability. The water storage and drainage system comprises a water drainage pipe assembly and an automatic water supplementing assembly. The drainage pipe assembly comprises a plurality of transverse assembly type pipes, a plurality of oblique assembly type pipes and a plurality of plastic capillary pipes, the transverse assembly type pipes and the oblique assembly type pipes are perpendicularly communicated in a staggered mode, and the transverse assembly type pipes are communicated with the plastic capillary pipes at the corresponding positions. A sand cushion layer and a perlite layer are laid on the slope surface of the side slope; the automatic water supplementing assembly comprises a water pumping machine, a plurality of water supplementing pipes and a plurality of wireless temperature and humidity sensors. A water outlet of the water pumping machine is connected with the water supplementing pipes through a water conveying pipeline. The water pumping machine is provided with an intelligent automatic sensing system; the water storage and drainage system capable of keeping the stability of the side slope further comprises a plurality of assembly type concrete sash beams laid on the perlite layer, and the water supplementing pipe is connected with the rotating spray heads. A soil dressing layer used for planting cold-resistant water-retaining vegetation is laid in the planting area of the fabricated concrete sash beam. The water content of the side slope can be effectively adjusted and controlled, so that the stability of the side slope is improved.

The invention provides a crop planting environment data acquisition and real-time optimization device integrated with the Internet of Things technology, which comprises a data acquisition module, a data cleaning and feature extraction module, an environment optimization strategy generation module, a decision support and execution module, an intelligent learning and optimization module and a monitoring and data visualization module, relates to the field of communication and data processing. The environment data is collected in real time through the distributed Internet of Things equipment, the planting area is fully covered, the problem of insufficient data acquisition in the prior art is solved, and a reliable data source is provided for subsequent optimization. The real-time performance and comprehensiveness of data acquisition are improved, and the accuracy of basic data optimization is ensured. An adaptive signal enhancement model is introduced, complex interference is effectively identified and eliminated, the intensity of weak signals is enhanced, and the problem that the optimization effect is reduced due to data noise in the prior art is solved. The precision and efficiency of data processing are improved, and high-quality input is provided for an optimization model.

The invention provides an intelligent irrigation control method and system for a high-standard farmland. The method comprises the following steps: acquiring crop canopy image data and farmland terrain elevation data of different crop planting areas in the high-standard farmland; determining a crop growth stage based on morphological characteristic data in the crop canopy image data, and constructing a soil hydrodynamic simulation model according to a root distribution depth interval and a theoretical water absorption rate coefficient corresponding to the crop growth stage; and based on the farmland terrain elevation data and the soil porosity parameter, generating a soil moisture migration parameter by using a soil hydrodynamic simulation model, generating a pipe network pressure regulation and control instruction in combination with the soil moisture migration parameter and a preset irrigation strategy, and adjusting the water outlet pressure distribution of the intelligent irrigation pipe network according to the pipe network pressure regulation and control instruction. Generating an irrigation control instruction; accurate space-time matching of irrigation of the multi-crop rotation area is achieved, the water utilization efficiency is improved, and the problem of irrigation uniformity at the terrain difference and the rotation boundary is solved.

The invention discloses a multi-system coupled agronomic and water-saving measure space optimization configuration method and system, and belongs to the technical field of agricultural production field management. Comprising the following steps: S1, dividing a planting area into M planting sub-areas; s2, constructing a prediction model, wherein the prediction model at least comprises an overall and vertical soil moisture change prediction model and a crop irrigation prediction model; s3, obtaining the temperature and rainfall of each planting sub-region at the future moment; utilizing the integral and vertical soil moisture change prediction model and outputting a prediction result; determining a target planting sub-region based on the prediction result; and calculating the irrigation amount required by the target planting sub-region. According to the invention, the dynamic change of soil moisture in different sub-regions can be predicted more accurately; the irrigation opportunity and the irrigation amount can be accurately judged, and crop growth limitation caused by excessive consumption of water resources or insufficient irrigation is avoided.

The invention discloses a farmland soil data digital analysis method and system, and the method comprises the steps: obtaining a farmland image, and dividing the image into different planting regions according to plants; based on the images of different planting areas, dividing the images into a plurality of sub-areas, obtaining soil samples in each sub-area, and analyzing the samples to obtain soil fertility in the sub-areas; based on the land fertility of different time nodes in each sub-region, obtaining a land fertility change condition; and comprehensively evaluating and analyzing the soil fertility change condition of the planting area based on the soil fertility change condition in each sub-area. The farmland soil fertility monitoring system has the advantages that accurate monitoring and dynamic tracking of farmland soil fertility are realized through image analysis and soil sample collection, and scientific decision support is provided for agricultural production. Through analysis of the soil fertility change trend, optimization of a fertilization scheme is facilitated, the land utilization efficiency is improved, and sustainable agricultural development is promoted.

The invention is suitable for the field of crop fertilization control, and provides a fertilization control method and system based on corn protective farming, and the method comprises the steps: synchronously collecting time-space multi-dimensional data of a target planting region, and constructing a multi-modal data cube; generating a seed fertilizer ratio decision vector; according to the seed-fertilizer ratio decision vector, controlling the multi-channel ratio of mechanical precision seed-fertilizer simultaneous sowing equipment, so as to realize the dynamic ratio and variable fertilization of the seed fertilizer; a multi-spectral image and soil moisture content data are collected through an unmanned aerial vehicle group, and a space-time continuous crop growth monitoring network is constructed; dynamically adjusting the topdressing amount and the water and fertilizer concentration of the unmanned aerial vehicle group based on the growth monitoring network data; according to the method, by constructing a multi-modal data cube, the dynamic proportioning and variable fertilization of seeds and fertilizers are realized, the proportioning precision is improved by utilizing fuzzy PID and machine vision, a space-time monitoring network is constructed by virtue of an unmanned aerial vehicle group, the dynamic adjustment of topdressing is realized, and a data-driven closed-loop fertilization system is formed.

The invention relates to the technical field of inspection unmanned aerial vehicle flight control, and discloses an unmanned aerial vehicle inspection method and system for a tobacco field, and the method comprises the steps: obtaining multi-source remote sensing image data of a preset tobacco planting region; detecting tobacco plants in the remote sensing image data, and performing spatial clustering on detection results to obtain a plurality of tobacco plant cluster targets; and generating an initial inspection path according to the position coordinate and the vegetation index of the tobacco plant cluster target. And in the flight process, the flight pose and environmental parameters of the target unmanned aerial vehicle and the deviation angle between the unmanned aerial vehicle and the current path section are collected in real time. And when the adjustment condition is satisfied, automatically generating an improved path, and adjusting the flight attitude and the camera angle of the target unmanned aerial vehicle. And identifying whether an abnormal condition exists in the image data acquired in the flight process. The tobacco planting scene-oriented inspection method integrating multi-source remote sensing perception, adaptive path planning and intelligent identification is realized.

The invention provides an intelligent agricultural irrigation optimization method and system, and relates to the technical field of agricultural management. The method comprises the following steps: acquiring first irrigation monitoring data of a plurality of sub-irrigation areas in a target planting area, calculating a temperature influence parameter and a humidity influence parameter of each sub-irrigation area, and constructing an irrigation feature vector of each sub-irrigation area; clustering the plurality of sub-irrigation areas to construct a plurality of irrigation area sets, calculating a water demand urgency factor of each irrigation area set and optimizing an initial irrigation scheme to obtain a first target irrigation scheme, and collecting second irrigation monitoring data of the plurality of sub-irrigation areas, and calculating an irrigation gain parameter of each irrigation area set, and optimizing the first target irrigation scheme to generate a second target irrigation scheme. According to the invention, accurate and efficient irrigation water distribution is realized.

The invention relates to the technical field of intelligent agricultural information processing, and provides a crop variety suitable planting area recommendation method and device based on deep reinforcement learning, and the method comprises the steps: obtaining crop variety features, environment data of candidate planting areas, and a pre-trained crop variety suitable planting area recommendation model; the recommendation model is trained through the following mode: training a pre-constructed intelligent agent based on variety planting data in different environments and environment data of planting sites and non-planting sites, and obtaining a trained deep reinforcement learning model. And adjusting the trained deep reinforcement learning model based on the variety promotion condition data to obtain a crop variety suitable planting area recommendation model. The recommendation model can accurately match the crop variety and the planting environment according to the input crop variety characteristics and the environment data of the candidate planting area, and accurate recommendation of the crop variety suitable planting area is realized.

The invention discloses a soil water content estimation method fusing multi-source time sequence remote sensing data, and relates to the field of optical detection, and the method comprises the following steps: 1, obtaining an SAR remote sensing image of Sentinel-1 and an optical remote sensing image of Sentinel-2 in a designated area, carrying out the image fusion through a PCA algorithm, carrying out the feature set extraction of the fused images, and carrying out the feature set extraction of the feature set; a minimum analysis unit is generated through image segmentation, geometric shape features of the minimum analysis unit are extracted, a crop planting area recognition model is constructed through a convolutional neural network algorithm, and a crop coverage area is obtained; 2, introducing an MIMICS microwave scattering model, and constructing a crop multi-growth-period microwave scattering model; the SAR image and the optical image are fused through the PCA algorithm, the crop planting area recognition model based on multi-source remote sensing image fusion and the convolutional neural network algorithm is constructed, and the accuracy of crop planting area recognition is improved by utilizing complementary advantages of remote sensing images.

The invention discloses a rose planting medication scheme decision-making method based on multi-modal feature fusion. The method comprises the following steps: firstly, collecting multi-modal data of a rose planting area, and inputting the obtained multi-modal data into a feature fusion module for multi-modal feature fusion; a double-branch decision network is used for identifying the types of diseases and pests and judging the types and dosage of pesticides, and a pesticide proportioning rule is introduced through a pesticide liquid mixing control module to correct the types and dosage of the pesticides. And finally, through a user interaction optimization mechanism, closed-loop control is achieved, and a medication scheme decision is optimized. According to the method, dynamic closed-loop decision making is realized in combination with user feedback, so that individual requirements of different farmer households are met, and the pesticide application problem caused by data isolation, decision making staticization and the like in traditional rose planting is solved. An artificial intelligence technology and agronomic knowledge are deeply coupled, and traditional agriculture is promoted to be transformed to intelligence and automation.

The invention discloses a radix ranunculi ternati planting monitoring method and a radix ranunculi ternati planting monitoring system based on the Internet, and relates to the technical field of planting management. Environment state parameters and radix ranunculi ternati state parameters in the radix ranunculi ternati planting area are collected in real time through installed environment monitoring Internet of Things equipment, and meanwhile the collected environment state parameters and the collected radix ranunculi ternati state parameters in the radix ranunculi ternati planting area are processed through a data processing method; the method comprises the following steps: processing environment state parameters of environment regulation and control Internet of Things equipment, setting catclaw buttercup root planting standard environment parameters after processing is completed, analyzing the processed environment state parameters and catclaw buttercup root state parameters based on the set standard environment parameters, finally collecting regulation and control parameters of the environment regulation and control Internet of Things equipment and the analyzed environment state parameters and catclaw buttercup root state parameters, predicting the regulation and control parameters in real time, and obtaining the regulation and control parameters of the environment regulation and control Internet of Things equipment. And the operation of the environment regulation and control Internet of Things equipment is controlled based on the regulation and control parameters predicted in real time, so that the real-time performance of planting monitoring of the radix ranunculi ternati is improved.

An integrated water and fertilizer spraying device for modern agriculture, provided with a water and fertilizer irrigation machine (1) localized and assembled on the ground, wherein a primary pipe (2) is mounted on the front side of the water and fertilizer irrigation machine on the outer surface thereof, and secondary pipes (3) are mounted on the sides of the primary pipe on the outer surface thereof. The device comprises clamping assemblies (4), each nested and connected to the upper side of the outer surface of the corresponding secondary pipe, wherein a moving assembly (5) is mounted on the rear side of each clamping assembly on the outer surface thereof. The integrated water and fertilizer spraying device for modern agriculture is provided with the clamping assemblies that facilitate localization of the secondary pipes, thereby improving the use stability of spraying heads assembled with the secondary pipes; the provided clamping assemblies are used in cooperation with protective pads and clips and clamps work in combination, so as to prevent damage of the secondary pipes; integral assembling of the clamping assemblies and the moving assemblies enables efficient movement along a localization rod and a suspension rod, allowing control of the heights of the spraying heads; and by means of a base mounted on the lower side of the localization rod, the spraying head can be localized within a planting area.

A plant illumination method, a plant illumination system, a storage medium and a program product relate to the field of plant illumination, and the method comprises the following steps: determining a target illumination duration, a target spectrum and a target illumination intensity required by a target plant according to a growth stage of the target plant in a target planting area; according to the sunlight illumination intensity of the target planting area, the accumulated duration of the target plant under the effective sunlight illumination is determined; if the accumulated duration is lower than the target illumination duration, determining whether the growth stage is in a preset key growth stage or not; if yes, the photovoltaic panel is set to be in a tracking mode; if not, the photovoltaic panel is set to be in a conventional mode; and when the light supplementing duration exceeds the difference value between the target illumination duration and the accumulated duration, controlling the photovoltaic panel to stop light supplementing. By implementing the method, the illumination efficiency and effect of plant growth are improved.

The invention relates to the technical field of agricultural informatization and intelligent decision making, and discloses a vegetable growth cycle pest control decision making system and method based on a knowledge graph. According to the method, an unstructured agricultural data stream including vegetable varieties, growth stages, environmental parameters, pest and disease records and geographic information is collected from an original database. A data tuple set with a time stamp is generated by performing scanning identification and content extraction on a data stream. Then, clustering is carried out according to the growth stages to which the tuples belong, and stage feature clusters sorted according to the growth cycle are formed; and performing multi-dimensional feature correlation analysis on the stage feature cluster, and establishing a dynamic coupling relationship among the environmental parameters, the disease and pest history and the geographic space features under the growth cycle dimension. And based on the dynamic coupling relationship, generating disease and pest control decision parameters of the vegetables in the specific planting area in each growth cycle stage. According to the invention, accurate matching between the control decision and the crop physiological time sequence and regional characteristics is realized.

The invention discloses a rice yield estimation method based on unmanned aerial vehicle remote sensing and rice spike feature extraction, which comprises the following steps: firstly, assuming a ground section RTK base station and combining an unmanned aerial vehicle remote sensing platform to determine a coverage range and a spatial position of a rice region, and then collecting a rice image and canopy height and performing deblurring processing; then using a rice ear detection model to obtain a rice ear segmentation mask, determining a pixel scaling factor to construct a rice ear feature index, and carrying out statistical analysis and identification on key features to form a feature data set; performing yield estimation by using a rice yield estimation model, and optimizing the model in combination with the actual yield; and finally, transmitting the data to a ground end to generate a thermodynamic diagram and a statistical diagram, and carrying out regional comparison and time sequence trend analysis. According to the method, the influence of the flight height on yield estimation is eliminated based on the rice ear segmentation result in combination with high-precision depth information, and the accuracy and efficiency of rice yield estimation are effectively improved.