109 results about "Disease damage" patented technology

The invention relates to the technical field of image recognition, and discloses a multispectral image and deep learning-based forest disease and insect pest automatic recognition method, which comprises the following steps of 1, carrying a multispectral camera containing a red edge wave band through an unmanned aerial vehicle to obtain a forest region image; 2, calculating a red edge normalized vegetation index of the image; 3, performing time sequence modeling on the red edge normalized vegetation index data of more than five consecutive periods, and inputting a time sequence convolutional network to generate an early lesion probability graph; 4, detecting a pest and disease damage target by adopting a multi-scale adaptive feature pyramid network; 5, outputting a disease and pest distribution thermodynamic diagram; and 6, driving the unmanned aerial vehicle cluster to execute precise pesticide spraying. According to the method, through the high sensitivity of the red-edge wave band to chlorophyll degradation and in combination with sequential convolutional network dynamic modeling, an initial lesion area can be recognized 7-10 days before disease development, the early disease recognition capability is remarkably improved, the disease discovery period is shortened, and large-scale disease diffusion is effectively avoided.

The invention relates to the technical field of agricultural disease and pest prevention and control, and discloses a big-data-driven disease and pest green prevention and control decision-making method. The method comprises the following steps: collecting multi-source heterogeneous agricultural environment data containing meteorological elements, crop physiological indexes and field biological activity information, and generating a dynamic weight coefficient according to a data source temporal-spatial resolution and a measurement dimension; constructing a disease and pest risk dynamic evaluation model, and calculating disease and pest occurrence probability indexes in different growth stages in different regions based on coefficient fusion data; establishing a green prevention and control measure knowledge base, and matching a candidate prevention and control scheme set according to crop types and growth cycles; in combination with the disease and pest occurrence probability index and the candidate scheme set, the prevention and control effect, the environmental influence and the economic cost are weighed, and an optimal prevention and control strategy sequence is generated; and implementing a dynamic adjustment mechanism, updating a disease and pest occurrence probability index according to real-time farmland feedback data, iteratively correcting an optimal prevention and control strategy sequence, and assisting agricultural green sustainable production.

The invention discloses a production method of distiller's grain fermentation liquid fertilizer specially applied to roxburgh rose. The distiller's grain fermentation liquid fertilizer is prepared bythe step of evenly mixing 50 to 80 parts of distiller's grain fermentation extracting solution, 0.5 to 1 part of microelement, 5 to 10 parts of seaweed meal, 5 to 10 parts of natural active organism,2 to 5 parts of potassium bicarbonate, 2 to 5 parts of natural active organism and 2 to 5 parts of natural surface active agent. A preparation method of the distiller's grain fermentation extracting solution comprises the steps: evenly stirring 50 to 80 parts of distiller's grain, 50 to 150 parts of rice washing water, 1 to 2 parts of EM microflora fermentation liquid and 0.5 to 1 part of sugar, performing composting fermentation at the room temperature to obtain a distiller's grain fermentation material, stirring and extracting through a sodium carbonate water solution, filtering and performing evaporation and condensation on obtained filtrate until the density is 1.1 to 1.2 to obtain distiller's grain fermentation extracting solution. The distiller's grain fermentation liquid fertilizerdisclosed by the invention has a good use effect, meanwhile can effectively prevent pest and disease damage to the roxburgh rose and is high-efficiency environment-friendly liquid fertilizer.

The invention discloses a crop disease and insect pest image recognition algorithm based on dynamic adaptive multispectral fusion Transform, and belongs to the crossing field of agricultural information technology and computer vision. The objective of the invention is to solve the problems of insufficient multi-spectral feature fusion, poor complex background adaptability and insufficient precision in traditional recognition. Acquiring pest and disease damage images of crops in different wave bands (visible light, near-infrared light and the like) to construct a data set; through a dynamic adaptive fusion module, spectral weight distribution is learned in real time based on an attention mechanism, weights are adjusted according to spectral response differences of disease and insect pest areas, and accurate feature aggregation is achieved; the fusion features are input into an improved Transform model, a self-attention mechanism of crop semantic priori knowledge is introduced, focusing of key features of diseases and insect pests is enhanced, and background interference is inhibited; and finally outputting the disease and pest category and confidence. According to the method, through dynamic fusion and Transform cooperation, the recognition accuracy and robustness in a complex scene are improved, support is provided for early warning and prevention of diseases and insect pests, and the application value is remarkable.

The invention discloses a cross-architecture knowledge distillation method based on fruit and vegetable disease and insect pest image classification, and relates to the technical field of fruit and vegetable disease and insect pest image classification. Constructing a teacher network model based on visual Transform and a student network model based on a convolutional neural network; realizing logs distillation by utilizing a cross attention mechanism; intensifying the capture of global and local features by the student model through inter-sample and intra-sample relation distillation; and training the student network based on multi-loss weighted optimization to obtain a classification model with high precision and light weight. According to the method, the global perception ability and the deep semantic knowledge contained in the pre-trained visual Transform teacher model are efficiently migrated to the lightweight convolutional neural network student model, so that the recognition ability and the classification performance of the student model on complex pest and disease damage characteristics are remarkably improved.

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 relates to the technical field of image processing, in particular to an intelligent detection system for field wheat ear diseases and insect pests. The system comprises an image acquisition module which is used for acquiring a field wheat ear image; the illumination analysis module is used for constructing an illumination influence coefficient based on a brightness value distribution condition in a neighborhood pixel block of each pixel point; the gray analysis module is used for determining a feature influence coefficient based on the gray value distribution condition in the neighborhood of each pixel point in the neighborhood pixel block; the image enhancement module is used for adaptively adjusting a preset basic attenuation parameter based on the feature influence coefficient and the illumination influence coefficient, and performing image enhancement on the wheat ear image in combination with a non-local mean filtering algorithm; the disease and insect pest detection module is used for detecting disease and insect pests of the wheat ears through the enhanced image; during denoising, detail reservation of the pest and disease damage area is enhanced, effective denoising is carried out, and the accuracy of subsequent field wheat ear pest and disease damage detection is improved.

The invention relates to the technical field of pest prediction, in particular to a pest occurrence environment threshold prediction method and system based on a knowledge graph, and the method comprises the following steps: obtaining parameters such as air temperature, humidity, illumination and rainfall through a sensor, generating time series data through sliding window standardization, and extracting sensitive factors through K mean value recognition mutation; the equal-frequency box is combined with the behavior log to generate threshold mapping, the random forest analyzes a triggering sequence to construct a causal chain, and the triple is embedded into the map to update edge weight matching to generate a prediction path map. According to the method, environment sequence features are extracted through a sliding window, mutation events are clustered and recognized, pest sensitive factors are screened, five-level threshold values are established in a binning mode, behavior nodes are mapped, a causal chain node sequence is generated through a random forest model, a knowledge graph is constructed through triples, dynamic update weights are embedded, real-time data are matched, and nonlinear association is carried out. The sudden change behavior relationship is identified, and the early warning precision and response efficiency are improved.

The invention discloses an agricultural pest automatic identification method based on Internet of Things and artificial intelligence, and the method comprises the following steps: obtaining image information and environment information of crops through multi-dimensional image collection and environment data fusion collection; utilizing a deep convolutional neural network model and an attention mechanism to extract crop disease and insect pest features, and constructing a comprehensive feature vector; performing similarity matching calculation on the feature vector of the to-be-identified crop and a standard feature vector in a pest and disease damage feature library, and identifying the type and severity of the pest and disease damage; carrying out rationality evaluation and correction on an identification result through cross check and a data check rule; and calling a preset intelligent decision-making system according to the finally determined pest and disease damage type and grade and the growth stage and environmental condition of the crops, and generating a precise prevention and control scheme. The method improves the accuracy and efficiency of pest recognition, is suitable for different crops and environmental conditions, and provides powerful support for agricultural production.

The invention relates to the technical field of agricultural information, in particular to an intelligent crop disease and pest monitoring system based on multispectral imaging and a use method thereof, and the system comprises a multispectral data acquisition and preprocessing module, a visual intelligent analysis module, an intelligent diagnosis module and a decision support module. According to the method, a high-contribution-degree spectral band is dynamically screened through a cross-band attention mechanism, and a 3D convolutional network and mutual information constrained feature decoupling reconstruction algorithm is combined, so that adaptive fusion and noise separation of multispectral features are realized to generate sparse feature representation focusing disease and pest sensitive information, and based on an Inception-v3 and LightGBM cascaded multi-model fusion algorithm, the disease and pest sensitive information is obtained. And in combination with Monte Carlo Dropout confidence evaluation, Grad-CAM interpretability analysis and TCN time convolutional network time sequence prediction, an intelligent diagnosis system with reliability quantification, decision visualization and trend pre-judgment capabilities is constructed, and accurate identification of pest and disease damage types and severity and dynamic prediction of diffusion trends in the next five days are realized.

The invention discloses an AI visual identification-based landscaping pest and disease damage intelligent monitoring and early warning system, and particularly relates to the technical field of AI visual identification pest and disease damage, and the system comprises the steps: obtaining time sequence visual image data, space-time labels and environment sensing data of a target garden area; inputting the time sequence visual image data into a pre-trained disease and insect pest AI identification model, and outputting an identification result; calculating initial occurrence density and local diffusion trend parameters of disease and pest species, and generating a spatial distribution thermodynamic diagram; constructing a multi-factor risk assessment model; dynamically generating a graded early warning signal; according to the invention, the landscaping pest and disease damage intelligent monitoring and early warning system is constructed through the multi-source heterogeneous data acquisition module, the feature extraction and identification module, the data aggregation and space association module, the multi-factor risk assessment and time sequence prediction module and the dynamic early warning and decision support module; the problems of single monitoring dimension, weak data fusion and space association capability, fragmentation of decision support information and the like are solved.

The invention belongs to the technical field of crop disease and insect pest recognition, and particularly discloses a crop disease and insect pest recognition analysis method based on image processing, and the method comprises the steps: collecting a state image of a plant in a dynamic environment, and eliminating the interference of the dynamic environment on the state image recognition of a plant leaf; differentiated analysis is carried out on disease and insect pest characteristics of different areas corresponding to each leaf area, so that leaf disease and insect pest types and defect data thereof are accurately identified; the color value distribution health state of leaves near a leaf vein distribution area is detected by simulating the leaf vein distribution contour of the interference area, the corresponding leaf vein structure stability of each leaf area is analyzed, and the leaf state of the remaining area after the interference area and the pest and disease damage area of the leaves are removed is identified. And assisting in evaluating the leaf risk area condition in each leaf area, and judging the pest and disease damage state in the plant leaf according to the condition. The recognition precision of the crop state image is improved, and targeted prevention and control measures can be taken.

The invention discloses a crop disease and pest identification method and system based on an unmanned aerial vehicle, and belongs to the technical field of image identification, and the method comprises the steps: building an association mapping table based on geographic coordinates, obtaining disease and pest labels obtained through rough identification of each planting collection image, and obtaining a label matrix, performing feature extraction on each planting acquisition image to construct a pest and disease damage matrix; and on the basis of the label matrix and the pest and disease damage matrix, determining a refined sub-region and the recognition precision of the refined sub-region, and according to the coarse recognition result of the non-refined sub-region and the fine recognition result of the refined sub-region, obtaining a pest and disease damage distribution map, and outputting and displaying the pest and disease damage distribution map. And an accurate spatial position and severity basis is provided for agricultural prevention and control.

The invention relates to the technical field of orchard disease and insect pest early warning and prevention resource management, in particular to an orchard disease and insect pest early warning and prevention resource management decision support method based on multi-source data, and the method comprises the steps: firstly obtaining orchard multi-source data, and constructing a space-time fusion data set with a geographic grid as a unit through space-time alignment and standardization processing; inputting the data set into a machine learning multi-classification model, and outputting a risk quantification index of a future specific pest and disease damage; triggering a prevention and treatment decision engine based on an index, and combining a resource knowledge base to generate an optimal prevention and treatment prescription through multi-objective optimization; then, matching prevention and control resource real-time inventory with agricultural machinery space positions, and generating a resource scheduling scheme and agricultural machinery operation path planning; and finally, the integrated information is visualized on an electronic map, a control instruction is issued, and feedback is received to form closed-loop management. The method improves the accuracy of disease and pest early warning, achieves the optimal prevention and control scheme, improves the utilization efficiency of resources and agricultural machinery, and provides support for scientific management of orchard diseases and pests.

The invention provides an unmanned aerial vehicle remote sensing monitoring and evaluating method and system for field orchard diseases and insect pests, and relates to the technical field of orchard disease and insect pest monitoring and evaluating. The interference of complex background noise is effectively eliminated by constructing a ternary separation index integrating feature enhancement, background suppression and brightness normalization; a threshold value is determined based on the exponential distribution, pixels are accurately divided into a non-canopy background, a shadow canopy and an illumination canopy, and fine segmentation of an unstructured scene is achieved; constructing a gain compensation item by using a local illumination reference mean value, correcting a shadow canopy reflectivity curve, and eliminating illumination non-uniform interference; selecting a local high quantile to establish a health reference basis, coupling a discriminant item and an attenuation item, and resolving to obtain a physiological stress index, so as to realize acute capture of early weak disease characteristics; and performing power weighted accumulation on the abnormal pixel difference value, outputting a regional hazard level index, and objectively quantifying the overall disaster risk of the region through nonlinear aggregation.

The invention discloses a pine seedling raising method and device based on temperature gradient and photoperiod coordinated regulation, and the method comprises the following steps: S1, seed source molecular marker-assisted screening: in a pine natural distribution area, collecting healthy excellent tree species with the tree age of more than or equal to 30 years, the fruiting amount interannual variation coefficient of less than 15%, and no pest or disease damage; high-grease seed sources are screened through a molecular marker technology, pretreatment with compound liquid is combined, and temperature-light synergistic regulation and control of three-temperature-zone gradient and precise spectrum are matched, so that the seed germination rate is effectively increased, and the quality stability of the pine is enhanced; a composite frozen soil defense structure is matched with an internet-of-things intelligent heating system, so that targeted protection of freezing injury of pine root systems is realized, and meanwhile, energy consumption is effectively reduced; through accurate temperature and light regulation and control of the closed seedling raising cabin and dynamic management of the ad hoc network, the defect of insufficient accumulated temperature in a cold region is overcome, and the limitation of short growing season is broken through, so that the growth efficiency of the pine is effectively improved.

The invention discloses a supporting device for forest tree pest and disease damage data acquisition, and relates to the technical field of agriculture and forestry, and the supporting device is characterized by comprising a telescopic rod, a fixing plate is arranged on the telescopic rod, an insect pest trapping and killing mechanism is arranged on the fixing plate, and an insect pest data acquisition mechanism is arranged on the insect pest trapping and killing mechanism; a supporting mechanism is arranged at the bottom of the telescopic rod, a clamping mechanism is arranged on the telescopic rod, and the clamping mechanism is clamped on a forest tree and used for improving the stability of the device. The system can adapt to different tree heights, trunk diameters and complex woodland terrains, is matched with solar power supply, realizes stable field long-term operation, can accurately identify plant diseases and insect pests, predicts trends through fusion of meteorological and forest basic data, scientific analysis and an ARIMA model, triggers graded early warning and pushes prevention and treatment suggestions, and has good application prospects. Large-scale and refined forest management requirements are met, a scientific basis is provided for disease and pest control decisions, and the accuracy and efficiency of forest management and protection are improved.

The invention discloses a fruit tree pest recognition method and system based on image recognition in the technical field of image recognition and agricultural pest detection, and the method comprises the steps: 1, collecting an original image, environment illumination intensity and humidity data of the surface of a fruit tree, and correcting the original image; 2, identifying the position of a hole in the original image, performing segmentation extraction on a specific area where the hole is located, performing image preprocessing to obtain a judgment image, and respectively extracting the shape feature of the hole and the color and texture features of powder around the hole; and step 3, judging whether the hole is a worm hole or not, and if so, judging the type of the fruit tree pest and disease damage. According to the method, the accuracy and efficiency of identifying the pests in the tree trunk are improved by constructing the multi-dimensional analysis system of the hole and powder features based on image identification, and technical support is provided for precise pest control.

The invention provides a farmland environment dynamic sensing node supporting early warning of diseases and insect pests, and relates to the technical field of agriculture, and the node comprises an environment sensor module which provides data about a farmland environment; the data acquisition module is connected with the environment sensor module and is used for acquiring farmland environment data in real time; the threshold setting module is used for determining a monitoring threshold of the environmental data according to preset parameters; the early warning processing module is used for carrying out comparative analysis on the environment data and a preset threshold value, and when the environment data exceeds the preset threshold value, the early warning processing module is used for triggering a pest and disease damage early warning signal; and the wireless communication module is used for transmitting data and early warning signals. According to the farmland environment dynamic sensing node supporting disease and pest early warning, real-time monitoring and dynamic analysis based on farmland environment data are realized by using the environment sensor module, the data acquisition module, the threshold value setting module and the early warning processing module, so that a disease and pest early warning signal can be triggered in time when the environment data exceed a set threshold value.

The invention discloses a method for efficiently and greenly cultivating morchella esculenta in a cherry orchard, and belongs to the technical field of edible mushroom cultivation. According to the technology, morchella esculenta interplanting cultivation is carried out in an idle period (May to November) from cherry orchard harvesting to leaf falling; the method comprises the key technologies of cherry root system protection type shallow soil treatment, arched shed type precise sun shading, green soil disinfection of quicklime, high-temperature shed braising and biological agents, compounding of waste with exogenous nutrition bags, pest and disease damage prevention and control through biological agents, mushroom dreg returning-to-field ecological circulation and the like. The technical problems that during interplanting of morchella esculenta on fruit trees, root systems are prone to damage, chemical agent residues exist, the facility cost is high, and the land utilization rate is low are solved. By adopting the method, the Mu yield of the morchella can reach 200-250kg, the production cost is saved by more than 25%, the pesticide residue meets the green food standard, the ecological cycle cultivation of the cherries and the morchella is realized, and the land utilization efficiency and the comprehensive economic benefit are remarkably improved.

The utility model provides an intelligent agricultural pest and disease damage agent mixing device, which relates to the technical field of agricultural production, and comprises a mixing barrel, the upper side of the mixing barrel is provided with a top cover, the upper side of the top cover is provided with a stirring motor, the tail end of the stirring motor is connected with a stirring rod, and the surface of the stirring rod is welded with forward stirring blades. When the device needs to be used, the pump body control switch directly pumps water from the outside through the water supply pump according to a preset liquid medicine proportion, the water flows into the uniform mixing barrel, the proportion of clear water can be better controlled, and the mixing proportion error caused by liquid medicine mixing is avoided; if the water level is low, liquid medicine can be injected through two or more groups of medicine pumps, the medicine proportion can be more accurately controlled, the water level sensor can sense the horizontal height of the current liquid medicine mixture in real time, and if the water level is low, the medicine pumps and the water supply pump can be controlled through the intelligent master controller to continue supplying according to the preset proportion.

The invention discloses an intelligent pest and disease damage collection device and system based on mobile phone photographing recognition, and belongs to the technical field of intelligent pest and disease damage collection, the intelligent pest and disease damage collection device comprises a placement plate, a mobile phone fixing module, a leaf placement module, a supporting and adjusting module and a light supplementing and control module, and the interior of the placement plate is hinged to a supporting piece through a 270-degree storage hinge. The intelligent pest and disease damage collection device and system based on mobile phone photographing recognition solve the problems that professional heavy equipment is difficult to move in complex terrains and cannot collect disease damage in time in the disease damage source position in the using process, can be rapidly unfolded and used in the field edge, ensure that disease damage images are collected in the first time, guarantee recognition timeliness and improve the disease damage collection efficiency. According to the method, monitoring delay caused by equipment limitation is avoided, manual operation differences of shooting angles, object distances, backgrounds and leaf forms are eliminated, high standardization of images in view angle, definition, background consistency and the like is ensured, stable input is provided for rear-end applet recognition, and the recognition accuracy is improved.

The utility model belongs to the technical field of trapping equipment, and particularly relates to forest pest and disease damage intelligent trapping equipment which comprises a control cabinet, a trapper is installed on the top of the control cabinet, two bases are welded to the bottom of the control cabinet, an insertion plate is inserted into one side of each base, and the top of the other side of each insertion plate is welded to the bottom of one side of a side plate. The bottom of the other side of the side plate is symmetrically welded with two groups of threaded lifting feet, and the threaded lifting feet are used for supporting the bottom of the control cabinet when the control cabinet is placed. And then fixing bolts penetrate through the surfaces of the side plates to be fixedly connected with the butt joint plates on the base. In this way, the base inserting plate and the side plate can be fixed to the bottom of the control cabinet, when the control cabinet is used, the control cabinet can be stably placed on the uneven ground by adjusting the length of the threaded lifting feet corresponding to the bottom of the side plate, and therefore the good trapping efficiency of the trapper can be guaranteed.

The invention relates to the technical field of agricultural Internet of Things, in particular to a facility vegetable disease and pest early warning and intelligent prevention and control system based on Internet of Things, which comprises a data acquisition module, a continuous cropping information module, a disease and pest judgment module, an early warning module and an intelligent prevention and control module which are in signal connection with one another, the information acquisition module is used for acquiring vegetable continuous cropping basic information of a target planting plot and transmitting to the pest judgment module; and the pest and disease damage judgment module adopts a pest and disease damage risk assessment model based on continuous cropping characteristics, the pest and disease damage risk assessment model pre-stores a characteristic parameter library of high-incidence pest and disease damage under different vegetable continuous cropping scenes, and the pest and disease damage risk level of the target land parcel is calculated and obtained. According to the method, the continuous cropping information and the multi-dimensional monitoring data are fused, the disease and pest risk assessment model based on continuous cropping characteristics is constructed, accurate early warning and intelligent prevention and control of high-incidence diseases and pests caused by continuous cropping are achieved, and pertinence and effectiveness of disease and pest prevention and control of greenhouse vegetables are improved.

The invention belongs to the technical field of disease and insect pest recognition and detection, and particularly relates to a plant disease and insect pest real-time detection method and device based on computer vision, and the method comprises the steps: obtaining a plant image, and setting the frequency of an obtaining action according to a user demand; constructing a recognition model based on transfer learning; constructing an image preprocessing module, automatically cutting the obtained plant image into a non-plant region image, and performing multispectral feature fusion on the remaining image; inputting the image after multispectral feature fusion into a recognition model, outputting an image set with pest and disease damage features, extracting the image set, and sending a lesion feature region to a cloud end to reduce flow consumption; and automatically updating a local identification model through an incremental learning mechanism, and carrying out plant life cycle management. According to the method, the edge calculation recognition model is deployed in a low-power-consumption embedded mode, only the lesion feature area is uploaded to the cloud after background interference is removed, and good performance is achieved in the aspects of real-time performance and background interference resistance.

The invention discloses a pest and disease damage pesticide spraying device for corn planting, and relates to the technical field of agricultural planting equipment. The vehicle comprises a vehicle body, a pesticide box is arranged in the vehicle body, and the pesticide box is connected with a pesticide pump; the cab is located at the front end of the vehicle body, and a control center is arranged in the cab; the pesticide spraying mechanism comprises a front-row pesticide applying integrated pipe and a rear-row pesticide applying integrated pipe, the front-row pesticide applying integrated pipe and the rear-row pesticide applying integrated pipe are communicated through a connecting pipe, front spraying rods are longitudinally arranged on the two sides of the front-row pesticide applying integrated pipe, the front spraying rods incline downwards, rear spraying rods are longitudinally arranged on the two sides of the rear-row pesticide applying integrated pipe, and the rear spraying rods incline upwards; the front spraying rod and the rear spraying rod are bendable pipes, and spraying heads are arranged at the tail ends of the front spraying rod and the rear spraying rod. By arranging the pesticide spraying mechanism with the front spraying rod inclining downwards and the rear spraying rod inclining upwards, upper and lower leaves of a corn plant can be covered with pesticide at the same time, the problem that a traditional pesticide spraying device is insufficient in covering the middle and upper leaves is solved, and the pesticide spraying uniformity and effectiveness are improved.

The utility model relates to the technical field of drip irrigation and pesticide application, and discloses a crop root drip irrigation and pesticide application system used for evaluating the effects of various pesticides, which comprises a main pipe, one side of the main pipe is fixedly connected with a plurality of branch pipes I, the middle parts of the branch pipes I are provided with communicating pipes, the top ends of the communicating pipes are provided with pressure reducing barrels, and the pressure reducing barrels are connected with the main pipe. An energy absorption assembly is arranged in the pressure reduction barrel, a ball valve is arranged at the output end of the first branch pipe, a second branch pipe is arranged at the output end of the ball valve, and a plurality of capillary pipes are fixedly connected to the surface of the second branch pipe; branch water supply can be achieved through a plurality of second branch pipes, the second branch pipes can conduct drip irrigation on roots of crops through capillary pipes, different kinds of pesticides can be added into the second branch pipes through a pesticide adding assembly, prevention and control of different pesticides can be evaluated, the requirements of plot experiments and field pesticide application for pest and disease damage prevention and control can be met, the flow can be adjusted through a ball valve, and the flow rate can be adjusted. And a specified branch pipe II can be controlled to work.

The invention relates to the technical field of deep learning and crop disease and insect pest diagnosis, in particular to a crop early disease and insect pest rapid diagnosis method based on multi-scale feature fusion, which comprises the following steps: inputting a to-be-diagnosed crop image into a trained disease and insect pest detection model to output a disease and insect pest detection result; in the disease and pest detection model, multi-scale feature extraction is performed on an input crop image through a backbone network, and a multi-scale initial feature map is obtained; performing feature fusion and enhancement on the initial feature maps of the plurality of scales through a neck network to obtain fusion enhanced feature maps of the plurality of scales; and inputting the multi-scale fusion enhanced feature maps into a detection head for target classification and positioning to obtain a bounding box, a category label and a confidence coefficient of a pest and disease damage target. According to the method, the edge features and global and local context information of the early disease and insect pest area are captured, adaptive modeling is performed on the early disease and insect pest area, and the accuracy of early disease and insect pest diagnosis of crops is improved.

The invention relates to the field of disease and insect pest early warning, in particular to an intelligent disease and insect pest early warning system for landscaping plants, which comprises a node number setting module used for determining the number of sensing nodes according to the coupling correlation degree of garden diseases and pests in a target garden area; the node position planning module is used for determining an associated region combination setting node position determined on the basis of interference influence gradient similarity or plant community spatial heterogeneity according to the region state of the target garden region; the node adding module is used for determining whether to add sensing nodes or not according to the disease and pest missing report rate and the disease and pest transition coefficient; and the additional analysis module is used for determining additional nodes based on the feature region or additional nodes based on the key region influence route based on the key region coverage and the angle dispersion coefficient. The method can improve the early warning precision of diseases and insect pests.

The invention belongs to the technical field of electrical automation control, and particularly relates to an electrical automation control system based on the Internet of Things, which comprises an AI-driven crop growth model module and an unmanned aerial vehicle and robot cooperation module, the AI-driven crop growth model is combined with the machine learning and big data analysis technology, the crop growth trend can be intelligently analyzed and predicted according to historical data, real-time environment conditions and crop physiological states, and environment control parameters and farming plans are dynamically adjusted, so that the crop growth environment is always kept in the optimal state, and the crop growth efficiency is improved. The unmanned aerial vehicle and robot cooperation module collects farmland multispectral images through a high-resolution camera and a sensor, early pest and disease damage conditions can be rapidly recognized, an agricultural robot automatically executes precise pesticide application, weeding and picking tasks according to recognition results, the efficiency is improved, the labor cost is reduced, and the labor intensity is relieved. Meanwhile, damage to crops caused by manual operation is avoided.