A method and system for detecting crop diseases and insect pests based on computer vision
A computer vision, pest and disease technology, applied in computer parts, computing, biological neural network models, etc., can solve the problems of low actual recognition rate, less training data, inconvenient acquisition of hyperspectral data, etc., to improve detection efficiency, improve Crop yield, effect of reducing planting risk
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Embodiment 1
[0054] figure 1 It is a flow chart of crop pest detection method based on computer vision. like figure 1 As shown, the present invention provides a method for detecting crop diseases and insect pests based on computer vision, the method comprises the following steps:
[0055] S1: Obtain the picture of the crop, input the picture of the crop into the pre-classification model, identify the picture of the crop that is affected by diseases and insect pests, and use it as the target picture to be detected;
[0056] S2: Input the target image to be detected into a detection model for detection to obtain a target detection result;
[0057] S3: Output the target detection result, where the target detection result includes the type of pests and diseases and the locations where the pests occur in the target picture.
[0058] Preferably, the pre-classification model adopts a residual neural network model, which combines the IncoptionNet network structure with the ResNet residual block...
Embodiment 2
[0087] figure 2 It is a schematic diagram of a crop pest detection system based on computer vision. like figure 2 As shown, the present invention also provides a computer vision-based crop pest detection system, the system comprising:
[0088] The classification module is used to obtain the picture of the crop, input the picture of the crop into the pre-classification model, identify the picture of the crop with disease and insect damage, and use it as the target picture to be detected;
[0089] a detection module, configured to input the target image to be detected into a detection model for detection to obtain a target detection result;
[0090] The output module is configured to output the target detection result, where the target detection result includes the type of pests and diseases and the location where the pests occur in the target picture.
[0091] Preferably, the pre-classification model adopts a residual neural network model, which combines the IncoptionNet n...
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