Detection method of apple tree diseases and insect pests based on dnn network and spot detection algorithm

Abstract

The invention discloses a method for detecting diseases and insect pests of apple trees based on a DNN network and a spot detection algorithm, and relates to the technical field of detection of fruit tree diseases and insect pests. And input the data set, update the weight matrix W and the bias parameter b through the forward propagation algorithm and the backward propagation algorithm of the neural network; step 2, use the Gauss pyramid algorithm to scale the collected image and then perform image segmentation, and the image Separate the foreground and background of the image; step 3, perform histogram equalization on the image segmented by the image, and enhance the feature points in the image; step 4, use the LOG algorithm to extract the feature points in the image, and then use the open operation to process , remove the noise, step 5: Input the feature points processed in step 4 into the trained DNN neural network for judgment, and identify whether there are diseases and insect pests on the leaves.

Description

technical field [0001] The invention relates to the technical field of detection of fruit tree diseases and insect pests, in particular to an apple tree disease and insect pest detection method based on a DNN network and a spot detection algorithm. Background technique [0002] my country is the largest apple producer in the world, with apple planting area and output accounting for more than 50% of the world's total. However, there is still a certain gap between the quality of apples in my country and developed countries, and the backward level of pest control is the main factor restricting the development of apples in my country. At this stage, there are two main methods for preventing and controlling apple tree pests and diseases in my country: "control calendar" and a single method for detecting pests and diseases. The control of pests and diseases through the "control calendar" is based on the occurrence of pests and diseases in previous years. It often misses the criti...

AI Technical Summary

Problems solved by technology

Use R-CNN, Tamura and other methods to prevent and control pests. The research is often on the pathological images of apples, which cannot be detected in the early stages of pests and diseases.

Using support vector machines, YOLO and other single detection methods for pest control is to compare the characteristics of the spots on the leaves of apple trees with the typical characteristics of pests and diseases, so as to achieve the purpose of identification, which is often easy to fall into local optimal solutions and gradients. Disappearance and other problems lead to low accuracy and it is difficult to meet the needs of apple tree orchard deployment

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