Method for detecting crop disease by using computer vision technology

Abstract

The invention relates to a method for detecting crop diseases by using a computer vision technology, which comprises the following steps: constructing a data set of crop disease images, and dividing the data set into a training set and a test set; constructing a convolutional neural network model for extracting disease image features; constructing a detection model of the diseased leaves; traininga neural network model; evaluating the network performance: testing the detection performance of the network by using the test set, and if an expected result is not reached, continuing to train untila trained network model is obtained; crop disease detection: shooting images of crop leaves by using a camera, inputting the images into the trained network model, and obtaining plant disease categories and position coordinates of disease parts on the leaves at an output end. The method is reasonable in design, lays a foundation for improving crop disease early warning, not only realizes detection and positioning of crop disease parts in a real environment, but also realizes accurate identification of disease types, and is suitable for prevention, control and early warning of crop diseases.

Description

technical field [0001] The invention belongs to the technical field of computer vision, in particular to a method for detecting crop diseases by using computer vision technology. Background technique [0002] Crop disease is one of the main agricultural disasters in my country. It has the characteristics of various types, great impact and frequent outbreaks. It not only causes losses to crop production, but also poses a threat to food safety. Therefore, the diagnosis and identification of crop diseases play an important role in ensuring crop yield and preventing food safety. At the same time, realizing accurate detection of crop diseases and the determination of the degree of disease is the key to the prevention and control of crop diseases. However, identification of diseases requires many professionals to carry out identification analysis and draw conclusions, but most planters have less professional knowledge and experience, which will delay the early prevention and treat...

AI Technical Summary

Problems solved by technology

However, the complex external environment brings great difficulties to the detection of leaf diseases. If the diseased area can be detected and then targeted for identification, a better identification effect can be achieved.