Plant Disease Detection Method Based on Computer Vision Technology

  • Yao Cheng
Keywords: Image Processing, Disease Recognition, Feature Extraction, Computer Vision


Aiming at the basic problems that plague crop growth in traditional agriculture, a method of identifying weeds
using machine vision and applying chemical agents with selective variables was proposed. Collected visible
light images of plant diseases, pre-processed the images, segmented the images using an instruction value
composed of R, G, and B color components as a threshold, and wrote an algorithm to misjudge the background
image after segmentation as a background. Pixels were used for information recovery; according to the analysis
of the change in color characteristics after the occurrence of lesions, sample lesions were extracted using the two
color features of G / R and G / B; the results of the damage degree of diseased leaves measured using image
processing technology The analysis was performed and compared with the results of the plant disease degree
determined by the paper card method in the traditional classification standard. The experiments show that the
selected 7 characteristic parameters are used as the input of the neural network, and the number of types of
cucumber leaf diseases that need to be identified is used as the output to build a BP neural network model. By
adjusting various parameters in the BP neural network, the parameters with the best recognition effect are
selected to train the network. The trained network is used to identify the plant disease image. As a result, the
disease can be identified well, and the recognition rate is 93.5%.