Rapid identification method for grapefruit diseases and insect pests

Abstract

A rapid identification method for grapefruit diseases and insect pests comprises the following steps: collecting pictures of various grapefruit diseases and insect pests, adopting LabelImg to label the diseases and insect pests in the pictures according to the types of the diseases and insect pests, and obtaining a training data set and a verification data set; inputting the pictures in the training set and the verification set into a YOLOv5x network model for model training to obtain optimal weight data of the YOLOv5x network model; and loading the optimal weight data into the YOLOv5x network model, inputting a to-be-identified picture, and outputting an identified disease and pest category. According to the method, the grapefruit disease and insect pest pictures capable of covering almost all categories are collected as the data set of the training prediction model, the practicability of target detection is improved, the purposes of rapid identification and prediction are achieved by adopting the YOLOv5 network model, and technical support is provided for prevention of disease and insect pests.

Description

technical field [0001] The invention relates to the technical field of target detection, in particular to a method for quickly identifying grapefruit diseases and insect pests. Background technique [0002] Agricultural pests and diseases are one of the important factors that affect agricultural economic damage. Various pests and diseases have a bad impact on the yield and quality of agricultural products. diagnosis. At present, most growers’ methods of discriminating crop diseases and insect pests stay on the traditional on-the-spot visual inspection, and make judgments based on the appearance and surface morphology of crops combined with personal production experience, which mostly depends on personal experience, with large errors, and it is difficult to judge one by one. screening. Although experts have rich experience in identifying pests and diseases, they often cannot rush to the scene in time, or cannot identify large areas due to limited human resources, which may ...

AI Technical Summary

Problems solved by technology

At present, most growers’ methods of discriminating crop diseases and insect pests stay on the traditional on-the-spot visual inspection, and make judgments based on the appearance and surface morphology of crops combined with personal production experience, which mostly depends on personal experience, with large errors, and it is difficult to judge one by one. screen out

Although experts have rich experience in identifying pests and diseases, they often cannot rush to the scene in time, or cannot identify large areas due to limited human resources, which may easily lead to misjudgments and missed judgments

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