STUDY ON RAPID DETECTION AND IDENTIFICATION OF MULTI CATEGORY APPLE LEAF DISEASE-Reference-Cited by-同舟云学术

STUDY ON RAPID DETECTION AND IDENTIFICATION OF MULTI CATEGORY APPLE LEAF DISEASE

Published:2022-08-31 Issue: Volume: Page:67-76
ISSN:2068-2239
Container-title:INMATEH Agricultural Engineering
language:en
Short-container-title:INMATEH

Author:

JIA Zongwei¹,HAO Jing¹,HOU Yiming²,WANG Ruibin¹,ZHANG Ruyi¹,YAO Simin¹

Affiliation:

1. College of Information Science and Engineering, Shanxi Agricultural University, Taigu / China

2. School of hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang / China

Abstract

Apple planting process is often accompanied by the impact of a variety of diseases. A single apple leaf often presents the situation of multiple diseases occurring at the same time, which brings great challenges to fruit farmers' rapid diagnosis and correct control. In this paper, aiming at the rapid detection and recognition of multi-category apple leaf disease, a multi-target detection model is constructed to realize the rapid detection and recognition of single leaf and multi leaf, single disease and multi disease. Through the technical means of manual labeling, data enhancement and parameter optimization, Yolo v4, SSD and Efficientdet are selected to train and evaluate the apple leaf disease data set. The results show that the target detection model based on Yolo v4 achieves better training effect, and its mAP value is 83.34%. The model can meet the needs of rapid disease spot detection and recognition of single leaf single disease and multi leaf multi disease in natural environment.

Publisher

INMA Bucharest-Romania

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,Food Science

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