Authors

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.

Abstract in Chinese

苹果种植过程常伴随多种病害的影响，单一苹果叶片经常呈现多种病害同时发作的情形，为果农快速诊断和正确防治带来极大地挑战。本文以多类别苹果叶病快速检测与识别为目标，构建多种目标检测模型，实现了对单叶片和多叶片、单病害和多病害的快速检测识别。通过对病斑图像数据手工标注、数据增强、参数优化设置等技术手段，选用Yolo v4、SSD及Efficientdet三种目标检测预训练模型，对苹果叶病数据集进行训练评估。结果表明，基于Yolo v4的目标检测模型取得了更好的训练效果，其mAP值为83.34%，模型可以满足单叶片单病害和自然环境下多叶片多病害的快速病斑检测识别需求。