Journal Article

Diagnosis and Mobile Application of Apple Leaf Disease Degree Based on a Small-Sample Dataset

Li, Lili, Yang, Hua, Wang, Bin, Li, Yanwen

2023

Overview

The accurate segmentation of apple leaf disease spots is the key to identifying the classification of apple leaf diseases and disease severity. Therefore, a DeepLabV3+ semantic segmentation network model with an actors spatial pyramid pool module (ASPP) was proposed to achieve effective extraction of apple leaf lesion features and to improve the apple leaf disease recognition and disease severity diagnosis compared with the classical semantic segmentation network models PSPNet and GCNet. In addition, the effects of the learning rate, optimizer, and backbone network on the performance of the DeepLabV3+ network model with the best performance were analyzed. The experimental results show that the mean pixel accuracy (MPA) and mean intersection over union (MIoU) of the model reached 97.26% and 83.85%, respectively. After being deployed into the smartphone platform, the detection time of the detection system was 9s per image for the portable and intelligent diagnostics of apple leaf diseases. The transfer learning method provided the possibility of quickly acquiring a high-performance model under the condition of small datasets. The research results can provide a precise guide for the prevention and precise control of apple diseases in fields.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Apple / apple leaf / Apples / Applications programs / Comparative analysis / complex environment / Computer networks / data collection / Datasets / Deep learning / Detectors / Diagnosis / diagnostic techniques / disease severity / Diseases and pests / Electronic data processing / Feature extraction / foliar diseases / Fruits / Identification and classification / Image segmentation / Learning / Leaves / Methods / Mobile applications / Mobile computing / mobile phone recognition system / mobile telephones / Neural networks / Plant diseases / Research methodology / Semantic segmentation / Semantics / small sample dataset / Smartphones / Telecommunication systems / Transfer learning / Wireless telephone software