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

Abstract

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.

Plant phenotyping relevance

リンゴ葉の病斑を画像分割し、病害の程度（植物の病態）を推定する深層学習手法を開発・評価し、スマートフォンへ実装しているため、植物フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe a self-collected apple leaf disease image dataset (267 images, annotated with Colabeler) and DeepLabV3+ segmentation experiments, but contain no public deposit, availability statement, or URL for the dataset, annotations, code, or trained model. The only URLs present are the article DOI and

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