A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based on Computer Vision

Abstract

In this paper, a deep learning enabled object detection model for multi-class plant disease has been proposed based on a state-of-the-art computer vision algorithm. While most existing models are limited to disease detection on a large scale, the current model addresses the accurate detection of fine-grained, multi-scale early disease detection. The proposed model has been improved to optimize for both detection speed and accuracy and applied to multi-class apple plant disease detection in the real environment. The mean average precision (mAP) and F1-score of the detection model reached up to 91.2% and 95.9%, respectively, at a detection rate of 56.9 FPS. The overall detection result demonstrates that the current algorithm significantly outperforms the state-of-the-art detection model with a 9.05% increase in precision and 7.6% increase in F1-score. The proposed model can be employed as an effective and efficient method to detect different apple plant diseases under complex orchard scenarios.

Plant phenotyping relevance

画像ベースでリンゴ植物の病害状態を検出する深層学習手法を開発・評価しており、植物表現型（病害状態）の取得が中心的な研究目的である。

Code and data availability

The paper's custom annotated dataset is only available upon request, but the underlying apple disease images come from the public Kaggle PlantPathology Apple Dataset, which the authors explicitly used to construct their dataset. LabelImg is a generic annotation tool, not a paper-specific asset, and no author analysis代码