Detecting strawberry diseases and pest infections in the very early stage with an ensemble deep-learning model.

Abstract

Detecting early signs of plant diseases and pests is important to preclude their progress and minimize the damages caused by them. Many methods are developed to catch signs of diseases and pests from plant images with deep learning techniques, however, detecting early signs is still challenging because of the lack of datasets to train subtle changes in plants. To solve these challenges, we built an automatic data acquisition system for the accumulation of a large dataset of plant images and trained an ensemble model to detect targeted plant diseases and pests. After obtaining 13,393 plant image data, our ensemble model shows a decent detection performance with an average of AUPRC 0.81. Also, this data acquisition and the detection process can be applied to other plant anomalies with the collection of additional data.

Plant phenotyping relevance

植物画像から病害・害虫感染の状態を早期検出する自動画像取得システムとアンサンブルモデルを開発しており、植物の状態推定手法が中心である。

Code and data availability

The paper's 13,393 strawberry plant images and polygon annotations are the paper-specific phenotyping assets, but they are not publicly deposited; the data availability statement says they will be shared by the authors upon request. No author analysis code or trained model checkpoints are stated as publicly available.

No evidence-backed public reproduction asset is currently recorded.