A High Performance Wheat Disease Detection Based on Position Information.

Abstract

Protecting wheat yield is a top priority in agricultural production, and one of the important measures to preserve yield is the control of wheat diseases. With the maturity of computer vision technology, more possibilities have been provided to achieve plant disease detection. In this study, we propose the position attention block, which can effectively extract the position information from the feature map and construct the attention map to improve the feature extraction ability of the model for the region of interest. For training, we use transfer learning to improve the training speed of the model. In the experiment, ResNet built on positional attention blocks achieves 96.4% accuracy, which is much higher compared to other comparable models. Afterward, we optimized the undesirable detection class and validated its generalization performance on an open-source dataset.

Plant phenotyping relevance

小麦病害の画像検出モデルを開発し、精度比較とオープンデータセットでの汎化検証を行っており、植物病害状態の表現型取得が中心である。

Code and data availability

The article describes a custom wheat disease image dataset (2626 images) and a PyTorch position attention block, but contains no public deposit, availability statement, or authors' URL for the dataset, images, code, or trained models. The only URL present is the CC BY license notice, which is not a paper-specific asset

No evidence-backed public reproduction asset is currently recorded.