Attention-Based Recurrent Neural Network for Plant Disease Classification.

Abstract

Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.

Plant phenotyping relevance

植物病害の感染領域をRGB画像から自動定位し、病徴に基づく分類を行うRNN手法の開発・比較検証が中心であり、植物の病害状態を直接推定するため、方法論文として収録する。

Code and data availability

The paper's experiments use public plant disease image datasets (PlantVillage; IPM/Bing collections from Mohanty et al. 2016) explicitly linked in the data availability statement, and the CNN feature extractor used in the analysis is a public GitHub repository (footnote 2). The authors' own RNN source code is only a 'T