Family-based Plant Disease Characterization using Deep Neural Networks

Abstract

Over the years, researchers have applied various deep learning techniques to automatically recognise plant diseases from both raster and spectral images. The primary focus of the existing studies is developing individual species-specific or disease-specific models, where the former recognises diseases of single crop type and the latter recognises single diseases of single or multiple crop types. Building one global model to recognise diseases of multiple crops has also been widely explored, where a class is treated as a crop-disease combination. While training individual species-specific or disease-specific deep models is labour-intensive, embracing a vast number of crop species and inherent diseases present on this planet makes the model cumbersome. In order to address this problem, a more intuitive and feasible family-based plant disease characterisation approach with botanical reasoning is proposed in this study. This approach demonstrates the feasibility of six state-of-the-art deep neural networks through a set of extensive experiments incorporating six key strategies. The results on a newly built family-based plant disease dataset confirm that the proposed novel approach is convincing to be applied in a plant family-based disease recognition problem. Further, this study creates future opportunities for more intuitive plant disease data collection and benchmark classification model development.

Plant phenotyping relevance

植物画像から病害を認識・特徴付ける深層学習手法を提案し、データセット構築と複数モデルによる検証を行っており、病害状態の表現型取得・分類が中心である。

Code and data availability

The paper's plant disease image analysis is built from the publicly available PlantVillage dataset, which the authors explicitly state is accessible via a Mendeley Data URL in the Data availability statement. This is the image dataset used for the paper's phenotyping measurements. The referenced GitHub repositories (ml