An advanced deep learning models-based plant disease detection: A review of recent research

Abstract

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

Plant phenotyping relevance

植物病害を画像と機械学習・深層学習で検出する手法を中心にレビューしており、感染植物の状態・病害を観察から推定するフェノタイピング手法レビューに該当する。

Code and data availability

This is a review article on deep learning for plant disease detection. The supplied blocks contain no paper-specific phenotype datasets, images, author code, models, or supplements with availability statements; only generic framework descriptions (TensorFlow, PyTorch, Caffe, Theano) and citations of prior work are made

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