Comprehensive Study on Machine Learning Enabled Robot for Plant Disease Detection and Recognition

Abstract

Plant diseases are a major problem for global food production because they reduce crop yields and cause big financial losses. Traditional ways of detecting these diseases involve manual checks, which are slow, tiring, and can easily lead to mistakes. This survey looks at how machine learning, especially using Convolutional Neural Networks (CNNs), combined with robots, can make detecting plant diseases faster and more accurate. The review includes various machine learning models and methods used to identify different types of plant infections like bacterial, viral, and fungal diseases. It also discusses how IoT devices and remote sensing can help monitor plants in real time. By automating disease detection, these technologies can give early warnings, reduce the need for chemical treatments, and improve plant health management, leading to more sustainable farming. The paper also talks about the challenges of current models, the importance of having diverse data for training, and how these technologies can be improved and used more widely in the future.

Plant phenotyping relevance

植物病害の画像・機械学習・ロボットによる検出を扱うレビューであり、病害状態という植物表現型の取得手法が中心です。

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