The Use of Machine Learning in Industry 4.0 as an Educational Tool to Address Plant Diseases for Small-Scale Farmers in Developing Countries: A Review Study

Abstract

This review study aims to provide an overview of the current state of research on the use of machine learning techniques for the detection of tomato leaf diseases in the context of climate change in Zambia. Plant diseases pose significant challenges to small-scale farmers in developing countries, impacting crop yields and livelihoods. The emergence of Industry 4.0 technologies, coupled with the power of machine learning, offers promising opportunities to address these challenges and empower farmers with valuable knowledge and tools This review study aims to provide an overview of the use of machine learning in Industry 4.0 as an educational tool specifically tailored to tackle plant diseases for small-scale farmers in developing countries. The study offers insights into the potential of these techniques to enhance disease detection and contribute to sustainable agricultural practices in the face of climate change. Climate change has had significant impacts on agricultural practices worldwide, leading to the emergence and spread of various plant diseases. The study examines existing literature, research articles, and practical implementations to analyze the potential applications of machine learning in plant disease management. The review focuses on four key areas: disease identification, early detection and prediction, knowledge sharing and education, and decision support systems. With further advancements in machine learning techniques and the integration of cutting-edge technologies, the agriculture sector can benefit from improved disease detection and mitigation strategies to ensure food security in the face of climate change.

Plant phenotyping relevance

植物病害を葉の観察に基づいて検出する機械学習手法を中心に整理したレビューであり、植物の病害状態を推定するフェノタイピング手法のレビューに該当する。

Code and data availability

This is a literature review study with no original phenotyping measurements, datasets, images, code, or models of its own. All URLs in the text are citations to prior published works (e.g., PlantVillage dataset paper, cited studies) or generic references, not authors' public assets reproducing this paper's analysis. No

No evidence-backed public reproduction asset is currently recorded.