A Machine Learning Approach for Robust Plant Disease Prediction in Agriculture Fields

Abstract

Plant diseases are becoming more prevalent, which poses a significant threat to global agricultural profitability and necessitates the development of reliable and effective disease detection technologies. This work employs machine learning (ML) techniques to provide a comprehensive evaluation of prior research on plant leaf disease identification, highlighting the benefits, drawbacks, and practical applications of the various approaches used in the last several studies. The study focuses on a variety of machine learning techniques, including Support Vector Machines (SVM), Decision Trees, Random Forest, K-Nearest Neighbors (KNN), and Naïve Bayes classifiers, which have been widely used for plant disease prediction and classification. Applying these ML algorithms to a range of crop categories, our research shows that plant leaf diseases can be reliably identified and categorized. The study also examines the difficulties and potential for the future in this area, emphasizing the significance of creating complex, real-time monitoring systems to increase the precision of disease detection and promote sustainable agricultural productivity.

Plant phenotyping relevance

植物葉の病害を画像等から識別・分類する機械学習手法を比較・評価するレビューであり、病害状態のフェノタイピング手法が中心です。

Code and data availability

The paper uses secondary data from PlantVillage and Mendeley Data Repository and a VGG16 transfer-learning model, but provides no authors' public code, model checkpoints, or dataset URLs. No qualifying paper-specific public assets are present, and no allowed URLs exist to cite.

No evidence-backed public reproduction asset is currently recorded.