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

Rani Sahu; Priyanka Khanna

doi:10.36948/ijfmr.2025.v07i06.63317

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

Author(s)	Ms. Rani Sahu, Dr. Priyanka Khanna
Country	India
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.
Keywords	Support Vector Machine (SVM), Random Forest, Decision Tree, Naive Bayes, K- Nearest Neighbour (KNN), Classification algorithm and Convolutional Neural Network (CNN), Transfer Learning (TL).
Field	Computer Applications
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-12
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.63317

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

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