Plant Disease Detection Using Machine Learning: A Comprehensive Framework and Performance Analysis

Abstract

Abstract - The global agricultural sector faces significant challenges due to plant diseases that threaten food security and sustainable agriculture. Traditional methods of disease detection are often labour-intensive, time-consuming, and require specialized expertise. This research presents a comprehensive machine learning framework for automated plant disease detection, leveraging both traditional machine learning and deep learning approaches. We implemented and evaluated multiple models including VGG19, Inception v3, Support Vector Machines (SVM), and k-Nearest Neighbors (kNN) on four distinct datasets: Banana Leaf, Custard Apple Leaf and Fruit, Fig Leaf, and Potato Leaf. Our experimental results demonstrate remarkable performance variations across different crops, with the highest achievement of 99.1% accuracy using VGG19 with kNN on the Custard Apple dataset, while the Potato Leaf dataset presented the greatest challenges with 62.6% accuracy using Inception v3 with SVM. The study provides valuable insights into model selection for specific agricultural applications and highlights the importance of customized solutions based on crop-specific characteristics. We also address critical challenges including dataset limitations, computational requirements, and implementation barriers in real-world agricultural settings. Keywords - Plant disease detection, machine learning, deep learning, convolutional neural networks, agricultural technology, precision agriculture.

Code and data availability

The paper describes four plant disease image datasets (Banana Leaf, Custard Apple, Fig Leaf, Potato Leaf) and TensorFlow/Keras experiments, but provides no public dataset URLs, no code/model availability statements, and no supplementary deposits. No paper-specific public asset is actionable.

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