Unverified paper record
Plant Disease Detection Using Machine Learning: A Comprehensive Framework and Performance Analysis
INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 5 Nov 2025 · 10.55041/ijsrem53457
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.
Plant phenotyping relevance
植物病害状態を対象に、機械学習・深層学習による自動検出フレームワークを提示し、複数モデルとデータセットで性能評価しているため、病害表現型の取得・判定手法が中心である。
abstractThis research presents a comprehensive machine learning framework for automated plant disease detection, leveraging both traditional machine learning and deep learning approaches.
abstractWe 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.
abstractOur 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.
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.
No evidence-backed public reproduction asset is currently recorded.
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