Combining Deep Features and MSVM Characteristics for Enhanced Classification of Plant Diseases
Received: 18 January 2025 | Revised: 18 April 2025, 23 April 2025, 14 May 2025, and 15 May 2025 | Accepted: 17 May 2025 | Online: 26 May 2025
Corresponding author: Zahraa Jabbar Hussein
Abstract
Plant diseases pose a significant global challenge, threatening food security and agricultural productivity. Accurate and timely diagnosis is essential for effective disease management and crop protection. This study utilized a comprehensive dataset of plant disease images collected from diverse agricultural regions to develop predictive models based on Convolutional Neural Networks (CNNs). The proposed CNN architectures were used to classify images after extracting important characteristics using several algorithms, such as Multi-class Support Vector Machine (MSVM), Decision Tree (DT), Neural Network (NN), and K-Nearest Neighbors (KNN). The results demonstrated that MSVM was the most accurate in identifying diseases that affect plant leaves, underscoring the role of deep learning techniques in rapid and precise disease detection and providing immediate intervention strategies to reduce agricultural losses. Furthermore, these models enable customized treatment recommendations for farmers, optimizing the use of pesticides and fungicides to reduce environmental impacts and enhance the economic sustainability of small farming communities. Experimental findings reveal that the deep learning model combined with MSVM significantly outperformed traditional methods, achieving an accuracy exceeding 99.1%.
Keywords:
CNN, plant disease detection, feature extraction, MSVM, DT, KNN, DLDownloads
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