A Customized CNN for Arecanut Disease Detection
Received: 11 June 2025 | Revised: 30 July 2025 and 22 August 2025 | Accepted: 25 August 2025 | Online: 8 December 2025
Corresponding author: K. Beena
Abstract
Arecanut is an economically important crop in many regions; however, its cultivation is significantly impacted by various diseases that affect different parts of the plant. The timely and precise detection of these diseases is critical for effective management strategies and for maintaining optimal yields. This study presents a customized Convolutional Neural Network (CNN), consisting of five convolutional layers and two fully connected layers, to automatically classify and identify the main diseases in arecanut plants. This study uses a curated dataset comprising images of healthy and diseased plant parts, including the foot, leaf, and nut, which encompass disease categories such as Mahali Koleroga, Stem bleeding, Stem cracking, and yellow leaf disease. Preprocessing techniques such as image resizing and normalization were applied to enhance model robustness and generalization. The proposed CNN model achieved an average classification accuracy of 97.33%, with high precision and recall across all classes, demonstrating its effectiveness in diagnosing arecanut palm diseases, offering a practical tool for agricultural practitioners and researchers by facilitating early disease detection, enabling prompt intervention, mitigating crop losses, and contributing to improved agricultural productivity.
Keywords:
convolutional neural networks, plant disease detection, arecanut, deep learning, agricultural image analysisDownloads
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