Coconut Tree Disease Detection Using the Piecewise Linear Chaotic Map-Based Cuckoo Search Optimization with Convolutional Neural Networks
Received: 26 June 2025 | Revised: 18 August 2025 | Accepted: 30 August 2025 | Online: 8 December 2025
Corresponding author: Kavitha Magadi Gopalakrishna
Abstract
Coconut trees are vital staple crops that are widely grown in coastal regions, contributing significantly to the national economy. However, diverse types of diseases affect coconut tree health, including leaf spot diseases, which affect crop health and productivity. To address these challenges, a Deep Learning (DL)-based detection model, combining a Piecewise Linear Chaotic Map-based Cuckoo Search Optimization (PCSO) algorithm with a Convolutional Neural Network (CNN), referred to as PCSO-CNN, is proposed for coconut tree disease detection. By optimizing the hyperparameters of the CNN, the network effectively learns subtle differences in diseased portions, enabling the PCSO-CNN model to accurately detect coconut tree diseases. The coconut tree images are preprocessed, and then multi-scale features are extracted using the EfficientNet-B7 to enhance disease detection through its fine-tuned architecture. Experimental results show that the proposed PCSO-CNN method achieves an accuracy of 98.53% on the coconut tree disease dataset, which is higher than that of the existing ResNet-50 approach.
Keywords:
coconut tree disease detection, Convolutional Neural Network (CNN), Deep Learning (DL), EfficientNet-B7, Piecewise Linear Chaotic Map-based Cuckoo Search Optimization (PCSO) algorithmDownloads
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