IAES International Journal of Robotics and Automation (IJRA) Vol. 14, No. 3, September 2025, pp. 407~417 ISSN: 2722-2586, DOI: 10.11591/ijra.v14i3.pp407-417  407 Journal homepage: http://ijra.iaescore.com Disease detection on coconut tree using golden jackal optimization algorithm Arun Ramaiah 1 , Muthusamy Shunmugathammal 2 , Hari Krishna Kalidindi 3 , Anish Pon Yamini Kumareson 4 1 Department of Computer Science and Engineering, P.S.R. Engineering College, Sivakasi, Virudhunagar, Tamilnadu, India 2 Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India 3 Department of Computer Science and Engineering, SRKR Engineering College (A), Bhimavaram, Chinamiram Rural, Andhra Pradesh, India 4 Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India Article Info ABSTRACT Article history: Received Oct 23, 2024 Revised Jun 3, 2025 Accepted Jul 10, 2025 Millions of people depend on coconut palms for their food and livelihoods, making them one of the most essential crops in tropical countries. However, Diseases may significantly reduce the output of coconut trees and possibly result in their death. To overcome this, a novel golden jackal optimized disease detection in COCOnut tree (GOD-COCO) has been proposed for detecting diseases in coconut trees. First, the input dataset images are pre- processed in pre-processing image rotation, image rescaling, and image resizing, and the enhanced images are gathered. The enhanced images are segmented using the PSP-Net. From the segmented images, the features are extracted using the Dense-Net. Then the features needed are selected using the golden jackal optimization algorithm (GJOA). Finally, the deep belief network (DBN) classifier classifies whether it is normal or abnormal. The experimental analysis of the proposed GOD-COC has been evaluated using the Plant Pathology datasets based on the accuracy, precision, and recall standards. By this, the proposed GOD-COCO achieves an accuracy rate of 99.31% and it achieves an overall accuracy rate of 0.77%, 0.31% and 1.17% by the existing methods such as AIE-CTDDC, DL-WDM, and CLS. Similarly, the proposed GOD-COCO model takes less time, 1.13 milliseconds to detect the disease, than the existing methods, which take 3.04, 2.5, and 2.67 milliseconds, respectively. Keywords: Coconut disease Deep belief network Dense-Net Golden jackal optimization Plant pathology datasets PSP-Net This is an open access article under the CC BY-SA license. Corresponding Author: Arun Ramaiah Department of Computer Science and Engineering, P.S.R Engineering College Sivakasi, Virudhunagar, Tamil Nadu, India Email: arun.r@psr.edu.in 1. INTRODUCTION Globally, coconut trees grow extensively and provide a significant source of income for numerous individuals in tropical places. Among various tropical developing countries and other Pacific Island nations, the coconut tree has significant ecological and economic benefits [1], [2]. These coconut trees have suffered from numerous diseases in recent years [3], [4]. The coconut tree is not only gorgeous but also incredibly practical [5], [6]. Many kinds of problems with coconut trees could prevent this tree from growing healthily [7], [8]. Therefore, for a coconut tree to flourish, proper diagnosis and treatment of problems are essential [9], [10]. A variety of pests frequently inflict serious harm to coconut trees [11], [12].