Disease detection on coconut tree using golden jackal optimization algorithm

Abstract

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.