Design and Implementation of Coconut Eriophyid Plant Disease Detection Using Mask-RCNN

Abstract

One of the most significant perennial tropical crops is the coconut palm (Cocos nucifera L). The coconut plantation sector relies on specialist guidance to diagnose and control diseases. Plant disease detection also uses spectroscopic techniques such as Ultraviolet-Visible (UV) spectroscopy, mass spectrometry, and nuclear resonance spectroscopy. Deep learning (DL) technology in computer vision has opened up a new path for finding agriculture solutions. Plantations of coconuts are seriously threatened by coconut eriophyid infections, which can result in severe output reductions. For prompt intervention and efficient disease treatment, accurate disease identification is essential. In this article, we offer a unique method for identifying the coconut Eriophyid plant disease, which combines Variational Autoencoders (VAEs) for noise reduction with Mask Region-based Convolutional Neural Network (Mask R-CNN) for accurate disease segmentation. The developed trained model’s performance was examined.