Automated Red Palm Weevil Detection using Gorilla Troops Optimizer with Deep Learning Model

Red palm weevil (RPW) is a pest that can cause severe damage to plantations and affects palm trees. Classical approaches to detection depend on visual analysis, which is inaccurate and time-consuming. Hence, deep learning techniques emerge as a potential solution used for automating the process of detection and presenting efficient and precise results. The initial detection of the RPW remains a difficult task for good production as the identification will protect palm trees infected from the RPW. So advanced technologies like artificial intelligence (AI) and computer vision (CV) can be used in preventing the spread of the RPW on palm trees. Various scholars still working on identifying a precise method for the classification, identification, and localization of the RPW pest. This article develops an automated Red Palm Weevil Detection using Gorilla Troops Optimizer with Deep Learning (RPWD-GTODL) method. The goal of the presented RPWD-GTODL approach lies in the accurate detection and localization of the RPW effectually. To accomplish this, the presented RPWD-GTODL technique initially uses the Gabor filtering (GF) technique to pre-process the images. For RPW detection, the RPWD-GTODL technique uses a Mask RCNN object detector with MobileNetv2 as a backbone network. Moreover, the detection performance of the RPWD-GTODL technique can be boosted by the design of the GTO algorithm for the hyperparameter selection of the MobileNetv2 model. The performance validation of the RPWD-GTODL technique is tested using the RPW dataset and the results demonstrate the enhanced performance on RPW detection process with maximum accuracy of 99.27%.