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