Deep Learning Driven Automated Red Palm Weevil Detection Using Sparrow Search Optimization

Abstract

In recent decades, Red Palm Weevils (RPW) have been demonstrated as a harmful pest of palm trees worldwide, predominantly in the Middle East. The RPW is produced massive damage to several palm varieties. Primary detection of the RPW is a complex problem to optimum date production while the recognition is avoided by palm trees as to be influenced by RPW. Several studies are driven to determine a precise approach for the detection, localization, and classification of RPW pests. Employing computer vision (CV) technology with pattern detection is verified that further productive once utilized for identifying and classifying insects. Thus, the automated method decreases either the problem or labor effort required for enhancing the farmer's income. The farmers can be stimulated to enhance the productivity of date fruit once this has been done. With this motivation, this article focuses on the design of automated RPW pest detection using sparrow search optimization with deep learning (RPWPD-SSODL) technique. The presented RPWPD-SSODL algorithm mostly focused on the detection and classification of RPW using computer vision approaches. To accomplish this, the RPWPD-SSODL technique employs bilateral filtering (BF) for noise removal. Next, the RPWPD-SSODL technique uses Dense-RefineDet object detector with ShuffleNet model as a backbone network. For improving the recognition solution, the hyperparameter tuning of the ShuffleNet model can be optimally adjusted using the SSO algorithm. To validate the simulation results of the RPWPD-SSODL technique, a wide-ranging simulation outcome is implemented. The simulation values potrayed the improvement of the RPWPD-SSODL algorithm over other approaches under several measures.