Hybrid leader based optimization with deep learning driven weed detection on internet of things enabled smart agriculture environment

Abstract

Recent technological advancements of Cloud Computing (CC), Internet of Things (IoT), Artificial Intelligence (AI), computer vision, etc. enable the transformation of traditional agricultural practices into smart agricultural practices. In this background, the current article introduces a novel Hybrid Leader-based Optimization with DL-driven Weed Detection in IoT-enabled Smart Agriculture (HLBODL-WDSA) model. The prime aim of the proposed HLBODL-WDSA model is to collect the images using IoT devices and recognize the weeds automatically. Initially, the HLBODL-WDSA model enables the IoT devices to capture the farm images and transmits the images to the cloud server for examination. Next, the HLBODL-WDSA model applies YOLO-v5-based weed detection process in which HLBO algorithm is exploited as a hyperparameter optimizer. Finally, the Kernel Extreme Learning Machine (KELM) model is applied for effective classification of the weeds. The proposed HLBODL-WDSA model was experimentally validated and the outcomes established the better performance of the proposed HLBODL-WDSA model over recent approaches.