Abstract
Recently, autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare, agriculture, industrial automation, etc. Among the interesting applications of autonomous systems, their applicability in agricultural sector becomes significant. Autonomous unmanned aerial vehicles (UAVs) can be used for suitable site-specific weed management (SSWM) to improve crop productivity. In spite of substantial advancements in UAV based data collection systems, automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops. The recently developed deep learning (DL) models have exhibited effective performance in several data classification problems. In this aspect, this paper focuses on the design of autonomous UAVs with decision support system for weed management (AUAV-DSSWM) technique. The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area. Besides, the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages. Moreover, the Adam optimizer with You Only Look Once Object Detector-(YOLOv3) model is applied for the detection of weeds. For the effective classification of weeds and crops, the poor and rich optimization (PRO) algorithm with softmax layer is applied. The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance. A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.