International Journal of Advanced Research in Computer and Communication Engineering

Abstract: As a reply to the tremendous complexity and extent of modern agricultural weed control, a statistical analytical review is being presented in this study of all modern scholarly contributions in the area of deep learning (DL), optimization algorithms, ecological control systems, and biochemical valorisation approaches. Its main objective is to bundle these disparate methods into a single benchmarking framework on six performance criteria: Weed Detection Accuracy, Precision, Reliability, Time Complexity, Memory Complexity, and Makespan Sets. Therefore, methodologies such as DC-YOLO, Efficient Net-based transfer learning, RSA-enhanced YOLOv3, SCR-DETR, and RCNN are compared in this review with approaches already using microbial weed suppression models, anaerobic bioreactors, and graph-based simulation techniques. Each of the above approaches is being evaluated both qualitatively and quantitatively while data were then gleaned from reported empirical findings or approximated through interpolation of cross-study performance. In the comparative matrix established through the dataset, detection efficiency, scalability, resource requirements, and robustness are displayed across a wide range of use cases; from UAV surveillance, thermal classifications, to mechanical weeding. All vision-based DL approaches score detection accuracies well above 93%, but the cost is increased memory usage and time complexity incurred when the complexity of the scene increases in process. Thus, this review identifies research gaps in data fusion, model interpretability, and real-world deployment, while proposing future integration pathways such as eco-AI coupling and secure visual processing for different scenarios. This work lays the groundwork for precision agriculture as an analytical resource in weed control events at some point from future technological applications in robust adaptive and environmentally sustainable weed control treatments in process.

Keywords: Weed Detection, Deep Learning, Precision Agriculture, Performance Benchmarking, Image Segmentation, Scenarios.

Downloads: | DOI: 10.17148/IJARCCE.2025.14935