Advances in Weed Detection: A Comparative Review of Deep Learning and Sensor-Based Methodologies

Abstract

Weed detection remains a critical task in the domain of precision agriculture, directly influencing crop yield optimization, resource management, and the reduction of chemical herbicide usage. Accurate and timely identification of weeds enables site-specific weed management, minimizing environmental impact and enhancing overall agricultural productivity. With recent advancements in deep learning, computer vision, and sensor technologies, a wide range of automated weed detection methods have emerged, offering promising alternatives to traditional manual or chemical-based approaches. This review paper provides a comprehensive analysis of state-of-the-art techniques used for weed detection, focusing primarily on Convolutional Neural Networks (CNNs), object detection models such as YOLO (You Only Look Once) and Faster R-CNN, semantic segmentation frameworks like U-Net and DeepLab, and various sensor-based approaches including multispectral and hyperspectral imaging. Each method is evaluated based on critical performance indicators such as mean Average Precision (mAP), recall, precision, F1 score, and computational efficiency, with an emphasis on real-world applicability. The review also highlights major challenges encountered in the development and deployment of these systems, including variability in environmental conditions (lighting, occlusion, background clutter), the generalization capability of trained models across different crop and weed species, and the feasibility of real-time field implementation. In addition, a comparative analysis is presented to synthesize the strengths and limitations of different approaches. Based on the insights gained, the paper outlines several future research directions, including the integration of multimodal data, transfer learning, few-shot learning, and edge computing, to further improve the robustness and scalability of weed detection systems in precision agriculture.