Deep Learning Techniques for Weed Detection in Agricultural Environments: A Comprehensive Review

Abstract

Agriculture has been completely transformed by Deep Learning (DL) techniques, which allow for quick object localization and detection. However, because weeds and crops are similar in color, form, and texture, weed detection and categorization can be difficult. Advantages in object detection, recognition, and image classification can be obtained with deep learning (DL), a vital aspect of machine learning (ML). Because crops and weeds are similar, ML techniques have difficulty extracting and choosing distinguishing traits. This literature review demonstrates the potential of various DL methods for crop weed identification, localization, and classification. This research work investigates the present status of Deep Learning based weed identification and categorization systems. The majority of research employs supervised learning strategies, polishing pre-trained models on sizable, labeled datasets to achieve high accuracy. Innovations are driven by the need for sustainable weed management methods, and deep learning is demonstrating encouraging outcomes in image-based weed detection systems. To solve issues like resource scarcity, population increase, and climate change, precision agriculture holds great promise for the integration of AI with IoT-enabled farm equipment.