Object Detection in Agriculture: A Comprehensive Review of Methods, Applications, Challenges, and Future Directions

Object detection has emerged as a transformative technology in precision agriculture, driving significant advancements in crop monitoring, weed management, pest detection, and autonomous field operations. This review provides a comprehensive synthesis of object detection methodologies, tracing their evolution from traditional hand-crafted feature-based approaches to modern deep learning architectures. Key agricultural applications are examined, emphasizing the role of publicly available datasets, including PlantVillage, DeepWeeds, and AgriNet, in catalyzing research progress. A comparative analysis of leading algorithms is presented, evaluating trade-offs among accuracy, inference speed, and computational efficiency within agricultural contexts. Persistent challenges are critically analyzed, including environmental variability, limited labeled data, difficulties in model generalization, real-time processing constraints, and the need for improved interpretability. Emerging research directions are also examined as potential strategies for enhancing object detection in complex agricultural environments. By bridging technical innovation with practical deployment, future object detection systems are positioned to revolutionize agricultural productivity, sustainability, and resilience on a global scale.