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

Zohaib Khan, Yue Shen () and Hui Liu
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Zohaib Khan: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Yue Shen: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Hui Liu: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 13, 1-36

Abstract: Object detection is revolutionizing precision agriculture by enabling advanced crop monitoring, weed management, pest detection, and autonomous field operations. This comprehensive review synthesizes object detection methodologies, tracing their evolution from traditional feature-based approaches to cutting-edge deep learning architectures. We analyze key agricultural applications, leveraging datasets like PlantVillage, DeepWeeds, and AgriNet, and introduce a novel framework for evaluating algorithm performance based on mean Average Precision (mAP), inference speed, and computational efficiency. Through a comparative analysis of leading algorithms, including Faster R-CNN, YOLO, and SSD, we identify critical trade-offs and highlight advancements in real-time detection for resource-constrained environments. Persistent challenges, such as environmental variability, limited labeled data, and model generalization, are critically examined, with proposed solutions including multi-modal data fusion and lightweight models for edge deployment. By integrating technical evaluations, meaningful insights, and actionable recommendations, this work bridges technical innovation with practical deployment, paving the way for sustainable, resilient, and productive agricultural systems.

Keywords: object detection; deep learning; crop monitoring; autonomous agricultural robots; agricultural datasets; smart farming (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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