Open Access

REVIEW

A Systematic Review of Deep Learning-Based Object Detection in Agriculture: Methods, Challenges, and Future Directions

Mukesh Dalal^1,*, Payal Mittal²

1 Department of Electrical & Instrumentation Engineering, Thapar Institute of Engineering & Technology, Patiala, 147004, Punjab, India
2 Department of Computer Science & Engineering, Thapar Institute of Engineering & Technology, Patiala, 147004, Punjab, India

* Corresponding Authors: Mukesh Dalal. Email: ,

Computers, Materials & Continua 2025, 84(1), 57-91. https://doi.org/10.32604/cmc.2025.066056

Received 28 March 2025; Accepted 30 April 2025; Issue published 09 June 2025

Abstract

Deep learning-based object detection has revolutionized various fields, including agriculture. This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years, highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time. The review emphasizes the integration of advanced models, such as You Only Look Once (YOLO) v9, v10, EfficientDet, Transformer-based models, and hybrid frameworks that improve the precision, accuracy, and scalability for crop monitoring and disease detection. The review also highlights benchmark datasets and evaluation metrics. It addresses limitations, like domain adaptation challenges, dataset heterogeneity, and occlusion, while offering insights into prospective research avenues, such as multimodal learning, explainable AI, and federated learning. Furthermore, the main aim of this paper is to serve as a thorough resource guide for scientists, researchers, and stakeholders for implementing deep learning-based object detection methods for the development of intelligent, robust, and sustainable agricultural systems.

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Supplementary Material

Supplementary Material File

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