Human-In-The-Loop AI for Precision Agriculture Scoping Review

This scoping study explores the role of Human-in-the-Loop Artificial Intelligence (HITL AI) in precision agriculture and evaluates the benefits of using human expertise in combination with Artificial Intelligence (AI) systems in decision-making within modern smart agricultural environments. The development of Artificial Intelligence, Machine Learning, Internet of Things, and robotics has significantly impacted modern agriculture by providing automated crop monitoring, disease detection, yield prediction, and smart farm management systems. However, Artificial Intelligence systems also face challenges in terms of understanding, interpretability, flexibility, and trustworthiness in modern smart agricultural environments. This study is based on the literature regarding human-in-the-loop systems, human-centric Artificial Intelligence systems, and collaborative robotics systems in the context of smart agriculture. The structured scoping study methodology has been followed to identify and evaluate studies regarding Artificial Intelligence systems in smart agricultural environments, with a focus on automation-centric Artificial Intelligence systems and human-centric Artificial Intelligence systems within the context of Agriculture 5.0 concepts. The study concludes that although automation-centric AI systems show high accuracy in simulated smart agricultural environments, Human-In-The-Loop (HITL) AI systems show higher robustness in smart agricultural environments. Explainability in AI has shown significant potential in supporting the effectiveness of HITL AI systems in decision-making within smart agricultural environments. The study also identifies some important gaps in the literature regarding HITL AI systems in smart agriculture. The study concludes that for the development of modern smart precision agriculture, collaborative intelligence within smart agricultural environments is necessary to create sustainable smart agriculture systems.

Human-in-the-Loop AI; Precision Agriculture; Explainable Artificial Intelligence; Smart Farming; Agriculture 5.0

1. Adve, S., et al. (2025). Advancing AI in agriculture through large-scale collaborative research. Communications of the ACM. [Google Scholar] [Crossref]

2. Arafat, M. (2025). An explainable human-AI-in-the-loop framework for segmentation-aware hyperspectral image classification. International Journal of Web and Open Systems. [Google Scholar] [Crossref]

3. Benos, L., et al. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758 [Google Scholar] [Crossref]

4. Benos, L., et al. (2025). Explainable AI-enhanced human activity recognition for human–robot collaboration in agriculture. Applied Sciences, 15, 650. https://doi.org/10.3390/app15020650 [Google Scholar] [Crossref]

5. Deka, G., et al. (2025). Enhancing precision agriculture through human-in-the-loop planning and control. Proceedings of the IEEE Conference on Automation Science and Engineering (CASE 2024). https://doi.org/10.1109/CASE59546.2024.10711319 [Google Scholar] [Crossref]

6. Dolatabadian, A., et al. (2025). Image-based crop disease detection using machine learning. Plant Pathology. [Google Scholar] [Crossref]

7. https://doi.org/10.1111/ppa.14006 [Google Scholar] [Crossref]

8. Emon, M., et al. (2025). Integration of IoT, machine learning, and sensors for intelligent environmental monitoring in agriculture. Journal of Computer Networks and Communications. https://doi.org/10.1155/2025/ (Check article page for full DOI) [Google Scholar] [Crossref]

9. Holzinger, A., et al. (2025). Human-centered AI in smart farming: Toward Agriculture 5.0. [Google Scholar] [Crossref]

10. Mourtzinis, S., et al. (2025). A human-in-the-loop approach to applying large language models for farm management insight. [Google Scholar] [Crossref]

11. Ngugi, L., et al. (2024). Machine learning and deep learning for crop disease diagnosis: A review. Agronomy, 14. https://doi.org/10.3390/agronomy14123001 [Google Scholar] [Crossref]

12. Razak, N. A., et al. (2024). Agriculture 5.0 and explainable AI for smart agriculture: A scoping review. Emerging Science Journal. [Google Scholar] [Crossref]

13. Saranti, A., et al. (2025). Actionable explainable AI (AxAI): A practical example with aggregation functions for adaptive classification. https://doi.org/10.3390/make4040047 [Google Scholar] [Crossref]

14. Sreeram, V., & Nof, S. Y. (2021). Human-in-the-loop role in cyber-physical agricultural systems. International Journal of Computers Communications & Control, 16(2). https://doi.org/10.15837/ijccc.2021.2.4166 [Google Scholar] [Crossref]

15. Subhan, F., et al. (2026). Crop disease detection using EfficientNetB0 deep learning architecture. Discover Computing. https://doi.org/10.1007/s10791-025-09881-y [Google Scholar] [Crossref]

16. Sudha, M., & Loret, J. L. (2026). Machine learning-based precision agriculture techniques integrated with IoT. Discover Environment. https://doi.org/10.1007/s44274-025-00305-8 [Google Scholar] [Crossref]

17. Waltz, D., et al. (2025). Cyberinfrastructure for machine learning applications in agriculture. Frontiers in Artificial Intelligence, 7. [Google Scholar] [Crossref]

18. https://doi.org/10.3389/frai.2025.1496066 [Google Scholar] [Crossref]

19. Yakkala, K., et al. (2025). Deep learning-based crop health enhancement through early disease prediction. Cogent Food & Agriculture, 11(1). https://doi.org/10.1080/23311932.2024.2423244 [Google Scholar] [Crossref]

• Drone-Based Phenotyping and its Utilization in Crop Improvement: A Review
• An IoT-Enabled Smart Healthcare Monitoring System Using Machine Learning for Early Health Risk Prediction
• Strategic Integration of Artificial Intelligence for Achieving Operational Excellence in Container Freight Stations Using Evidence from Chennai, India
• Adoption of OTT platforms: Analyzing User Behavior through the UTAUT2 Model
• Customers’ Buying Behaviour in Relation to Online Food Delivery (Evidence from National Capital Region (NCR) of India)