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Develop and assess an agentic artificial intelligence framework integrated with federated learning to enable real-time, privacy-preserving decision support in precision agriculture.

January 14, 2026Frontiers in Plant ScienceOpen Access

Agentic AI for smart and sustainable precision agriculture

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Develop and assess an agentic artificial intelligence framework integrated with federated learning to enable real-time, privacy-preserving decision support in precision agriculture.
Designed a distributed deployment architecture linking field sensors, autonomous software agents, and federated learning algorithms.
Trained and evaluated local deep learning architectures (DenseNet121, MobileNetV2, EfficientDet-D0, and YOLOv8) across two computer vision datasets for tomato disease classification and weed detection.
The federated global model achieved an overall accuracy of 96.4% for tomato disease classification, outperforming standalone client models DenseNet121 (95.0%) and MobileNetV2 (93.9%).
For weed species detection, EfficientDet-D0 achieved an mAP@0.5 of 0.978, average precision of 0.865, and an F1-score of 0.961, outperforming YOLOv8 which achieved an mAP@0.5 of 0.956 and an F1-score of 0.935.

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Implication

Machine learning evaluation demonstrates high diagnostic and detection accuracy across crop datasets, suggesting distributed agentic systems can optimize sustainable agricultural management.

Cite This Study

Srinivasu et al. (2026) studied this question.

synapsesocial.com/papers/6a11cea781e48c4370dce585 https://doi.org/10.3389/fpls.2025.1706428

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Implication

Machine learning evaluation demonstrates high diagnostic and detection accuracy across crop datasets, suggesting distributed agentic systems can optimize sustainable agricultural management.

Agentic AI for smart and sustainable precision agriculture

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Implication

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Agentic AI for smart and sustainable precision agriculture

Key Points

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Implication

Cite This Study

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