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Purpose <p>Efficient orchard management requires high-throughput phenotyping technologies to assist growers in crop monitoring and decision-making. This study presents Agrosense, an advanced artificial intelligence (AI) powered sensing system designed for real-time phenotypic data collection in orchards, addressing the limitations of traditional manual methods.</p> Methods <p>Agrosense integrates four RGB-D cameras with a Jetson Xavier microprocessor to collect high-resolution data and perform tree crop counting, canopy density classification, and tree height estimation. A citrus orchard served as a case study, where 337 trees were imaged to train and validate AI models. YOLOv8 was employed for object detection and classification tasks, while five methods were tested for estimating tree height.</p> Results <p>The YOLOv8 model achieved a mean average precision (mAP) of 0.977 for tree trunkdetection and 0.974 for canopy density classification. In field testing on 157 citrus trees, the system achieved 95% accuracy for tree trunk detection and 94% accuracy for canopy density classification, with only 11 misclassifications. The best-performing method for height estimation achieved a mean absolute percentage error (MAPE) of 8.53%. Agrosense completed phenotyping tasks in 398 s, a 515% speed improvement over manual methods (2,446 s).</p> Conclusion <p>Agrosense effectively supports precision orchard management by automating key phenotyping tasks with high accuracy and efficiency. The system significantly reduces data collection time and improves consistency. Future work will focus on algorithm refinement and adaptation to other tree crops to broaden the system’s utility in precision agriculture.</p>

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Agrosense: Accelerating precision orchard management through an AI-enabled monitoring system

  • 期刊论文

  • 发表日期：

    2025年7月26日

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  • Congliang Zhou,
  • Yiannis Ampatzidis,
  • Hengyue Guan,
  • Shiyu Liu,
  • Wenhao Liu,
  • Antonio de Oliveira Costa Neto,
  • Sanju Kunwar,
  • Ozgur Batuman

摘要

Purpose

Efficient orchard management requires high-throughput phenotyping technologies to assist growers in crop monitoring and decision-making. This study presents Agrosense, an advanced artificial intelligence (AI) powered sensing system designed for real-time phenotypic data collection in orchards, addressing the limitations of traditional manual methods.

Methods

Agrosense integrates four RGB-D cameras with a Jetson Xavier microprocessor to collect high-resolution data and perform tree crop counting, canopy density classification, and tree height estimation. A citrus orchard served as a case study, where 337 trees were imaged to train and validate AI models. YOLOv8 was employed for object detection and classification tasks, while five methods were tested for estimating tree height.

Results

The YOLOv8 model achieved a mean average precision (mAP) of 0.977 for tree trunkdetection and 0.974 for canopy density classification. In field testing on 157 citrus trees, the system achieved 95% accuracy for tree trunk detection and 94% accuracy for canopy density classification, with only 11 misclassifications. The best-performing method for height estimation achieved a mean absolute percentage error (MAPE) of 8.53%. Agrosense completed phenotyping tasks in 398 s, a 515% speed improvement over manual methods (2,446 s).

Conclusion

Agrosense effectively supports precision orchard management by automating key phenotyping tasks with high accuracy and efficiency. The system significantly reduces data collection time and improves consistency. Future work will focus on algorithm refinement and adaptation to other tree crops to broaden the system’s utility in precision agriculture.