BudCAM: An Edge Computing Camera System for Bud Detection in Muscadine Grapevines

Chi-En Chiang, Wei-Zhen Liang (), Jingqiu Chen (), Xin Qiao, Violeta Tsolova, Zonglin Yang and Joseph Oboamah
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Chi-En Chiang: Department of Biological Systems Engineering, University of Nebraska—Lincoln, Lincoln, NE 68583, USA
Wei-Zhen Liang: Department of Biological Systems Engineering, University of Nebraska—Lincoln, Lincoln, NE 68583, USA
Jingqiu Chen: Biological Systems Engineering, College of Agriculture and Food Sciences, Florida A&M University, Tallahassee, FL 32307, USA
Xin Qiao: Department of Biological Systems Engineering, University of Nebraska—Lincoln, Lincoln, NE 68583, USA
Violeta Tsolova: Center for Viticulture and Small Fruit Research, Florida A&M University, Tallahassee, FL 32307, USA
Zonglin Yang: Department of Biological Systems Engineering, University of Nebraska—Lincoln, Lincoln, NE 68583, USA
Joseph Oboamah: Department of Biological Systems Engineering, University of Nebraska—Lincoln, Lincoln, NE 68583, USA

Agriculture, 2025, vol. 15, issue 21, 1-25

Abstract: Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM, a low-cost, solar-powered, edge computing camera system based on Raspberry Pi 5 and integrated with a LoRa radio board, developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Small Fruit Research from mid-February to mid-March, 2024, monitoring three wine cultivars ( A27 , noble , and Floriana ) with three replicates each. Muscadine grape canopy images were captured every 20 min between 7:00 and 19:00, generating 2656 high-resolution (4656 × 3456 pixels) bud break images as a database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified the top performers for both the single-stage (mAP@0.5 = 86.0%) and two-stage (mAP@0.5 = 85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations conducted during the 2025 growing season demonstrated the system’s effectiveness, with the two-stage model exhibiting superior robustness against environmental interference, particularly lens fogging. A time-series filter smooths the raw daily counts to reveal clear phenological trends for visualization. In its final deployment, the autonomous BudCAM system captures an image, performs on-device inference, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management.

Keywords: bud break detection; object detection; YOLOv11; muscadine vineyard management; deep learning (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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