An autonomous UAV system for precision greenhouse muskmelon growth monitoring

J.H. Lin; T.T. Lin

An autonomous UAV system for precision greenhouse muskmelon growth monitoring

In: Precision agriculture '25

Authors:

J.H. Lin

J.H. Lin National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei, Taiwan, ROC, ^*m456@ntu.edu.tw

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T.T. Lin

T.T. Lin National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei, Taiwan, ROC, ^*m456@ntu.edu.tw

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Type:: Chapter

Pages:: 629–634

DOI:: https://doi.org/10.1163/9789004725232_082

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Dar Hadith al Hassania

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Greenhouse muskmelon cultivation requires precise monitoring to optimize management and yield, particularly during the vine growth stage. This study developed an image processing pipeline through a visual SLAM-based autonomous unmanned aerial vehicle system for continuous monitoring in greenhouse environment. The proposed system, equipped with a camera, followed a pre-defined flight path to capture visual data of the growing plants. Image processing and visual segmentation techniques were then employed to analyze the data. Specifically, YOLOv8 is used for plant detection, SORT for tracking, and DinoV2 for segmentation and measurement. Testing on 15 muskmelon plants over 21 days achieved a mean Average Precision (mAP) of 0.921 and Multiple Object Tracking Accuracy (MOTA) of 72.4%. Accurate height (R²=0.979) and width (R²=0.747) measurements highlight its potential for scalable greenhouse crop monitoring.

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Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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An autonomous UAV system for precision greenhouse muskmelon growth monitoring

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Precision agriculture '25

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