Estimating Xisha watermelon yield using sampling and global scanning based on drone remote sensing

Authors

  • Xiaofei Zhang 1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
  • Yuqing Zheng 1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
  • Ao Shen 1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
  • Yi Xun 1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310032, China; 2. Taizhou Key Laboratory of Advanced Manufacturing Technology, Taizhou Institute, Zhejiang University of Technology, Taizhou 318014, Zhejiang, China
  • Qinghua Yang 3. College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 310032, China; 4. The Collaborative Innovation Center for Intelligent Production Equipment of Characteristic Forest Fruits in Hilly and Mountainous Areas of Zhejiang Province, Zhejiang A&F University, Hangzhou 310032, China
  • Zhiheng Wang 1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310032, China; 2. Taizhou Key Laboratory of Advanced Manufacturing Technology, Taizhou Institute, Zhejiang University of Technology, Taizhou 318014, Zhejiang, China

Abstract

Xisha watermelon, one variety of selenium-rich economic crops, has been widely cultivated in the semi-arid regions of northwest China. It is critical to estimate its yield for the decision making on the harvesting and market. This paper presents a yield estimation model for Xisha watermelon using drone remote sensing in digital agriculture. Firstly, the watermelon weight was estimated in three steps: object detection with YOLOv8n, contour fitting with the functions from the OpenCV library, and weight estimation of individual watermelon through a volume-to-weight model. Then, two yield estimation strategies were developed. 1) Sampling: the total yield of watermelons over the entire plot was calculated using the average yield within sampled units and the plot area. 2) Global scanning: an overall yield distribution of watermelons was obtained to scan the entire plot using orthoimagery. Finally, a series of field tests was carried out to verify the estimation in plantations. The results reveal that the average accuracy of the detection model was 0.986 using YOLOv8n. Once the number of watermelons exceeded 45, the relative error between the total estimated and the measured weight was less than 1.00%. The speed of sampling was 27.37 m2/s for a 9000 m2 field size of Xisha watermelon, approximately 50 times higher than that of global scanning. Compared with global scanning, the sampling-based estimation underestimated the count by 1.77% and the total weight by 5.10%, both of which fall within an acceptable range. Each estimation can be suitable for the specific scenarios of application. The sampling can be expected to provide the higher efficiency for the total field yield. While the global scanning can effectively represent the overall yield distribution of Xisha watermelons in the field. This study provides a new research approach and direction for fruit and vegetable yield estimation in precision agriculture based on UAV remote sensing technology.      

Keywords: yield estimation, drone remote sensing, object detection, Xisha watermelon

DOI: 10.25165/j.ijabe.20261902.9946

 

Citation: Zhang X F, Zheng Y Q, Shen A, Xun Y, Yang Q H, Wang Z H. Estimating Xisha watermelon yield using sampling and global scanning based on drone remote sensing. Int J Agric & Biol Eng, 2026; 19(2): 272–281.

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Published

2026-05-21

How to Cite

(1)
Zhang, X.; Zheng, Y.; Shen, A.; Xun, Y.; Yang, Q.; Wang, Z. Estimating Xisha Watermelon Yield Using Sampling and Global Scanning Based on Drone Remote Sensing. Int J Agric & Biol Eng 2026, 19, 272-281.

Issue

Vol. 19 No. 2 (2026): IJABE

Section

Information Technology, Sensors and Control Systems

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