UAV-based field watermelon detection and counting using YOLOv8s with image panorama stitching and overlap partitioning

文献类型： 外文期刊

第一作者： Jiang, Liguo

作者： Jiang, Liguo;Jiang, Hanhui;Jing, Xudong;Dang, Haojie;Li, Rui;Fu, Longsheng;Fu, Longsheng;Chen, Jinyong;Majeed, Yaqoob;Sahni, Ramesh

作者机构：

关键词： Watermelon yield estimation; Unmanned aerial vehicle; Object detection; Panorama stitching; Overlap partitioning

期刊名称：ARTIFICIAL INTELLIGENCE IN AGRICULTURE （ 影响因子：12.4； 五年影响因子：12.7 ）

ISSN：

年卷期： 2024 年 13 卷

页码：

收录情况： SCI

摘要： Accurate watermelon yield estimation is crucial to the agricultural value chain, as it guides the allocation of agricultural resources as well as facilitates inventory and logistics planning. The conventional method of watermelon yield estimation relies heavily on manual labor, which is both time-consuming and labor-intensive. To address this, this work proposes an algorithmic pipeline that utilizes unmanned aerial vehicle (UAV) videos for detection and counting of watermelons. This pipeline uses You Only Look Once version 8 s (YOLOv8s) with panorama stitching and overlap partitioning, which facilitates the overall number estimation of watermelons in field. The watermelon detection model, based on YOLOv8s and obtained using transfer learning, achieved a detection accuracy of 99.20%, demonstrating its potential for application in yield estimation. The panorama stitching and overlap partitioning based detection and counting method uses panoramic images as input and effectively mitigates the duplications compared with the video tracking based detection and counting method. The counting accuracy reached over 96.61 %, proving a promising application for yield estimation. The high accuracy demonstrates the feasibility of applying this method for overall yield estimation in large watermelon fields. (c) 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序