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

作　　者：Liguo Jiang Hanhui Jiang Xudong Jing Haojie Dang Rui Li Jinyong Chen Yaqoob Majeed Ramesh Sahni Longsheng Fu 

机构地区：[1]College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China [2]Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling,Shaanxi 712100,China [3]Zhengzhou Fruit Research Institute,Chinese Academy of Agricultural Sciences,Zhengzhou,Henan 450009,China [4]Department of Biological&Agricultural Engineering,Texas A&M University,Dallas,TX 75252,United States [5]Agricultural Mechanization Division,ICAR-Central Institute of Agricultural Engineering,Bhopal 462038,Madhya Pradesh,India

出　　处：《Artificial Intelligence in Agriculture》2024年第3期117-127,共11页农业人工智能（英文）

基　　金：supported by the National Natural Science Foundation of China(32371999);Science and Technology Program of Yulin City,China(2023-CXY-183);Open Project of Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in Southeastern China(Co-construction by Ministry and Province),Ministry of Agriculture and Rural Affairs,China(QSKF2023002);National Foreign Expert Project,Ministry of Science and Technology,China(QN2022172006L,DL2022172003L).

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 onmanual 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 ofwatermelons 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 comparedwith the video tracking based detection and countingmethod.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.

关 键 词：Watermelon yield estimation Unmanned aerial vehicle Object detection Panorama stitching Overlap partitioning 

分 类 号：S651[农业科学—果树学] TP39[农业科学—园艺学]