外文摘要：Soybean is a critical source of food, protein, and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod -counting algorithm capable of performing effectively in real -field conditions remains a significant challenge. This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point -based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and opensourced, consisting of 113 drone images with more than 260k manually annotated soybean pods. The images are taken from an altitude of approximately 13 ft, with angles between 53 and 58 degrees, under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real -world scenarios. The publication also provides both the source code and the labeled soybean dataset, offering a valuable resource for future research endeavors in soybean pod counting and related fields.
外文关键词：deep learning;convolutional neural networks;Soybean yield prediction;Soybean pod counting;Transformer-based network;Point-based crowd counting
作者：Chen, Dong;Li, Jiajia;Magar, Raju Thada;Lin, Feng;Wang, Dechun;Yin, Xiang;Zhuang, Weichao;Li, Zhaojian
作者单位：Michigan State Univ;Shanghai Jiao Tong Univ;Southeast Univ;Univ Virginia
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：220
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台