  Soybeans are a critical source of food, protein and oil, and thus have
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 open-sourced, consisting of 113
drone images with more than 260k manually annotated soybean pods captured under
natural lighting conditions. Through comprehensive evaluations, SoybeanNet
demonstrated superior performance over five state-of-the-art approaches when
tested on the collected images. Remarkably, SoybeanNet achieved 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 (\url{https://github.com/JiajiaLi04/Soybean-Pod-Counting-from-UAV-Images})
and the labeled soybean dataset
(\url{https://www.kaggle.com/datasets/jiajiali/uav-based-soybean-pod-images}),
offering a valuable resource for future research endeavors in soybean pod
counting and related fields.
