The UAV technology is gradually maturing and can provide extremely powerful support for smart agriculture and precise monitoring. Currently, there is no dataset related to green walnuts in the field of agricultural computer vision. Thus, in order to promote the algorithm design in the field of agricultural computer vision, we used UAV to collect remote-sensing data from 8 walnut sample plots. Considering that green walnuts are subject to various lighting conditions and occlusion, we constructed a large-scale dataset with a higher-granularity of target features - WalnutData. This dataset contains a total of 30,240 images and 706,208 instances, and there are 4 target categories: being illuminated by frontal light and unoccluded (A1), being backlit and unoccluded (A2), being illuminated by frontal light and occluded (B1), and being backlit and occluded (B2). Subsequently, we evaluated many mainstream algorithms on WalnutData and used these evaluation results as the baseline standard. The dataset and all evaluation results can be obtained atthis https URL.
View on arXiv@article{wu2025_2502.20092,
title={ WalnutData: A UAV Remote Sensing Dataset of Green Walnuts and Model Evaluation },
author={ Mingjie Wu and Chenggui Yang and Huihua Wang and Chen Xue and Yibo Wang and Haoyu Wang and Yansong Wang and Can Peng and Yuqi Han and Ruoyu Li and Lijun Yun and Zaiqing Chen and Yuelong Xia },
journal={arXiv preprint arXiv:2502.20092},
year={ 2025 }
}