In this letter, we present a new dataset to advance the state of the art in detecting citrus fruit and accurately estimate yield on trees affected by the Huanglongbing (HLB) disease in orchard environments via imaging. Despite the fact that significant progress has been made in solving the fruit detection problem, the lack of publicly available datasets has complicated direct comparison of results. For instance, citrus detection has long been of interest in the agricultural research community, yet there is an absence of work, particularly involving public datasets of citrus affected by HLB. To address this issue, we enhance state-of-the-art object detection methods for use in typical orchard settings. Concretely, we provide high-resolution images of citrus trees located in an area known to be highly affected by HLB, along with high-quality bounding box annotations of citrus fruit. Fruit on both the trees and the ground are labeled to allow for identification of fruit location, which contributes to advancements in yield estimation and potential measure of HLB impact via fruit drop. The dataset consists of over 32,000 bounding box annotations for fruit instances contained in 579 high-resolution images. In summary, our contributions are the following: (i) we introduce a novel dataset along with baseline performance benchmarks on multiple contemporary object detection algorithms, (ii) we show the ability to accurately capture fruit location on tree or on ground, and finally (ii) we present a correlation of our results with yield estimations.

翻译：本文提出一个新的数据集，以推动在果园环境中受黄龙病影响的树上柑橘果实检测及产量精确估算技术发展。尽管果实检测问题已取得显著进展，但公开数据集的缺乏导致结果难以直接比较。例如，柑橘检测长期是农业研究领域的关注焦点，但涉及受黄龙病影响柑橘的公开数据集相关研究仍存在空白。为解决这一问题，我们改进了现有最先进的目标检测方法，使其适用于典型果园环境。具体而言，我们提供了位于黄龙病高发区域柑橘树的高分辨率图像，以及高质量柑橘果实边界框标注。树上和地面果实均被标注以识别果实位置，这有助于提升产量估算精度并通过落果现象评估黄龙病影响程度。该数据集包含579张高分辨率图像中超过32,000个果实实例的边界框标注。总结而言，我们的贡献包括：(i) 提出新数据集并建立多个当代目标检测算法的基线性能基准，(ii) 展示准确识别树上或地面果实位置的能力，(iii) 建立检测结果与产量估算之间的相关性。