Plant Detection and Counting: Enhancing Precision Agriculture in UAV and General Scenes

Abstract

Plant detection and counting play a crucial role in modern agriculture, providing vital references for precision management and resource allocation. This study follows the footsteps of machine learning experts by introducing the state-of-the-art Yolov8 technology into the field of plant science. Moreover, we made some simple yet effective improvements. The integration of shallow-level information into the Path Aggregation Network (PANet) served to counterbalance the resolution loss stemming from the expanded receptive field. The enhancement of upsampled features was accomplished through combining the lightweight up-sampling operator Content-Aware ReAssembly of Features (CARAFE) with the Multi-Efficient Channel Attention (Mlt-ECA) technique to optimize the precision of upsampled features. This collective approach markedly amplified the discernment of small objects in Unmanned Aerial Vehicle (UAV) images, naming it Yolov8-UAV. Our evaluation is based on datasets containing four different plant species. Experimental results demonstrate the strong competitiveness of our proposed method even when compared to the most advanced counting techniques, and it possesses sufficient robustness. In order to advance the cross-disciplinary research of computer vision and plant science, we also release a new cotton boll dataset with detailed annotated bounding box information. What’s more, we address previous oversights in existing wheat ear datasets by providing updated labels consistent with global research advancements. Overall, this research offers practitioners a powerful solution for addressing real-world application challenges. For UAV scenarios, recommend using the specialized Yolov8-UAV, while Yolov8-N is a wise choice for general scenes due to its sufficient accuracy and speed in the majority of cases. Furthermore, we contribute two meaningful datasets that have research significance, effectively promoting the application of data resources in the field of plant science. In short, our contribution is to improve the use of Yolov8 in UAV scenarios and open two datasets with bounding boxes. The curated data and code can be accessed at the following link: https://github.com/Ye-Sk/Plant-dataset.

Keywords

• Cotton boll
• detection and counting
• UAV
• wheat ear
• Yolov8