Abstract
Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models.
Abstract (translated)
蚜虫灾害是导致小麦和玉米田遭受严重破坏的主要原因之一，也是植物病毒最普遍的传播媒介，导致大量农业产量损失。为解决这个问题，农民通常会采用对有害化学农药的低效利用，这对健康和环境都有负面影响。因此，在无重大病虫害的地区，大量的农药被浪费在无明显病虫害的区域上。这使得人们更加关注迫切需要一种智能自主系统，可以在复杂的作物叶片中准确、选择性地定位和喷洒足够大的蚜虫群。 我们开发了一个大型的多尺度蚜虫聚类检测和分割数据集，从实际的玉米田中收集，并精心注释以包括蚜虫聚类。我们的数据集包括54,742个图像补丁，展示了各种视角、不同的光照条件和多个尺度，突出了其在现实应用中的有效性。 在本研究中，我们训练和评估了四种实时语义分割模型和三种专门用于蚜虫聚类检测和检测的对象检测模型。在准确性和效率之间取得平衡后，Fast-SCNN取得了最有效的分割结果，达到80.46%的均方精度（mAP）、81.21%的均召回率和91.66帧每秒（FPS）。在物体检测方面，RT-DETR在平均精度（mAP）、总体召回率和NVIDIA V100 GPU上的得分均最高。我们的实验进一步表明，蚜虫聚类分割更适合评估蚜虫灾害，而不是使用检测模型。
URL
https://arxiv.org/abs/2405.04305
