PDF(Pubmed)
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.
摘要：
蚜虫侵染是小麦和高粱田大面积破坏的主要原因之一,也是植物病毒最常见的传播媒介之一,造成了巨大的农业产量损失。为了解决这个问题,农民经常使用低效的有害化学农药，对健康和环境有负面影响。因此,大量农药被浪费在没有严重虫害的地区。这引起了对智能自主系统的迫切需要，该系统可以在复杂的作物冠层内选择性地定位和喷洒足够大的侵扰。我们开发了一个用于蚜虫簇检测和分割的大型多尺度数据集，从实际的高粱田收集，并精心注释，包括蚜虫簇。我们的数据集包含总共54,742个图像块，展示各种观点，不同的照明条件，和多个尺度，强调其在实际应用中的有效性。在这项研究中,我们训练并评估了四个实时语义分割模型和三个专门用于蚜虫簇分割和检测的对象检测模型。考虑到准确性和效率之间的平衡，Fast-SCNN提供了最有效的分割结果，达到80.46%的平均精度，81.21%平均召回，和91.66帧每秒（FPS）。对于对象检测，RT-DETR表现出最佳的整体性能，平均精度为61.63%（mAP），92.6%平均召回，和72.55在NVIDIAV100GPU上。我们的实验进一步表明，蚜虫簇分割比使用检测模型更适合评估蚜虫的侵染情况。
