A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields

TL;DR

This paper introduces a large, annotated dataset for aphid cluster detection in sorghum fields and evaluates multiple models, finding segmentation models like Fast-SCNN effective for real-time aphid infestation assessment.

Contribution

The study provides a new comprehensive dataset and compares multiple segmentation and detection models for aphid cluster identification in agricultural settings.

Findings

Fast-SCNN achieved 80.46% precision and 81.21% recall.

RT-DETR achieved 61.63% mAP and 92.6% recall.

Segmentation models are more suitable than detection models for infestation assessment.

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.