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Simple Summary: Sorghum holds significant economic importance across both the northern and southern regions of the U.S. Since 2013, the presence of the sorghum aphid Melanaphis sorghi (Theobald) has led to -yield reduction in unmanaged sorghum. Various management strategies have been critical in controlling aphid infestations, including cultural practices, host–plant resistance, and insecticide use. Typically, the common practice is applying insecticides at a whole-field level once aphids reach they reach a certain economic threshold. However, this traditional approach often includes treating plants that are not yet infested. To minimize insecticide usage in sorghum, we suggested selectively spraying individual plants only when aphids are present or not, as opposed to conventional spraying based on economic thresholds. Through field experiments conducted over two summer seasons, our findings demonstrated that spraying individual plants can manage aphid populations while reducing overall insecticide usage. This approach holds promise for advancing targeted insecticide applications in sorghum. Since the invasion of the sorghum aphid Melanaphis sorghi (Theobald), farmers in the sorghum (Sorghum bicolor L. Moench) production region in the Great Plains of the U.S. have faced significant crop damage and reduced yields. One widely used practice to aid in managing sorghum aphids is pest monitoring, which often results in field-level insecticide applications when an economic threshold is reached. However, relying on this traditional management practice includes the application of insecticides to non-infested plants. To reduce insecticide usage in sorghum, we proposed spraying individual plants when aphids are present or absent compared to traditional spraying based on a standard economic threshold using field replicate plots over two summer seasons. The experimental results of this study indicated fewer aphids in plots managed with an economic threshold, followed by randomly sprayed and plant-specific treatments compared with the untreated control treatment. Therefore, compared with traditional management, those treatments can be alternative strategies for managing aphids on sorghum within our field plot study. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community. [ABSTRACT FROM AUTHOR]