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Abstract Sorghum aphid, Melanaphis sorghi (Theobald) have become a major economic pest in sorghum causing 70% yield loss without timely insecticide applications. The overarching goal is to develop a monitoring system for sorghum aphids using remote sensing technologies to detect changes in plant-aphid density interactions, thereby reducing scouting time. We studied the effect of aphid density on sorghum spectral responses near the feeding site and on distal leaves from infestation and quantified potential systemic effects to determine if aphid feeding can be detected. A leaf spectrometer at 400–1000 nm range was used to measure reflectance changes by varying levels of sorghum aphid density on lower leaves and those distant to the caged infestation. Our study results demonstrate that sorghum aphid infestation can be determined by changes in reflected light, especially between the green–red range (550–650 nm), and sorghum plants respond systemically. This study serves as an essential first step in developing more effective pest monitoring systems for sorghum aphids, as leaf reflection sensors can be used to identify aphid feeding regardless of infestation location on the plant. Future research should address whether such reflectance signatures can be detected autonomously using small unmanned aircraft systems or sUAS equipped with comparable sensor technologies.

Wheat (Triticum aestivum) is a major cereal crop planted in the Southern Great Plains. This crop faces diverse pests that can affect their development and reduce yield productivity. For example, aphids are a significant pest in wheat, and their management relies on pesticides, which affect the sustainability and biodiversity of natural predators that prey on aphids. Coccinellids, commonly named lady beetles, are the most abundant natural predators of wheat. These natural enemies contribute to the natural predation of aphids, which can reduce the use of excessive pesticides for aphid management. Usually, visual observations of these natural enemies are performed during pest sampling; however, it is time-consuming and requires manual labor, which can be expensive. An automation system or detection models based on machine learning approaches that can detect these insects is needed to reduce unnecessary pesticide applications and manual labor costs. However, developing an automation system or computer vision models that automatically detect these natural enemies requires imagery to train and validate this cutting-edge technology. To solve this research problem, we collected this dataset, which includes images and label annotations to help researchers and students develop this technology that can benefit wheat growers and science to understand the capabilities of automation in Entomology. We collected a dataset using mobile devices, which included a diverse range of coccinellids on wheat images. The dataset consists of 2,133 images with a standard size of 640 × 640 pixels, which can be used to train and develop detection models for machine learning purposes. In addition, the dataset includes annotated labels that can be used for training models within the YOLO family or others, which have been proven to detect small insects in crops. Our dataset will increase the understanding of machine learning capabilities in entomology, precision agriculture, education, and crop pest management decisions.

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 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 Increased global production of sorghum has the potential to meet many of the demands of a growing human population. Developing automation technologies for field scouting is crucial for long-term and low-cost production. Since 2013, sugarcane aphid (SCA) Melanaphis sacchari (Zehntner) has become an important economic pest causing significant yield loss across the sorghum production region in the United States. Adequate management of SCA depends on costly field scouting to determine pest presence and economic threshold levels to spray insecticides. However, with the impact of insecticides on natural enemies, there is an urgent need to develop automated-detection technologies for their conservation. Natural enemies play a crucial role in the management of SCA populations. These insects, primary coccinellids, prey on SCA and help to reduce unnecessary insecticide applications. Although these insects help regulate SCA populations, the detection and classification of these insects is time-consuming and inefficient in lower value crops like sorghum during field scouting. Advanced deep learning software provides a means to perform laborious automatic agricultural tasks, including detection and classification of insects. However, deep learning models for coccinellids in sorghum have not been developed. Therefore, our objective was to develop and train machine learning models to detect coccinellids commonly found in sorghum and classify them according to their genera, species, and subfamily level. We trained a two-stage object detection model, specifically, Faster Region-based Convolutional Neural Network (Faster R-CNN) with the Feature Pyramid Network (FPN) and also one-stage detection models in the YOLO (You Only Look Once) family (YOLOv5 and YOLOv7) to detect and classify seven coccinellids commonly found in sorghum (i.e., Coccinella septempunctata, Coleomegilla maculata, Cycloneda sanguinea, Harmonia axyridis, Hippodamia convergens, Olla v-nigrum, Scymninae). We used images extracted from the iNaturalist project to perform training and evaluation of the Faster R-CNN-FPN and YOLOv5 and YOLOv7 models. iNaturalist is an imagery web server used to publish citizen’s observations of images pertaining to living organisms. Experimental evaluation using standard object detection metrics, such as average precision (AP), AP@0.50, etc., has shown that the YOLOv7 model performs the best on the coccinellid images with an AP@0.50 as high as 97.3, and AP as high as 74.6. Our research contributes automated deep learning software to the area of integrated pest management, making it easier to detect natural enemies in sorghum.

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.

Aphids are a challenging crop pest to manage. The sorghum aphid, for example, causes considerable yield loss in unmanaged sorghum. One of the key strategies to mitigate yield losses caused by this pest includes monitoring productions fields and using economic thresholds to spray insecticides. However, monitoring aphids is a time-consuming task and requires regular, visual assessments across large hectarage once aphids are detected on sorghum plants. To address this challenge, we propose to use object detection models based on deep learning to automatically detect aphid infestations on sorghum leaves using digital images. We used 1190 images collected during field monitoring events and evaluated the performance of 3 deep learning detection models within the YOLOv5 family that vary in complexity: YOLOv5n, YOLOv5s, and YOLOv5m. We then tested three different image sizes, including input resolutions of 416 × 416, 640 × 640, and 1280 × 1280 pixels. We trained models to detect individual aphids, which ranged between 1 and 125 sorghum aphids/leaf and is comparable to threshold levels used to manage aphids in field conditions (i.e., 50–125 aphids per leaf). Detection models had a precision of 92% precision with a 84.5% recall and 90.6% mAP@0.5 for YOLOv5m Pytorch, making it a potential candidate for quantifying aphid densities using deep learning. The models tested and methodology developed here can be implemented in management decisions of sorghum aphids or as sampling tools for use in screening insect-resistant varieties. Development of mobile applications and integration into unmanned vehicles with sophisticated sensor systems will aid in use and adoption of computer vision models for pest management.

Sugarcane aphid, Melanaphis sacchari (Zehntner), has caused significant yield loss across the sorghum (Sorghum bicolor L. Moench) production region in the U.S. Adequate management of sugarcane aphid depends on pest monitoring and economic threshold levels to spray insecticides. However, scouting this pest under field conditions is time-consuming and inefficient. To assist pest monitoring, we propose the use of deep learning models to automatically classify sugarcane aphid infestation on leaves according to different density levels in images. We used a total of 5,048 images collected during field scouting events and evaluated the performance of four deep learning classification models: Inception v3, DenseNet 121, Resnet 50, and Xception. We trained the models to classify aphid densities into 6 classes based on established standard threshold levels for spraying: no aphids present (0 sugarcane aphids/leaf), no threat or below an action/treatment threshold (1–10, and 11–39 sugarcane aphids/leaf), and infested above an economic threshold where an insecticide should be applied (40–125, 126–500, and > 500 sugarcane aphids/leaf) to manage sugarcane aphid in field conditions. Among these models, Inception v3 and Xception performed best with an overall accuracy score of 86% and a lower number of misclassified images. Importantly, the models correctly classified aphids as above or below threshold spray density over 97% of the time. The methodology developed and the models tested in this study can be used in sampling protocols and further mobile applications or remote sensing technologies. These technologies can assist sorghum growers and researchers to scout and screen sugarcane aphid in susceptible and resistant sorghum varieties automatically and provide accurate recommendations on whether or not to apply pesticides.

Abstract Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification. We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). Species-level error rates were generally smaller for species represented by more training images. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.

Pest infestation causes significant crop damage during crop production, which reduces the crop yield in terms of quality and quantity. Accurate, precise, and timely information on pest infestation is a crucial aspect of integrated pest management practices. The current manual scouting methods are time-consuming and laborious, particularly for large fields. Therefore, a fleet of scouting vehicles is proposed to monitor and collect crop information at the sub-canopy level. These vehicles would traverse large fields and collect real-time information on pest type, concentration, and infestation level. In addition to this, the developed vehicle platform would assist in collecting information on soil moisture, nutrient deficiency, and disease severity during crop growth stages. This study established a proof-of-concept of a crop scouting vehicle that can navigate through the row crops. A reconfigurable ground vehicle (RGV) was designed and fabricated. The developed prototype was tested in the laboratory and an actual field environment. Moreover, the concept of corn row detection was established by utilizing an array of low-cost ultrasonic sensors. The RGV was successful in navigating through the corn field. The RGV’s reconfigurable characteristic provides the ability to move anywhere in the field without damaging the crops. This research shows the promise of using reconfigurable robots for row crop navigation for crop scouting and monitoring which could be modular and scalable, and can be mass-produced in quick time. A fleet of these RGVs would empower the farmers to make meaningful and timely decisions for their cropping system.

Dectes texanus larvae girdles the stems of soybean and cultivated sunflowers causing significant yield losses in North America. The soybean Plant Introduction (PI) 165673 exhibits antibiosis resistance to the larval stage. The objectives of this study were: (1) to determine the inheritance of D. texanus resistance in PI165673; (2) evaluate PI165673 antibiosis resistance before 21 d post infestation; (3) evaluate girdling damage in PI16563 at the end of the season. K07-1544/PI165673 F2 and F2:3 populations were tested for resistance to D. texanus in 2011 and 2012, and PI165673 antibiosis resistance and girdling damage were evaluated in 2014. Segregation for resistance to D. texanus and heritability estimates in the F2 and F2:3 populations indicated that resistance was controlled by two genes with dominant and recessive epistasis. Antibiosis evaluations indicated: (1) PI165673 contained lower number of larvae and eggs relative to the number of oviposition punctures at 15 d post infestation; (2) the proportion of first-instar larvae was higher in PI165673 at 15 d post infestation; (3) larvae reach the sixth-instar stage in PI165673. None of the PI165673 plants were girdled at the end of the season. Identification of additional sources of D. texanus resistance is required to impair larval development in the stem.