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Mungbean (Vigna radiata(L.) Wizcek) is an important pulse crop, increasingly used as a source of protein, fiber, low fat, carbohydrates, minerals, and bioactive compounds in human diets. Mungbean is a dicot plant with trifoliate leaves. The primary component of many plant functions, including photosynthesis, light interception, and canopy structure, are leaves. The objectives were to investigate leaf morphological attributes, use image analysis to extract leaf morphological traits from photos from the Iowa Mungbean Diversity (IMD) panel, create a regression model to predict leaflet area, and undertake association mapping. We collected over 5000 leaf images of the IMD panel consisting of 484 accessions over 2 years (2020 and 2021) with two replications per experiment. Leaf traits were extracted using image analysis, analyzed, and used for association mapping. Morphological diversity included leaflet type (oval or lobed), leaflet size (small, medium, large), lobed angle (shallow, deep), and vein coloration (green, purple). A regression model was developed to predict each ovate leaflet's area (adjustedR² = 0.97; residual standard errors of < = 1.10). The candidate genesVradi01g07560,Vradi05g01240,Vradi02g05730, andVradi03g00440are associated with multiple traits (length, width, perimeter, and area) across the leaflets (left, terminal, and right). These are suitable candidate genes for further investigation in their role in leaf development, growth, and function. Future studies will be needed to correlate the observed traits discussed here with yield or important agronomic traits for use as phenotypic or genotypic markers in marker‐aided selection methods for mungbean crop improvement.

Pneumatically actuated soft continuum manipulators (SCMs) are constructed by combining several extending or contracting fiber reinforced elastomeric enclosure (FREE) actuators in series, parallel and a combination thereof. While it is well known that architectures with serial combinations of FREEs yield large workspace and dexterity, they suffer from design and control complexity, increased number of valves and inertia. Recent advances in exploring the FREE design space has demonstrated using parallel combinations of dissimilar FREEs (bending and rotating) to improve workspace and dexterity. This paper presents a comprehensive investigation of SCM design architectures by enumerating possibilities of serial and parallel combinations of similar and dissimilar FREEs. A novel dexterity metric is proposed to enable objective comparison of different SCM designs based on shape similarity and end-effector tangent. Given a fixed resource of control inputs (actuator and valve inputs), the paper systematically selects the best architecture of the SCM (serial, parallel, similar or dissimilar FREE) that maximizes dexterity and workspace. It is seen that optimal designs are heavily dependent on the context of the application, which may change how these manipulators are deployed. The paper presents two practical design applications that demonstrate the usefulness of the enumeration framework. While in general, serial design combinations using symmetric bending actuators result in larger workspace and dexterity, some architectures with asymmetric combinations of FREEs may see similar levels of dexterity and workspace.

Insect pests cause significant damage to food production, so early detection and efficient mitigation strategies are crucial. There is a continual shift toward machine learning (ML)‐based approaches for automating agricultural pest detection. Although supervised learning has achieved remarkable progress in this regard, it is impeded by the need for significant expert involvement in labeling the data used for model training. This makes real‐world applications tedious and oftentimes infeasible. Recently, self‐supervised learning (SSL) approaches have provided a viable alternative to training ML models with minimal annotations. Here, we present an SSL approach to classify 22 insect pests. The framework was assessed on raw and segmented field‐captured images using three different SSL methods, Nearest Neighbor Contrastive Learning of Visual Representations (NNCLR), Bootstrap Your Own Latent, and Barlow Twins. SSL pre‐training was done on ResNet‐18 and ResNet‐50 models using all three SSL methods on the original RGB images and foreground segmented images. The performance of SSL pre‐training methods was evaluated using linear probing of SSL representations and end‐to‐end fine‐tuning approaches. The SSL‐pre‐trained convolutional neural network models were able to perform annotation‐efficient classification. NNCLR was the best performing SSL method for both linear and full model fine‐tuning. With just 5% annotated images, transfer learning with ImageNet initialization obtained 74% accuracy, whereas NNCLR achieved an improved classification accuracy of 79% for end‐to‐end fine‐tuning. Models created using SSL pre‐training consistently performed better, especially under very low annotation, and were robust to object class imbalances. These approaches help overcome annotation bottlenecks and are resource efficient.