Soybeans (Glycine max (L.) Merr.), a popular food resource worldwide, have various uses throughout the industry, from everyday foods and health functional foods to cosmetics. Soybeans are vulnerable to pests such as stink bugs, beetles, mites, and moths, which reduce yields. Riptortus pedestris (R. pedestris) has been reported to cause damage to pods and leaves throughout the soybean growing season. In this study, an experiment was conducted to detect R. pedestris according to three different environmental conditions (pod filling stage, maturity stage, artificial cage) by developing a surveillance platform based on an unmanned ground vehicle (UGV) GoPro CAM. Deep learning technology (MRCNN, YOLOv3, Detectron2)-based models used in this experiment can be quickly challenged (i.e., built with lightweight parameter) immediately through a web application. The image dataset was distributed by random selection for training, validation, and testing and then preprocessed by labeling the image for annotation. The deep learning model localized and classified the R. pedestris individuals through a bounding box and masking in the image data. The model achieved high performances, at 0.952, 0.716, and 0.873, respectively, represented through the calculated means of average precision (mAP) value. The manufactured model will enable the identification of R. pedestris in the field and can be an effective tool for insect forecasting in the early stage of pest outbreaks in crop production.

Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation—a 1:1 (v/v) mixture of 95% ethanol and NaOCl (6–14%)—produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.

For the purpose of breeding documentation, researchers and breeders kept handwritten records of the breeding history, including parental information and breeding methods. The cultivars were used again as parents for further breeding, and modern cultivars of rice have a wide range of alleles from many generations of parents and ancestors. To understand such a breeding history, it is necessary to ask around for relevant information, which is then usually documented in Excel or Word by multiple breeders or breeding institutes. Here, we constructed RicePedigree, which contains the breeding history of rice based on the documents provided by the Rural Development Administration (RDA) in Korea. We devised a simple method for collecting a breeding history and storing it in a database. RicePedigree is a web-based application on the database that facilitates researchers’ and breeders’ utilization of the breeding history of rice. Based on the query cultivar name, it will return a hierarchical tree of breeding histories and a list of cultivars and breeding lines that contain query cultivars in their breeding histories. This app would be a good way to review and keep track of information about current and future cultivars.

Resveratrol is a phytochemical with medicinal benefits, being well-known for its presence in wine. Plants develop resveratrol in response to stresses such as pathogen infection, UV radiation, and other mechanical stress. The recent publications of genomic sequences of resveratrol-producing plants such as grape, peanut, and eucalyptus can expand our molecular understanding of resveratrol synthesis. Based on a gene family count matrix of Viridiplantae members, we uncovered important gene families that are common in resveratrol-producing plants. These gene families could be prospective candidates for improving the efficiency of synthetic biotechnology-based artificial resveratrol manufacturing.