Xiaolei Guo’s research while affiliated with University of Florida and other places

Interactive Annotation for object delineation can be considered as a semi-supervised few-shot learning problem where machine learning models learn from a small set of annotated pixels and generalize to the entire picture to extract the object of interest. One aim of interactive annotation is to reduce the effort of manually labeling data. Some existing works attempted to address this problem with deep metric learning so that the encoding layers in the network are able to extract features that boost discriminability among pixels belonging to different classes. To keep the data structure in the embedding space, metric loss with prototypes has been proposed. In our work, we improved the existing methods by developing a new objective function to update the network and prototypes simultaneously. The prototypes are optimized based on the loss that enhances their dissimilarity instead of clustering or sampling from the dataset. Moreover, we designed a GUI with the proposed method for interdisciplinary collaboration of image-support plant phenotyping studies.

Fresh fruit and vegetables are invaluable for human health; however, their quality often deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. We currently lack any objective indices which indicate the freshness of fruit or vegetables resulting in limited capacity to improve product quality eventually leading to food loss and waste. In this conducted study, we hypothesized that certain proteins and compounds, such as glucosinolates, could be used as one potential indicator to monitor the freshness of broccoli following harvest. To support our study, glucosinolate contents in broccoli based on HPLC measurement and transcript expression of glucosinolate biosynthetic genes in response to postharvest stresses were evaluated. We found that the glucosinolate biosynthetic pathway coincided with the progression of senescence in postharvest broccoli during storage. Additionally, we applied machine learning-based hyperspectral image (HSI) analysis, unmixing, and subpixel target detection approaches to evaluate glucosinolate level to detect postharvest senescence in broccoli. This study provides an accessible approach to precisely estimate freshness in broccoli through machine learning-based hyperspectral image analysis. Such a tool would further allow significant advancement in postharvest logistics and bolster the availability of high-quality, nutritious fresh produce.

Fresh fruits and vegetables are invaluable for human health, but their quality deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. The current lack of any objective indices for defining “ freshness ” of fruits or vegetables limits our capacity to control product quality leading to food loss and waste. It has been hypothesized that certain proteins and compounds such as glucosinolates can be used as an indicator to monitor the freshness of vegetables and fruits. However, it is challenging to “visualize” the proteins and bioactive compounds during the senescence processes. In this work, we propose machine learning hyperspectral image analysis approaches for estimating glucosinolates levels to detect postharvest senescence in broccoli. Therefore, we set out the research to quantify glucosinolates as “freshness-indicators” which aid in the development of an innovative and accessible tool to precisely estimate the freshness of produce. Such a tool would allow for significant advancement in postharvest logistics and supporting the availability for high-quality and nutritious fresh produce.