Category: Publication

Abstract: The recent development of deep learning leads to the current surge of breakthroughs in the research field of computer vision. Most deep learning models rely on the convolution operator to extract features. However, the convolution operator has several limitations. For example, convolution is a linear feature extractor based on the correlation between filters and […]

Abstract: Plant research has primarily focused on above-ground traits, such as leaves and flowers, while roots have received comparatively less attention due to their difficulty in imaging. Minirhizotron (MR) systems are commonly used to capture root images underground, but their use may impact plant root growth, and they only provide a two-dimensional (2D) view of […]

Abstract: Segmenting regions of interest in images, such as identifying roots in minirhizotron root images, is a critical step in several applications. However, manually annotating large image datasets to train a reliable segmentation model is time-consuming. To address this challenge, we propose a deep interactive segmentation framework to reduce the annotation burden. Interactive segmentation allows […]

Abstract: Coffee and peanut are globally important crops playing pivotal roles in both food systems and global economies. Under future climate projections, both crops will experience major changes in environmental conditions, including more frequent and intense drought, and exposure to rising temperatures. In this work we identified physiological responses to drought for coffee and peanut […]

Abstract: Semantic segmentation is the task of dividing entire images into non-overlapping regions with per-pixel class labels that correspond to a problem’s objects of interest. To do the task well, researchers introduce techniques of extracting object features from the image cues of shape, color, and texture to give models an improved ability to discriminate between […]

Abstract: Typically, the training of machine learning systems assumes that unexpected or unreasonable data, otherwise called outliers (e.g., samples that are distinct from and not represented in the training data distribution), will never be encountered. Since outliers are samples not drawn from the data distribution of interest, the diversity of outliers precludes the ability to […]

Abstract: Domain translation, a pivotal task in multimodal machine learning, is the process of transforming data from one domain to another. Domain translation enables tasks such as image-to-image translation, text-to-image synthesis, and cross-modal retrieval, and in remote sensing, it applies to modalities like hyperspectral imagery, optical imagery, and Synthetic Aperture Radar (SAR). The need for […]

Abstract: Motivated by deep learning regimes with multiple interacting yet distinct model components, we introduce learning diagrams, graphical depictions of training setups that capture parameterized learning as data rather than code. A learning diagram compiles to a unique loss function on which component models are trained. The result of training on this loss is a […]

Abstract: Aflatoxin contamination in peanuts (Arachis hypogaea L.) is a significant public health risk. Aflatoxin is detected postharvest after inspection of loads associated with grading at peanut buying points, leaving growers and shellers in a precarious position. Stricter limits on aflatoxin contamination could restrict the United States access to international markets. Predicting aflatoxin risk remains challenging, […]

Abstract: Many plants are defended from herbivory by costly insect mutualists. Understanding positive associations between plants and mutualists requires a whole-plant perspective including roots. We hypothesized that root surface area increases with mutualist activity (to a saturation threshold) and recent rainfall but that this relationship shifts when herbivores are excluded. We also hypothesized that invasive […]