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DiagrammaticLearning: A graphical language for compositional training regimes

April 3, 2026

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 […]

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Developing statistical models of aflatoxin risk in peanuts using historical weather data

April 3, 2026

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, […]

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Mutualism, herbivory, and invasive ants as seasonally dependent drivers of root surface area in a foundational savanna ant–plant

April 3, 2026

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 […]

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A Machine Learning Approach for Chlorophyll-a Estimation in Coastal Waters from Top-of-Atmosphere VIIRS Satellite Data

April 3, 2026

Abstract: Chlorophyll-a (Chl-a) concentration is a key indicator of water quality and phytoplankton biomass, used to monitor algal blooms in coastal and offshore waters. Traditional Chl-a prediction methods involve a two-step process: atmospheric correction to convert top-of-the-atmosphere (TOA) data to water-leaving radiance, followed by a machine learning or band-ratio algorithm. However, atmospheric correction is challenging […]

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Comparison of unsupervised quantitative metrics for superpixel segmentation

April 3, 2026

Abstract: Superpixel algorithms group statistically similar areas of images into local compact segments. Using superpixels as a preprocessing step has significantly reduced computation times for applications such as facial recognition, object detection, and classification. Many metrics have been established to evaluate superpixel segmentations given ground-truth labels. However, obtaining pixel-level or superpixel-level labels can be costly. […]

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PlantCV v4: Image Analysis Software for High-throughput Plant Phenotyping

March 10, 2026

Abstract: PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding […]

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Assessing Transferability of Remote Sensing Pasture Estimates Using Multiple Machine Learning Algorithms and Evaluation Structures

October 25, 2025

Abstract: Both the vastness of pasturelands and the value they contain—e.g., food security, ecosystem services—have resulted in increased scientific and industry efforts to remotely monitor them via satellite imagery and machine learning (ML). However, the transferability of these models is uncertain, as modelers commonly train and test on site-specific or homogenized—i.e., randomly partitioned—datasets and choose […]

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Plant parasitic nematode identification in complex samples with deep learning

October 24, 2025

Abstract: Plant parasitic nematodes are significant contributors to yield loss worldwide, causing devastating losses to every crop species, in every climate. Mitigating these losses requires swift and informed management strategies, centered on identification and quantification of field populations. Current plant parasitic nematode identification methods rely heavily on manual analyses of microscope images by a highly […]

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Histogram Layers for Neural “Engineered” Features

July 23, 2025

Abstract: In the computer vision literature, many effective histogram-based features have been developed. These engineered features include local binary patterns and edge histogram descriptors among others and they have been shown to be informative features for a variety of computer vision tasks. In this paper, we explore whether these features can be learned through histogram […]

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