Category: Journal Papers

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

Abstract: Emergent ecosystem properties, such as population and trait distributions, biodiversity and energy and water fluxes, occur because of the dynamic interactions of individuals in their environment. Remote sensing, where image data is collected over large areas, can provide information about individual organisms that reveals important ecosystem patterns and processes that are critical for macrosystems […]

Abstract: The curse of dimensionality in genomic prediction has been established and hampers genetic gain for complex traits. Artificial intelligence (AI) that fuses symbolic and sub-symbolic approaches to prediction is emerging as an approach that can deal effectively with this problem. By leveraging information across physiological and genetic networks, it is plausible to increase prediction […]

Abstract: Fine-scale pixel-level annotation of minirhizotron root images is a less common and challenging task. We present an interactive segmentation framework to accelerate root annotation. We leverage the concept of few-shot segmentation so that the pre-trained model can be effectively fine-tuned and transferred to an unseen category. To provide immediate feedback for real-time interaction, we […]