OSC-CO: coattention and cosegmentation framework for plant state change with multiple features.

Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object's pixels, and then displaying a final segmented image.

Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability.

Despite efforts to collect genomics and phenomics ('omics') and environmental data, spatiotemporal availability and access to digital resources still limit our ability to predict plants' response to changes in climate. Our goal is to quantify the improvement in the predictability of maize yields by enhancing climate data. Large-scale experiments such as the Genomes to Fields (G2F) are an opportunity to provide access to 'omics' and climate data.

Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.

Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set.

A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950-2013.

A data set of observed daily precipitation, maximum and minimum temperature, gridded to a 1/16° (~6 km) resolution, is described that spans the entire country of Mexico, the conterminous U.S. (CONUS), and regions of Canada south of 53° N for the period 1950-2013.