Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient.
The high-throughput field phenotyping systems and in particular robotic systems play an …

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic
variation of plants through multiple types of sensors, such as red green and blue (RGB) …

Machine learning systems deployed in the wild are often trained on a source distribution but
deployed on a different target distribution. Unlabeled data can be a powerful point of …

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with
decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led …

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor,
physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at …

Plant phenomics bridges the gap between traits of agricultural importance and genomic
information. Limitations of current field-based phenotyping solutions include mobility …

The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied
for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave …

Background Population growth and climate uncertainty increase the demand for more food
to feed the world. Finding a new variety and discovering novel genetic traits that confer …

We explore the use of deep convolutional neural networks (CNNs) trained on overhead
imagery of biomass sorghum to ascertain the relationship between single nucleotide …

Grain sorghum is a versatile crop, which can thrive under limited water and other inputs.
However, crop loss from weed infestation continues to be a major constraint in grain …