Abstract: A measurement system including: a. a sensor system that includes: i. a Light Detection And Ranging (LiDAR) module with a laser emitter configured to generate measurement data representing raw range measurements to measure heights of a crop, and ii. a computing module, including: at least one wireline/wired communications module configured to communicate with the LiDAR module for the computing module to acquire the measurement data from the LiDAR module; and at least one wireless communications module configured for the computing module to communicate using a wireless connection/link with a remote computing system that is configured receive the acquired measurement data and to determine/calculate/estimate phenotypic quantities of the crop based on the measured heights for the purpose of high-throughput plant phenotyping (HTPP); and b. a mobile/vehicle mount configured to hold/support the sensor system above the crop and to direct the laser emitter towards the crop.

Abstract: The present invention is directed to a method for automated hyperspectral vegetation index (VI) determination, including: accessing measured spectra and respective measured ground truth values of a selected vegetation trait; accessing a library of VI models, each model including a relationship defining an index value for the vegetation trait by mathematically combining spectral measurement values at a plurality of wavebands; selecting a VI model from the library; generating a hyperparameter for each of the spectral measurement values of the selected model, the hyperparameter including a selected waveband for each of the plurality of model wavebands; evaluating the selected model with the selected wavebands with an objective function score; a model parameter tuning step using an optimizer to select the waveband for each of the at least two wavebands based on sequential model-based optimization (SMBO); and repeating the model selection, generation, evaluation and tuning steps for a plurality of iterations.

Abstract: Methods for image-based remote sensing of crop plants include: acquiring images of the crop plants from a camera flown over the crop by an unmanned/uncrewed aerial vehicle (UAV); forming an artificial neural network (ANN); and using the trained ANN to identify and/or measure one or more phenotypic characteristics of the crop plants in the images by classification and/or regression; and/or obtaining multispectral images of the crop plants from a multispectral camera flown over the crop by the UAV; mosaicking the multispectral images together; determining crop measurement metrics; crop height model (CHM), crop coverage (CC) and crop volume (CV) representing the crop plants in three dimensions from a fusion of a digital surface model and a digital terrain model; determining various vegetation indices (VIs) based on the multispectral orthomosaic reflectance map; and determining a measurement of dry biomass using CV and fresh biomass using CV×VIs.

Abstract: Isolated nucleic acids and proteins and plants expressing the same for improved nitrogen utilization, increased yield, and increased stress tolerance.