Plant Genomics and Plant Science

The transition away from legacy BIOS firmware architectures provides an opportunity to increase the accuracy of large genomics platforms through advanced chipsets integrated with custom-built 4G/LTE broadcast base-stations that currently enable high speed data compression in remote locations. How these devices integrate phenotype data for selection of traits with respect to environmental variation in field trials requires efficient data capture, storage and real time GPU virtualisation of all connected devices. We propose a method utilizing unmanned drones with precision instrumentation for pre-processing and offline data capture systems with pre-flashed custom ROMs for phenotypic measurement based on Markov chains and probability functions making use of Gibb's sampling. Environmental datasets such as topography maps, soil type, and climate data is cross-referenced to accurately and efficiently select genetically in the field via the onboard CPU/ GPU cluster and cloudbased API’s (solid state SSH super-computer CPU/GPU nuc SSH connection) kernel whilst online. Ultimately computation compression ratios, CPU and GPU facilitation of metascopic data clusters and embedded machine states, will determine much of the way forward in this space. However, the logistics required to “train” a drone via a neural network machine learning pipeline to accurately assess genotype in the field or, the management approach whether that be the desired outcome is now a reality. This research provides the preliminary pipeline using barley and yield, maturity, canopy-temperature, NDVI and stomata physiology as the plant characteristics to deliver a proof of concept .