A Machine Learning Approach for Rice Crop Yield Prediction and Phenotype Analysis

Abstract

The study of phenomics, which combines biology with transdisciplinary methods including image processing, data science, and engineering, is a developing discipline in India. Many nations used this technique to examine plants at various growth stages for their photosynthetic efficiency and temperature tolerance. Numerous research have looked at creating a reliable method to forecast rice production and yield based on multiple environmental conditions and phenotypic characteristics as height, yield, colour, and biomass. Rice grains per panicle, sometimes referred to as spikelets per panicle, are the primary phenotypic marker that has been used by researchers throughout the world to gauge the timely production (SPP).