Computer Vision for Pasture Biomass Estimation: Enabling Data-Driven Grazing Decisions through Multi-Modal Deep Learning

This research presents a practical system that helps farmers make better decisions about how to manage their grazing lands. I developed a computer-based method that uses satellite and drone images to accurately measure how much grass is available in pastures. Unlike previous approaches that simply estimate grass quantity, my system goes further by providing clear recommendations about whether farmers should graze their animals, how many animals the pasture can support, and what the environmental impact of their decisions will be. My method achieved 91% accuracy in predicting grass biomass, with an average error of only 195 kilograms per hectare, which is reliable enough for real-world farming use. The system also calculates how much carbon the pasture is storing, assesses the overall health of the grazing land, and provides sustainability scores to help farmers maintain their pastures for the long term. By transforming complex image data into simple, actionable advice, i bridge the gap between advanced technology and everyday farming practice, offering a tool that is both scientifically robust and practically useful for sustainable livestock management.