FURI | Summer 2023

Prediction of Agricultural Droughts Using Deep Learning

Sustainability icon, disabled. A green leaf.

This research harnesses Convolutional Neural Networks (CNNs) to predict agricultural droughts, a costly disaster affecting food security and the environment. By analyzing two-dimensional remote sensing data, CNNs offer a novel approach. Aligned with the themes of Data and Sustainability, this study aims to enhance data-driven decision-making and mitigate climate change-related hazards. The anticipated outcome is a high-accuracy deep-learning model for real-world drought prediction.

Student researcher

Devbrat Hariyani

Computer science

Hometown: Vadodara, Gujarat, India

Graduation date: Spring 2025