AI4MORECROPS: Predicting Crop Yields at a Farm-Level and District Level using Machine Learning in the Internet of Agro-Things (IoAT) in Tanzania,

Currently, farmers in Tanzania are faced with the challenge of predicting crop yields based on the climate conditions and their variability. This project  develop a model that utilizes historical multi-source data to predict maize and sorghum yields at the district level using climate reanalysis data, satellite imagery data, weather data, soil data, and proximal IoT data to train machine learning models to predict district-level yield data. The project is focused on the development of a smart AI –driven farming system using low-cost Internet of Agro Things (IoAT) sensors and interactive cloud-based big data analytics to monitor and evaluate crops’ performance in real-time.