Machine Learning Models for Yield Prediction Based on Environmental Data

This chapter explores the use of machine learning for predicting crop yields based on environmental data. Key factors contributing to its potential include the availability of large volumes of historical data and powerful algorithms capable of processing it. Changes in climatic zones make artificial intelligence crucial for enhancing productivity. Procedures for data preparation are proposed, considering various factors such as climate conditions and fertilizer usage, improving predictions' accuracy. The systematization of these procedures creates a unified database for the agricultural sector, optimizing cultivation and minimizing risks. Advanced approaches and algorithms and the opportunities and challenges of integrating them into modern agrarian technologies are examined. A cloud-based solution architecture is proposed, ensuring flexibility in analytics. This will benefit farmers, researchers, and investors in enhancing resilience and productivity.