Novel Approaches used for Sugarcane Yield Prediction using Artificial Intelligence and Machine Learning

Sugarcane is a major cash crop in India that must be closely regulated because it contributes significantly to national exchange and employs over a million people mostly through sugarcane and renewable bioenergy production. Many facets of sugarcane crop management will benefit from an accurate model for estimating sugarcane production. It is a significant crop in the Indian agricultural market. Techniques aimed at increasing raw material production and quality, such as localized management, have been used manually by farmers for many years and have significant potential. Many Sugarcane yield monitoring systems have been developed and tested over the years, but their utilization is still limited. This study proposes artificial intelligence and ML algorithms for predicting Sugarcane Yield/Production Environments using a reduced number of low-cost variables. Satellite imagery was combined with climate, soil, and elevation data, and a Sugarcane harvester was provided in the Controller Area Network. All of the factors were required for the models to forecast Sugarcane Yield for better performance.