Optimization of Crop Recommendations Using Novel Machine Learning Techniques

A farmer can use machine learning to make decisions about what crops sow, how care for those throughout the growing season, and predict crop yields. According World Health Organization, agriculture is essential nation’s quick economic development. Food security, access, adoption are three cornerstones of organization. Without a doubt, main priority ensure that there enough food everyone. Increasing agricultural yield help sufficient supply. The country-wide variation in yields substantial. As result, this will be foundation research into whether cluster analysis used identify patterns field. Previous study investigations were only marginally successful accomplishing their primary intended objectives because unstable conditions imprecise methodology. vast majority farmers base predictions on prior observations growth farms, which deceptive. Standard preprocessing methods random value selection not always reliable, according literature. proposed overcomes shortcomings conventional methodology by highlighting significance learning-based classification/partitioning hierarchical approaches offering trained prediction state Karnataka. dataset was collected from ICAR-Taralabalu Krishi Vigyan Kendra, Davangere, In two techniques employed find anomalies, area, production significant variables. Crop area important variables detect anomalies. emphasizes importance mathematical model algorithm identifying trends, assist selecting have large seasonal impact productivity.