Grapes Quality Prediction Using Iot & Machine Learning Based on Pre Harvesting

Minimizing pesticide use, preserving water, as well enhancing soil health are just a few of the sustainable farming techniques that must be carefully considered while growing grapes high calibre. These practices can help preserve environment and ensure longevity vineyard. However, it is difficult for farmers to find suitability its cultivate with quality. Thus this research aims evaluate fitness quality aid machine learning algorithm. The was done on Nasik region which called “Grape Capital India” situated in Maharashtra. Total 154 villages were examination specimens collected sent government testing lab characteristics by considering both micro macro nutrients, water obtained from lab. Also climatic features, petiole fruit included creating dataset. data given six different algorithm classify defining whether fit or not. Moreover, proposed analyze correlation between nutrients relationship dependency features equal importance suitable obtaining grapes. Based results obtained, Pimpalas Ramche contains more grape grow successfully based samples gathered vine yards decision tree classifier scores better than any other classifiers among algorithms employed terms accuracy.