Application of machine learning in Crop Management

This comparision contains four aspects of crop management; forecasting yields, identifying diseases spotting weeds and enhancing crop quality. Machine learning (ML) methods are utilized in each category to tackle obstacles. Forecasting yields aims to boost productivity disease identification targets pest management weed detection prioritizes farming practices and crop quality analysis strives to elevate the value of products. These instances showcase how ML can effectively address facets of crop cultivation and oversight.