Machine Learning Model for Agriculture Crop: A Review Study

In contrast to the former system, which only took into account one state, we used a big dataset that covered all of India's states in our new method. These recommendations could be taken out and applied to teach the farmers. By creating a visual representation, the farmer can better comprehend the crops to grow. With the use of machine learning techniques, we can make predictions and create a clear model from the data. It is possible to resolve agricultural problems such crop rotation, crop prediction, water and fertiliser needs, and crop protection. In contrast to the former system, which only took into account one state, we used a big dataset that covered all of India's states in our new method. These recommendations could be taken out and applied to teach the farmers. By creating a visual representation, the farmer can better comprehend the crops to grow. With the use of machine learning techniques, we can make predictions and create a clear model from the data. It is possible to resolve agricultural problems such crop rotation, crop prediction, water and fertiliser needs, and crop protection. Crops are suggested for use in such a strategy depending on their quantity and meteorological considerations. Data analytics opens the door for the development of valuable agricultural database extraction. After analysing the crop data set, crops are recommended based on their productivity and the season