Maximise Yield of Crop using Recommendation system based on Machine learning

Data mining is the way of interrogate and concluding decisive information from raw data. Data Mining discovers its usage in various areas like agriculture, health, retail, finance, medicine and etc. Data mining is used in agriculture for establishing the correlation between soil nutrients and climate for determining the farmer’s decision making. Crop farming is dependent on soil nutrients like phosphorus, calcium, and potassium as well as soil PH, temperature, and rainfall. These factors are investigated using a variety of techniques, including Random Forest, ANN, SVM, NB, LR, and DT algorithms. Our research paper’s objective is to identify the ideal crop type. Our farmers are better able to choose the crop that is best suited for the soil in their fields as a result of our efforts to try and solve one of their problems [26]. This paper also covers the theoretical and conceptual aspects of crop recommendation systems using a concept of machine learning and its various techniques. We also had to make sure that our model was more accurate and efficient than others and that it was updated regularly to ensure that users received updates on time. We need to gather data from a particular area that includes all relevant factors, such as the soil and environment. With 99.77% accuracy for crop prediction, Random Forest is the best algorithm among all the techniques used in our model. The random forest algorithm makes use of decision tree features in addition to its own.