Enhanced precision Multi-Crop farming using Machine Learning Recommendations

Generally, India’s agriculture sector is vital to the nation’s economy and serves as its backbone. Due to artificial climate change, food manufacturing and accessibility are becoming more scarce these days. This climatic change results in poor yield which in turn adversely affects the economy of farmers. Also, the farmers remain unfamiliar with forecasting future crops. There are various machine learning algorithm exists which can predict future crops. Though there are many methods exist to suggest the crops, it has not been tested in real-time soil. Hence, we propose a system, that can collect real-time soil data and recommend an appropriate or best-fitted crop planting. Also proposed system recommends the farmer whether to plant a single crop or multi crop for better yield. we have started this research by using multiple parameters in a single sensor method according to its characteristics. And based on the nutrition level in the soil we recommend the multi-crop seeding system from the research. Four different classification algorithms are employed in this study to determine the accuracy of the final result by visiting agricultural areas and locating soil nutrients with sensors. The final result which is discovered using the label that has previously been trained is the only one that is utilized for the classification that yields an accurate value. we considered the main role of algorithm is the Decision Tree algorithm only, because that’s only getting most accurate value from other classification algorithms its helps to recommending most suitable crop based on the predicted soil nutrients.