Fertilizer Forecasting using Machine Learning

Machine learning is a newer technology that deals with a greater volume of data than any other in the world today. The primary source of income in India comes from agriculture. The primary goals are to increase profitability and produce enough food for everyone in India, though agriculture is combined with cutting - edge technology to progress the industry and achieve the goals. In this research, predictions are made about the fertilizers that will increase crop yield and boost profits. Fertilizer prediction is a crucial task in agriculture that involves determining the appropriate type and quantity of fertilizer to use for a certain crop. This work has a variety of difficulties despite being crucial for raising agricultural yields and reducing the environmental impact of farming. To overcome this, machine learning methods like Random Forest has been employed. This method is considered because, it demonstrates greater accuracy, compared to other methods like linear regression, K-Nearest Neighbours, etc. This paper considers the past conditions and farmer's experience and the answers, making or considering the datasets from Kaggle. The datasets are used to predict the fertilizers based on the environmental, soil, and plant conditions. Therefore, this research work predicts the fertilizers which are suitable for the above-mentioned conditions.