Crop Recommendation and Yield Estimation Using Machine Learning

Abstract

In most developing countries like India, Agriculture is seen as one of the most widely followed habitations and contributes majorly to the country’s economy. Agriculture provides the primary source of food, income, livelihood and employment to the majority of rural populations in India. Many crops are destroyed every year due to a lack of technical knowledge and unpredictable weather patterns such as temperature, rainfall, and other atmospheric parameters, which play a massive role in deciding the crop yield and profit. Therefore, choosing the right crop to grow and enhancing crop yield is an essential aspect of improving real-life farming scenarios. One of the motives is to collect and integrate the agricultural data from specific regions that may be used to analyse the optimal crop and estimate the crop yield. This script is novel by using simple crop, soil and weather parameters like crop, the area under cultivation, nitrogen, phosphorus and potassium content of the soil, season, average rainfall and temperature of a district in Karnataka, India. The user can predict the most suitable crop and its estimated yield for a chosen year. This model uses primary classification, techniques like the random forest, k-NN, logistic regression, decision tree, XGBoost, SVM and gradient boosting classifier for determining the most suitable crop and regression algorithms like Linear Regression, k-NN, DBSCAN, Random Forest and ANN algorithm to estimate the yield of the most optimal crop identified earlier. The algorithm that has the least mean error is chosen for prediction and estimation and thus gives better results than the particular machine learning algorithm domain. There is a web interface for ease of use for end-users. Therefore, this project assists the farmers in choosing the suitable crop that can be grown in a particular region during a specific season or specific period and estimate its yield and predict if the recommended crop is profitable. Hence this project helps the farmers in preserving their time by assisting them in the decision-making process.