Agricultural Crop and Fertilizer Recommendations based on Various Parameters

Agricultural uncertainty lowers the Tamil Nadu’s productivity because it is a coastal state. More production should be possible only with more people and land, but it is not possible. Farmers used to rely on word-of-mouth, but climate circumstances prevent them from doing so. Soil is one of the important factors in cultivating the crops. The new farmers are unaware about the crop which has to be cultivated in the particular season. To solve this problem, this system helps the farmer to know about the crop which has to be cultivated in the particular season to increase the production of the crops. Machine learning methods allow us to forecast the future and produce a precise model from the data. Agricultural issues like crop rotation, crop forecasting, water and fertilizer requirements, and crop protection can be solved. The need for an effective approach to simplify crop cultivation and the need to assist farmers in managing and producing their crops results from the environment’s unstable climatic conditions. This could enable aspiring farmers to practice better agriculture. With the aid of data mining, a farmer may be given a system of suggestions to aid in crop production. Crops are suggested for use in such a strategy depending on their quantity and meteorological considerations. The analysis of the crop dataset resulted in crop recommendations based on season, soil type, water and productivity. Various techniques like Linear Regression, Support Vector Regression and Adaptive Network Based Fuzzy Inference System (ANFIS) are used in this system, which enable accuracy comparison and assist farmers in selecting the best crop under various situations.