| Popis: |
Agriculture is an important occupation in India. Agriculture contributes to approximately 17% of India’s Gross Domestic Product and it’s still the most popular occupation amongst 70% of India’s population. Due to the various climatic factors the agriculture productivities in India are continuously decreasing over a decade. Farmers are still struggling when it comes to picking the right decision for better crop production. Thus, to accelerate the crop production, different technologies are being proposed worldwide. Data mining and machine learning in agriculture is a new approach for predicting crop yield production. The reasons for continuously decrease in production over a decade was studied mostly using regression analysis. The emerging technologies can change the situation of farmers and help in decision making. For this we are using Machine learning algorithms called k-means algorithm, AdaBoost and Random-forest algorithm. So, these algorithms take in different parameters to give us best recommendations which not only leads to better yields but also minimum use of resources and capital. In this model, the algorithm classifies the dataset into 4 classes namely, 0-poor yield, 1- average yield,2-good yield, 3-very good yield. This model predicts the expected accurate yield by considering the parameters like, state name, crop, production of crop, and season. |
