Tackle Outliers for Predictive Small Holder Farming Analysis

Agriculture plays a major role in a country's economy and GDP. Most of the farmers still follow old and conventional farming practices which may lead to crop failure. They depend on brokers/middlemen. Create a huge loss for the farmers due to which suicidal rates have increased. Provides accurate results as it considers various factors like soil and weather conditions for determining the best crop and for predicting the yield. The government gets an estimation of the total yield per area. Provides a Peer- To-Peer environment between farmers and buyers removing the need for brokerage, which enables farmers to get profit directly from buyers. Crop failures result from planting a crop without adequate knowledge of climatic and soil conditions, and selling goods to a broker result in even more losses. The goal is to develop a system that allows farmers to receive crop proposals and yield forecasts, as well as sell their harvest directly to the government without the involvement of middlemen. Crop yield is determined by soil and meteorological factors such as pH, NPK levels, temperature, rainfall, and humidity. Based on this, the system advises farmers on which crop is the most suitable and profitable, as well as the potential yield. To tackle the Small Holders' Crop Classification using Optimal Points, Values, Crop Prediction Regression Using Multiple Linear Regression, and Logistic Regression are supervised machine learning models. It eliminates the need for an intermediary to sell to buyers, allowing farmers to earn directly