Enhancing Crop Yield Prediction and Provide Direct Market Access to Farmers Using Web Technologies and Machine Learning Models

Agriculture plays a vital role in this modern world because it provides food productivity. However, among the challenges responsible for this trend, very few people practice agriculture because of lack of proper information on crops that are best suited to current soil and weather conditions. Many farmers fail to maintain adequate profits on their yields mainly because of a lack of direct markets, which often results in cheaper sale prices for the produce and greater reliance on intermediaries. To address such difficulties, our project will include the implementation of an integrated web-based system designed to predict crop yield utilizing a machine learning model. Using algorithms like LightGBM, KNN Regressor, Gradient Boosting, and CatBoost, it predict which crops are suitable for today's agriculture. It further allows farmers to have a direct connection with customers enabling that they can sell their produce directly to the customers without any broker or middleman. Improvement in farmers' profit margins is supposed to go hand in hand with serving consumers fresh products at cost. The web application is designed in modern frontend as well as in backend technologies making it will be easy in use and approachable for farmer. Consequently, this initiative should improve productivity in agriculture, providing actionable information for farmers; in the end, it would facilitate a more efficient access strategy in markets to agricultural producers.