AgriSense Smart Crop and Fertilizer Prediction with Yield Forecasting and Farmer Marketplace using Machine Learning

Mucheti Sravanthi; Mugada Ratna Koushik; Shaik Abdul Khan; Masuvarapu Bhargav Naidu; Chokkakula Devi

AgriSense Smart Crop and Fertilizer Prediction with Yield Forecasting and Farmer Marketplace using Machine Learning

Author(s)	Ms. Mucheti Sravanthi, Mr. Mugada Ratna Koushik, Mr. Shaik Abdul Khan, Mr. Masuvarapu Bhargav Naidu, Ms. Chokkakula Devi
Country	India
Abstract	Agriculture is very important for the economic growth of many countries, but farmers often face difficulties in making appropriate choices because of changing weather, soil conditions, and market prices. Experience based decisions play a big role in traditional farming methods, which don't always lead to the best results. This paper introduces AgriSense, an intelligent agricultural decision support system that utilizes machine learning and explainable artificial intelligence to aid farmers in making data-driven decisions. The proposed system includes a number of modules, such as crop recommendation, fertilizer recommendation, yield prediction, and market price forecasting. It uses important agricultural factors like the type of crop, the amount of nitrogen, phosphorus, and potassium in the soil, the temperature, the humidity, and the amount of rain. To make predictions more accurate and reliable, hybrid machine learning models that use both Random Forest and XGBoost algorithms are used. These models learn from old agricultural data to find patterns and make accurate suggestions. The system also uses Explainable AI with SHapley Additive explanations (SHAP) to make sense of model predictions and give information about how important each feature is. This improves transparency and enhances user trust. The system is set up as a web-based dashboard that lets users enter data and get predictions right away. Experimental results show that AgriSense works well and is accurate across different modules, making it a good choice for precision agriculture. The proposed system's goals are to boost productivity, make better use of resources, and help farmers use sustainable methods.
Keywords	Keywords: Smart Farming, Precision Agriculture, Explainable Artificial Intelligence, XGBoost, LSTM, Hybrid Machine Learning Models, Decision Support Systems.
Field	Computer
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-06

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AgriSense Smart Crop and Fertilizer Prediction with Yield Forecasting and Farmer Marketplace using Machine Learning

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