International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025
p-ISSN: 2395-0072
www.irjet.net
AgroAdvisor: A Smart Agricultural Assistant for Crop Price Forecasting and Fertility-Based Crop Recommendation. Prof. Puneeth S P 1, Akash G V2, C R Ajjaiah3, Hemalata S Shedad4, Manohar H Koppad5 1Assistant Professor, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Karnataka, India
2,3,4,5 Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and
Technology, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------them choose suitable crops and plan their market strategy Abstract - An Agriculture in India remains highly effectively.
vulnerable to unpredictable market prices and inconsistent soil fertility conditions. Farmers often rely on experiencebased decisions rather than data-driven insights, resulting in economic losses and reduced productivity. AgroAdvisor is an intelligent web-based system designed to support farmers through two key capabilities: crop price forecasting and fertility-based crop recommendation. The system uses deep learning and machine learning techniques to analyze historical market data, soil nutrient levels (N, P, K), pH, and regional climatic conditions. Forecasting models predict future crop prices using time-series patterns, while classification models recommend suitable crops based on soil fertility and environmental factors. The platform also offers a simple and accessible user interface for farmers to input soil parameters, view insights, and make informed cultivation decisions. AgroAdvisor aims to bridge the technological gap in agriculture by providing personalized, intelligent, and actionable guidance, ultimately improving farmers’ productivity and profitability.
1.1 Intelligent Crop Recommendation The AgroAdvisor system uses a trained Decision Tree classifier to analyze soil nutrient values such as Nitrogen, Phosphorus, Potassium, and pH. By learning patterns from agricultural datasets, it identifies the crops that match specific soil profiles. This model not only evaluates raw values but also interprets nutrient balance, deficiency, and soil acidity before suggesting crops.
1.2 Soil Fertility–Based Decision Support
Key Words: Machine Learning, Crop Recommendation, Price Forecasting, Soil Fertility, Agriculture, Deep Learning, Time-Series Analysis, Smart Farming.
AgroAdvisor focuses heavily on soil fertility as the foundation for decision-making. The system evaluates the nutrient levels provided by the user and determines whether the soil conditions are optimal for particular crops. It reduces the chances of nutrient mismatch, which can negatively affect crop growth. By understanding soil fertility, the platform encourages farmers to grow crops best suited to their land rather than following traditional but unsuitable practices.
1.INTRODUCTION
1.3 Data-Driven Price Forecasting
Agriculture is one of the most critical sectors of the Indian economy, yet farmers continue to face challenges such as uncertain market prices, poor soil knowledge, and unpredictable climatic conditions. Traditional methods of crop selection often depend on experience or generalized advisory services, which fail to provide personalized guidance. With advancements in machine learning and data analytics, there is an increasing opportunity to support farmers through intelligent decision-support systems. AgroAdvisor is a smart agricultural assistant designed to enhance the decision-making process by combining soil fertility analysis with crop price forecasting. By taking soil nutrient parameters such as Nitrogen (N), Phosphorus (P), Potassium (K), and pH, along with climatic factors and historical price trends, the system provides actionable insights to farmers. It helps
The price forecasting component is powered by Random Forest Regression, which analyzes historical mandi price data. It identifies seasonal trends, market fluctuations, and growth patterns over time. This helps predict future crop prices with considerable accuracy. Farmers can plan cultivation cycles, choose profitable crops, and decide the right time to sell their produce. The forecasting model reduces reliance on middlemen who often manipulate price. It empowers farmers with data-driven insights, helping them make informed financial decisions.
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1.4 User-Friendly Web Interface The platform features an intuitive and simple interface designed for farmers with minimal technical knowledge. It allows users to enter soil values easily and receive instant recommendations. The dashboard is visually appealing, with charts, graphs, and color-coded indicators for better
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