International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 2
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Farmer crop Prediction

Author(s) Ms. Arushi Mathur, Ms. Mahi Prajapat, Ms. Dinky Lata, Ms. Malishka Pancholi, Dr. Meeta Sharma
Country India
Abstract Agriculture plays a crucial role in the Indian economy, yet farmers often struggle with selecting appropriate crops due to unpredictable weather conditions, soil variability, and lack of real-time decision support. This paper presents a Smart Crop Prediction and Agricultural Decision Support System that leverages machine learning techniques, real-time weather data integration, fertilizer recommendation, and an AI-based assistant to support farmers in making informed decisions.
The proposed system utilizes clustering-based crop prediction using soil and environmental parameters such as nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall. Additionally, the system integrates real-time weather data using an API and provides a 5-day forecast along with farmer-specific advisory. A fertilizer recommendation module suggests suitable fertilizers based on soil nutrient levels, and a natural language processing-based AI assistant enhances user interaction by answering farming-related queries.
The system is implemented using Python, Flask, and Scikit-learn, providing a user-friendly web interface. Experimental results demonstrate that the system can effectively assist farmers in crop selection and agricultural planning. This work contributes toward building
Keywords Crop Prediction, Machine Learning, Smart Agriculture, Weather API, Fertilizer Recommendation, AI Assistant, Precision Farming
Field Engineering
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-09
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E-ISSN 2582-2160
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