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March 25, 20260 citationsOpen Access

An Integrated Mobile Application for Agricultural Schemes, Weather Forecasting, and Fertilizer Prediction

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KPKanishka S, Madhumitha R, Mr. Prabhu

Key Points

  • To develop a comprehensive digital tool that aids farmers in making informed decisions regarding agricultural practices.
  • Developed an integrated web-based application combining weather forecasts and fertilizer recommendations.
  • Utilized a meteorological API for accurate five-day weather predictions.
  • Employed a decision tree algorithm to analyze soil nutrients for fertilizer recommendations.
  • Created a user-friendly interface using HTML, CSS, and JavaScript, with a Python-Flask backend.
  • Provided detailed information on agricultural schemes and eligibility criteria.
  • Achieved accurate weather forecasts with historical data analysis.
  • Recommended optimal fertilizer types and application quantities based on soil nutrient content.

Abstract

The growing demand for technology-driven farming has prompted the development of intelligent digital platforms to support farmers in effective decision-making. This paper presents an integrated web-based Smart Agriculture Assistance Application that combines agricultural scheme information, weather forecasting, and soil-based fertilizer recommendations into a unified digital platform. The system provides comprehensive details about central and state government agricultural schemes, including eligibility criteria, benefits, and application procedures, enabling farmers to make informed financial and developmental decisions. The weather forecasting module retrieves historical climate data and generates accurate five-day predictions using a trusted meteorological API. A key feature is the Fertilizer Recommendation Module, which employs the Decision Tree algorithm to analyze soil nutrient content—Nitrogen, Phosphorus, and Potassium—and recommends the most suitable fertilizer type and optimal application quantity. The application is developed using HTML, CSS, JavaScript for the frontend and Python-Flask for the backend, offering a practical and accessible decision-support tool aimed at enhancing agricultural productivity and sustainability.

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Cite This Study

Kanishka S, Madhumitha R, Mr. Prabhu (2026) studied this question.

synapsesocial.com/papers/69c37bb3b34aaaeb1a67e673https://doi.org/10.5281/zenodo.19179309
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