A machine learning-based model for crop recommendation using Agro-climatic and soil nutrient parameters (Agrismart)
Year: 2025
Type: article
Abstract: Agrismart is an innovative decision support system for agriculture, utilizing machine learning to enhance crop and fertilizer recommendations. It integrates advanced data analysis techniques to process a wide range of agricultural data, including soil information and weather data. Through the analysis of soil parameters such as moisture levels, nitrogen, phosphorus, potassium content, and pH, combined with real-time weather data including temperature, humidity, and rainfall, Agrismart generates recommendations for crop selection and optimal fertilizer application. The system also provides real-time information through external APIs like weather forecast accessible via a user-friendly interface. Agrismart’s goal is to improve productivity, resource efficiency, and sustainability in agriculture. By providing farmers with data-driven recommendations, Agrismart empowers them to make informed decisions, ultimately leading to increased crop yields, reduced input costs, and a more sustainable farming practice. Moreover, Agrismart is committed to ongoing research and development, continuously refining its models and scaling its capabilities for real-world deployment. With its innovative approach, Agrismart is poised to revolutionize farming practices globally, making agriculture more efficient, sustainable, and profitable.
Authors: Rebina Ferdous, Soyaib Rahman, Salim Mia, Hafizur Rahman
Institutions: Bangladesh Jute Research Institute, Jagannath University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4411501517