Crop recommendation system for growing best suitable crop

Yash Gupta and Garima Srivastava *

Amity School Of Engineering and Technology, Amity University Uttar Pradesh, Lucknow, India.
 
Review
International Journal of Science and Research Archive, 2024, 12(01), 2928–2936.
Article DOI: 10.30574/ijsra.2024.12.1.1111
Publication history: 
Received on 08 May 2024; revised on 15 June 2024; accepted on 18 June 2024
 
Abstract: 
Agriculture is critical to ensuring global food security and financial stability, but faces significant challenges such as climate change, resource scarcity and population growth. To address these issues, crop recommendation systems have proven to be valuable tools to help farmers decide which crops to plant. These systems aim to increase crop yields and make better use of resources. This report offers an in-depth look at these promising crops, covering their importance, opportunities, challenges and future prospects. By analyzing existing research and case studies, we hope to provide a clearer understanding of the current consensus and suggest areas for further study and development.
A crop recommendation system is essentially a decision-making tool for farmers. It helps them select the best crops to grow based on factors such as soil type, climate, available resources and market demand. These systems use data analytics, machine learning and agronomic knowledge to analyze input parameters provided by farmers and then generate personalized recommendations. Using historical data, weather forecasts, soil quality assessments and crop performance models, the system aims to increase agricultural productivity, reduce risk and increase profitability for farmers. By easily integrating into digital platforms and mobile applications, crop recommendation systems enable farmers to make informed decisions and adapt to changing environmental conditions, promoting sustainable agriculture and food security.
 
Keywords: 
Crop Recommendation; Recommendation System; Random Forest Model; Hybrid Model; Classification
 
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