Abstract
Agriculture plays a crucial role in the economic stability of many nations, and optimizing crop selection is essential for enhancing agricultural productivity and sustainability. The "Crop Recommender System Using Machine Learning Approach" aims to leverage machine learning techniques to provide precise crop recommendations based on various environmental and soil conditions. By incorporating factors such as soil composition, pH level, temperature, humidity, rainfall, and geographic location, this system suggests the most suitable crops for a given area. The system utilizes machine learning models, particularly Random Forest and Decision Trees, to analyze historical agricultural data, predict optimal crops, and improve the decision-making process for farmers. By training the model on large datasets, it ensures accurate predictions that align with real-world agricultural practices. The application of this system can lead to higher crop yields, sustainable farming practices, and reduced risks associated with poor crop choices. Through rigorous evaluation using standard classification metrics, the model's performance demonstrates its potential to revolutionize farming practices by aiding farmers in making informed decisions. The system has the potential to be an invaluable tool for agricultural consultants, farmers, and policymakers, ensuring long-term sustainability and improved productivity.
Citations
IRE Journals:
B Harshita , Nayana N , Kaveri P , Mamatha C , Sumalatha
"The Smart Crop Recommendation" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 1110-1116
IEEE:
B Harshita , Nayana N , Kaveri P , Mamatha C , Sumalatha
"The Smart Crop Recommendation" Iconic Research And Engineering Journals, 9(3)
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