Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{174153,
        author = {Hiresh Beria and Bondil Adithya Singh and E Preethi},
        title = {AGRO SMART: COMPARISON ANALYSYS FOR FERTILIZER AND CROP SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4605-4610},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174153},
        abstract = {This project presents a robust agricultural support system combining machine learning-powered Fertilizer and Crop Recommendation Systems to enhance farming efficiency. The Fertilizer Recommendation System analyzes critical environmental factors such as soil type, pH level, temperature, rainfall, and humidity, providing precise fertilizer suggestions to optimize crop yields. Simultaneously, the Crop Recommendation System identifies the most suitable crops based on historical performance and specific soil and climate conditions, increasing the likelihood of successful harvests. It estimates production costs, including expenses for materials, labor, and machinery, enabling farmers to plan budgets effectively. Additionally, a location-based feature connects farmers to nearby fertilizer retailers, offering information such as retailer locations, contact details, and operating hours, ensuring convenient access to essential resources. This system empowers farmers to make informed decisions, improving productivity and profitability in agriculture.},
        keywords = {Machine Learning, Precision Farming, Resource Optimization},
        month = {March},
        }