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@article{193807,
        author = {PAVITHRAN S and ANBUSELEVAN V and KARTHIKEYAN B},
        title = {ECO FERTILIZATION ANALYSIS USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1766-1769},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193807},
        abstract = {Agriculture is still considered one of the key areas in food production and economic progress. In order to boost the production of crops, the farmers often rely heavily on the chemical fertilizers. Although fertilizers are used to enhance crop productivity, their overuse or misuse has adverse effects on the soil and the immediate surrounding environment. Eco-fertilization is aimed at applying fertilizers in a well balanced and efficient way according to the real requirements of plants and soil nutrients. Modern technology is evolving very fast and machine learning has begun to contribute significantly to the enhancement of farming. The agricultural data that can be analyzed by machine learning models include soil nutrients, crop type, rainfall, and temperature to prescribe the most appropriate fertilizer that can help in enhancing better growth of crops. The current review paper examines various studies on the topic of eco-fertilization and machine learning-fertilizer recommendation systems. There are also advantages, constraints and future opportunities of such intelligent systems as explicated in the paper. The paper demonstrates that data-driven approaches may assist farmers in minimizing wastes of fertilizers, enhancing crop production, and contributing to the sustainability of farming activities.},
        keywords = {Eco-fertilization, Machine Learning, Fertilizer Recommendation, Sustainable Agriculture, Smart Farming.},
        month = {March},
        }