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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64097
Volume:
10 |
5 |
May 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science & Engineering
Ashokrao mane Group of institution
C
A
Computer Science & Engineering
Ashokrao mane Group of institution
Computer Science & Engineering
Ashokrao mane Group of institution
Computer Science & Engineering
Ashokrao mane Group of institution
Computer Science & Engineering
Ashokrao mane Group of institution
Conventional agriculture's reliance synthetic chemical fertilizers poses significant environmental challenges, including soil degradation, water eutrophication, and greenhouse gas emissions. This project, the Eco-Fertilization Project, addresses these issues by developing and validating sustainable, ecologically sound alternatives for soil nutrient management and crop enhancement.The research focuses on the formulation, testing, and comparative analysis of bio-fertilizers and organic soil amendments derived from naturally abundant, renewable resources (e.g., microbial cultures, composted organic waste, and mineral-rich botanical extracts). Key objectives include optimizing nutrient availability ($N, P, K$ and micronutrients) in the formulated products, quantifying the impact on soil health parameters (e.g., microbial diversity, water retention capacity, and pH balance), and evaluating the ultimate effect on target crop yield and quality under controlled and field conditions.Results demonstrate that the developed eco-fertilization strategies achieve comparable or superior crop yields to synthetic fertilizers while significantly reducing the environmental footprint. Specifically, they enhance soil organic carbon, minimize nutrient runoff, and promote a healthier rhizosphere environment. The project concludes that eco-fertilization is a viable, economically feasible, and environmentally responsible approach to ensuring future food security and transitioning towards sustainable agricultural practices.
KEYWORDS
The Eco-Fertilization System successfully demonstrates how machine learning and weather forecasting can be combined to create an intelligent, user-friendly, and eco-friendly agricultural decision-support tool. By predicting NPK nutrient requirements and analyzing upcoming weather conditions, the system helps farmers apply fertilizers at the right time and in the right quantity, reducing wastage and increasing crop productivity. The model promotes sustainable farming by preventing nutrient leaching, minimizing chemical fertilizer dependence, and ensuring environmentally responsible agricultural practices. Although the system has some limitations—such as dependency on weather APIs and lack of sensor data—it provides a strong foundation for future development. In conclusion, the Eco-Fertilization System is a valuable technological innovation that supports precision agriculture, improves fertilizer efficiency, and paves the way toward smarter and more sustainable farming methods.
1. Krutika Hampannavar, Vijay Bhajantri, “Predicting Fertilizer Consumption Using Machine Learning Techniques,” International Journal of Engineering Research, 2018. 2. Prabakaran N., Harshita M., Kiranmai G., “Prediction of Chemical Fertilizer Consumption Using Fuzzy Logic,” International Journal of Innovative Research in Computer and Communication Engineering, 2019. 3. Shital Bhojani, Nirav Bhatt, “Crop Yield Prediction Using Data Mining Techniques,” International Journal of Advanced Research in Computer Science, 2017. 4. Yin, X., Kersebaum, K.C., Kollas, C., “Modelling Soil and Crop Response to Different Soil Fertility Levels,” European Journal of Agronomy, 2018. 5. Hess, L.J.T., et al., “Rainfall Intensification Increases Nitrate Leaching,” Journal of Environmental Quality, 2017. 6. Rani, S., “Climate Change and Its Impact on Agriculture,” International Journal of Environmental Studies, 2020. 7. Yang, X., et al., “Cropping Systems and Soil Health,” Soil & Tillage Research, 2019. 8. Ahmed, N., et al., “Nutrient Recommendation Using Evolutionary Computation,” Journal of Agricultural Informatics, 2020. 9. Pant, A., et al., “Crop Pattern Recognition Using Transfer Learning,” IEEE Access, 2021. 10. Garnaik, S., et al., “Long-Term Fertilizer Experiments Using Machine Learning,” Agricultural Systems, 2021.
Publication Date
5/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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