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All Journal AGRIVITA, Journal of Agricultural Science International Journal of Advances in Intelligent Informatics
Islam, Aminul
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Agriculture, Biological Sciences & Forestry Computer Science & IT

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Biofertilizers for Sustainable Agricultural Practice in Crop Production: A Review Samanta, Kousik; Islam, Aminul; Banik, Madhurima; Koley, Shankha
AGRIVITA Journal of Agricultural Science Vol 47, No 2 (2025)
Publisher : Faculty of Agriculture University of Brawijaya in collaboration with PERAGI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17503/agrivita.v47i2.4783

Abstract

By 2050, a projected global population of 9.7 billion will exacerbate the existing resource scarcity, demanding innovative solutions for sustainable food production. While synthetic fertilizers have boosted yields, their environmental impact—including soil and water contamination, greenhouse gas emissions, and declining response rates—is undeniable. Organic farming and the utilization of biofertilizers offer a compelling alternative. These naturally occurring microorganisms, including nitrogen-fixing bacteria and phosphorus-solubilizing bacteria, enhance soil fertility, boost crop yields, and mitigate the negative consequences of chemical fertilizers. While challenges remain regarding production, distribution, and widespread farmer adoption, the growing global demand for sustainable agriculture, coupled with ongoing research into biofertilizer optimization, paints an optimistic picture for the future of this eco-friendly technology. Indeed, the shift towards biofertilizers represents not just a solution to a pressing problem but a crucial step towards a healthier planet and more secure food supply. The findings suggest that excessive use of chemical fertilizers negatively affect agricultural ecosystems. Besides this, the use of biofertilizers offers a natural sustainable alternative solution to address the imbalance of soil nutrients.
Student Major Subject Prediction Model for Real-Application Using Neural Network Islam, Aminul; Hoque, Jesmeen Mohd Zebaral; Hossen, Md. Jakir; Basiron, Halizah; Tawsif Khan, Chy. Mohammed
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1490

Abstract

The university admission test is an arena for students in Bangladesh. Millions of students have passed the higher secondary school every year, and only limited government medical, engineering, and public universities are available to pursue their further study. It is challenging for a student to prepare all these three categories simultaneously within a short period in such a competitive environment. Selecting the correct category according to the student's capability became important rather than following the trend. This study developed a preliminary system to predict a suitable admission test category by evaluating students' early academic performance through data collecting, data preprocessing, data modelling, model selection, and finally, integrating the trained model into the real system. Eventually, the Neural Network was selected with the maximum 97.13% prediction accuracy through a systematic process of comparing with three other machine learning models using the RapidMiner data modeling tool. Finally, the trained Neural Network model has been implemented by the Python programming language for opinionating the possible option to focus as a major for admission test candidates in Bangladesh.
Co-Authors Banik, Madhurima Basiron, Halizah Hoque, Jesmeen Mohd Zebaral Hossen, Md. Jakir Koley, Shankha Samanta, Kousik Tawsif Khan, Chy. Mohammed