Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Indonesian Journal of Sustainable Agriculture and Environmental Sciences
Muhammad Madnee
The Islamia University of Bahawalpur Pakistan
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Environmental Science

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Harnessing Nanotechnology for Environmental Sustainability: Applications, Challenges, and Future Perspectives Areeba Sagheer; Muhammad Iqbal; Duaa Sagheer; Areesha Sagheer; Muhammad Madnee; Hussain Ahmed Makki; Intazar Ali; Nugraha Akbar Nurrochmat
Indonesian Journal of Sustainable Agriculture and Environmental Sciences (IJSAES) Vol. 2 No. 1 (2026): Indonesian Journal of Sustainable Agriculture and Environmental Sciences (IJSAE
Publisher : CV. Truly Science Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65896/ijsaes.v2i1.25

Abstract

Background: Nanotechnology has emerged as a transformative and rapidly advancing field with significant potential to address pressing environmental challenges. Owing to the unique properties of nanoparticles, particularly their high surface area-to-volume ratio and enhanced reactivity, nanotechnology offers innovative solutions across multiple environmental sectors. Aim: This review aims to explore the applications, advantages, challenges, and future perspectives of nanotechnology in promoting environmental sustainability. Methods: A comprehensive literature review approach was employed to examine the role of nanotechnology in key areas, including climate change mitigation, wastewater treatment, sustainable agriculture, food quality enhancement, and civil engineering. Conclusion: Nanotechnology provides cost-effective, efficient, and sustainable solutions for reducing environmental pollutants, improving resource management, and enhancing ecosystem resilience. However, concerns regarding environmental toxicity, long-term impacts, and regulatory challenges highlight the need for further research, risk assessment, and the development of robust regulatory frameworks to ensure its safe and widespread implementation.
Artificial Intelligence Adoption in Smart Agriculture: A PRISMA-Based Systematic Comparative Review across Australia, South Korea, Indonesia and Pakistan Muhammad Faizan Khan; Muhammad Tariq Nawaz; Bambang Hendro Trisasongko; Muhammad Madnee; Nimrah Ameen
Indonesian Journal of Sustainable Agriculture and Environmental Sciences (IJSAES) Vol. 2 No. 2 (2026): Indonesian Journal of Sustainable Agriculture and Environmental Sciences (IJSAE
Publisher : CV. Truly Science Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65896/ijsaes.v2i2.38

Abstract

Background: Artificial intelligence (AI) is steadily changing the agricultural systems around the world. However, its adoption remains uneven between developed and developing economies, creating significant inequality in productivity, technological access and food security. Aim: This systematic review determines the adaptation of AI in agriculture across Australia, South Korea, Indonesia and Pakistan. Prior systematic reviews of AI in agriculture have largely examined technical performance within single countries or technology categories, without directly comparing adoption pathways across countries at different stages of economic development. This review addresses that gap. Methods: This study addresses a critical research gap by following PRISMA 2020 guidelines. A systematic search of the Scopus database showed 940 records. Out of these, only 47 peer-reviewed studies (2015-2024) were included after screening for qualitative synthesis and thematic analysis. Extracted data were coded and grouped into four themes aligned with the review objectives, and the percentages reported below reflect the proportion of the 47 included studies in which each theme, technology, barrier or benefit was identified during coding. Results: The most prominently reported AI approaches in the studies were Machine learning (51.1%), precision farming (48.9%) and computer vision (46.8%). Developed economies demonstrated advanced integration of precision agriculture and automation. Developing economies mainly use AI for disease detection, crop monitoring and yield forecasting. The most common challenges reported were the high cost of implementation (38.3%), limited infrastructure (36.2%) and poor data quality (31.9%). The most frequently reported benefits were improved crop yields (61.7%), better resource use efficiency (51.1%) and more effective disease management (46.8%). These percentages show how many studies reported each benefit, but they do not indicate the actual level of improvement achieved. Conclusion: Overall, the findings suggest that the adoption of AI depends not only on the availability of technology but also on infrastructure readiness, supportive policies and strong institutional capacity. This review highlights the need for context-specific strategies, greater investment in rural digital infrastructure and inclusive innovation frameworks to support fair and sustainable agricultural development.
Co-Authors Areeba Sagheer Areesha Sagheer Bambang Hendro Trisasongko Duaa Sagheer Hussain Ahmed Makki Intazar Ali Muhammad Faizan Khan Muhammad Iqbal Muhammad Tariq Nawaz Nimrah Ameen Nugraha Akbar Nurrochmat