Activating the role of Artificial Intelligence for Optimised Resource Management in light of Meta-Goal Programming for Sustainable Agriculture
DOI:
https://doi.org/10.62933/25636x68Abstract
Optimising sustainable agriculture has become a vital objective for human survival, while economic progress has become the prime aim. The rapid population increase has to be faced by producing more from fewer resources. Thus, a comprehensive approach can be equipped to manage resources under-uncertainty conditions. This paper presents an integrated approach of Meta-Goal Programming with Artificial Intelligence (AI) to optimise resources in Malaysian sustainable agriculture. It has focused on rice and oil palm cultivation. Dual challenges have been addressed by this integrated approach, resource optimisation and environmental sustainability. A case study conducted to optimise resource-scarce agriculture has demonstrated significant improvements in water efficiency, crop yields, and ecological balance. This paper contributes a novel perspective to sustainable agriculture. Predictions have shown robust results which are aligned with actual data fields in terms of accuracy, 98% and 97% for rice and oil palm yield, respectively. The optimisation process has improved crop yields by reducing the usage of water and fertiliser use by 14% and 4% for rice and oil palm yield, respectively. The results validated the efficacy of the proposed approach, achieving the sustainable agriculture goals.
References
[1] Adebunmi Okechukwu Adewusi, Onyeka Franca Asuzu, Temidayo Olorunsogo, Chinwe Iwuanyanwu, Ejuma Adaga, and Donald Obinna Daraojimba. Ai in precision agriculture: A review of technologies for sustainable farming practices. World Journal of Advanced Research and Reviews, 21(1):2276–2285, 2024.
[2] Valérie Angeon, Marion Casagrande, Mireille Navarrete, and Rodolphe Sabatier. A conceptual frame-work linking ecosystem services, socio-ecological systems and socio-technical systems to understand the relational and spatial dynamics of the reduction of pesticide use in agrifood systems. Agricultural Systems, 213:103810, 2024.
[3] Amirhossein Barzigar, SM Hosseinalipour, and Arun S Mujumdar. Ai-driven optimization of hybrid solar-wind dryers for climate-resilient agriculture, 2025.
[4] Showkat Ahmad Bhat and Nen-Fu Huang. Big data and ai revolution in precision agriculture: Survey and challenges. Ieee Access, 9:110209–110222, 2021.
[5] Animesh Biswas and Bijay Baran Pal. Application of fuzzy goal programming technique to land use planning in agricultural system. Omega, 33(5):391–398, 2005.
[6] Petros Chavula and Fredrick Kayusi. Systematic review on the application of nanotechnology and artificial intelligence in agricultural economics. LatIA, (3):322, 2025.
[7] Daniele De Wrachien. Land use planning: a key to sustainable agriculture. In Conservation agriculture: environment, farmers experiences, innovations, socio-economy, policy, pages 471–483. Springer, 2003.
[8] FAO. Overcoming water challenges in agriculture, 2020.
[9] Enock Musau Gideon. Sustainable agriculture leveraging artificial intelligence systems in kenya’s agri-food supply chain: Leveraging artificial intelligence systems in kenya’s agri-food supply chain. Agricultural Science, 7(2):153–171, 2024.
[10] Chabi Gupta and Alex Khang. Cultivating efficiency-harnessing artificial intelligence (ai) for sustainable agriculture supply chains. In Agriculture and Aquaculture Applications of Biosensors and Bioelectronics, pages 375–391. IGI Global Scientific Publishing, 2024.
[11] Manya Gupta, Gargi Mishra, Supriya Bajpai, Abhinav Bhardwaj, and Milind Gautam. Ai in agriculture: A comprehensive exploration of technological transformation. Emerging Smart Agricultural Practices Using Artificial Intelligence, pages 105–132, 2025.
[12] Amaresh Jha and Sanjeev Ratna Singh. Bridging the divide: capacity building for ai adoption in developing countries. In AI strategies for social entrepreneurship and sustainable economic development, pages 133–150. IGI Global Scientific Publishing, 2025.
[13] M Johnson, R White, and J Green. Ai for sustainable agriculture: Challenges and opportunities. Journal of Agricultural Technology, 15(1):45–58, 2019.
[14] Andreas Kamilaris and Francesc X Prenafeta-Boldú. Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147:70–90, 2018.
[15] Prasann Kumar and Debjani Choudhury. Innovative technologies for effective water resources management. Water Crises and Sustainable Management in the Global South, pages 555–594, 2024.
[16] Konstantinos G Liakos, Patrizia Busato, Dimitrios Moshou, Simon Pearson, and Dionysis Bochtis. Machine learning in agriculture: A review. Sensors, 18(8):2674, 2018.
[17] Hao W Lin, SV Nagalingam, and GCI Lin. An interactive meta-goal programming-based decision analysis methodology to support collaborative manufacturing. Robotics and Computer-Integrated Manufacturing, 25(1):135–154, 2009.
[18] Bijay Baran Pal and Somsubhra Gupta. A goal programming approach for solving interval valued multiobjective fractional programming problems using genetic algorithm. In 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems, pages 1–6. IEEE, 2008.
[19] Cristina Bianca Pocol, Peter Šedı́k, Ioan Sebastian Brumă, Antonio Amuza, and Aurica Chirsanova. Organic beekeeping practices in romania: Status and perspectives towards a sustainable development. Agriculture, 11(4):281, 2021.
[20] Jules Pretty, Tim G Benton, Zareen Pervez Bharucha, Lynn V Dicks, Cornelia Butler Flora, H Charles J Godfray, Dave Goulson, Sue Hartley, Nic Lampkin, Carol Morris, et al. Global assessment of agricultural system redesign for sustainable intensification. Nature Sustainability, 1(8):441–446, 2018.
[21] Redmond R Shamshiri, Cornelia Weltzien, Ibrahim A Hameed, Ian J Yule, Tony E Grift, Siva K Balasundram, Lenka Pitonakova, Desa Ahmad, and Girish Chowdhary. Research and development in agricultural robotics: A perspective of digital farming. 2018.
[22] Adrian Ramirez-Nafarrate, Ozgur M Araz, and John W Fowler. Decision assessment algorithms for location and capacity optimization under resource shortages. Decision Sciences, 52(1):142–181, 2021.
[23] Abhinav Sharma, Arpit Jain, Prateek Gupta, and Vinay Chowdary. Machine learning applications for precision agriculture: A comprehensive review. IEEe Access, 9:4843–4873, 2020.
[24] SB Sinha, KA Rao, and BK Mangaraj. Fuzzy goal programming in multi-criteria decision systems: a case study in agricultural planning. Socio-Economic Planning Sciences, 22(2):93–101, 1988.
[25] Tanha Talaviya, Dhara Shah, Nivedita Patel, Hiteshri Yagnik, and Manan Shah. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial intelligence in agriculture, 4:58–73, 2020.
[26] Lakmali Weerasena, Douglas Shier, David Tonkyn, Mark McFeaters, and Christopher Collins. A sequential approach to reserve design with compactness and contiguity considerations. Ecological Modelling, 478:110281, 2023.
[27] Zheng-Yun Zhuang and Amine Hocine. Meta goal programing approach for solving multi-criteria denovo programing problem. European journal of operational research, 265(1):228–238, 2018.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ibrahim Zeghaiton Chaloob (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Licensed under a CC-BY license: https://creativecommons.org/licenses/by-nc-sa/4.0/
