Articles
An Analytical Hierarchy Process-Based Framework for Optimized Selection of Agro-Meteorological Indicators in Integrated Plant Disease Management Systems
Downloads
Keywords
Analytical Hierarchy Process
Agro-Meteorological Parameters
Plant Disease Management
Multi-Criteria Decision Making
Article Usage Statistics
Live Metrics
Abstract Views
230
File Views
124
PDF
0
HTML
0
Other
124
Total
354
Article Usage Chart
Abstract
File Views
PDF
HTML
Other
Total
Abstract
The increasing complexity of plant disease dynamics under changing climatic conditions necessitates the development of intelligent decision-support systems capable of optimizing agro-meteorological parameter selection. Traditional disease management approaches often rely on generalized environmental indicators, resulting in suboptimal interventions and reduced crop productivity. This study proposes an Analytical Hierarchy Process (AHP)-based framework for prioritizing agro-meteorological indicators within integrated plant disease management systems. The framework integrates multi-criteria decision-making (MCDM) principles to systematically evaluate environmental variables such as temperature, humidity, rainfall, and wind speed in relation to disease propagation.
The proposed model employs pairwise comparison matrices, eigenvector-based weighting, and consistency validation mechanisms to ensure robust decision-making. Furthermore, the framework is conceptually aligned with hybrid decision methodologies used in engineering and artificial intelligence domains, demonstrating adaptability and scalability. The methodology is validated through a hypothetical case study involving crop disease severity influenced by climatic parameters. Results indicate that the AHP-based prioritization significantly enhances predictive accuracy and resource allocation efficiency.
The study contributes to the intersection of artificial intelligence, agricultural engineering, and environmental analytics by presenting a structured and scalable decision-making architecture. Limitations and future enhancements involving hybrid AI models and real-time data integration are also discussed.
Downloads
Download data is not yet available.
References
1. G. Akman, “Evaluating suppliers to include green supplier development programs via fuzzy c-means and VIKOR methods,” Computers and Industrial Engineering, vol. 86, pp. 69-82, 2015, doi: 10.1016/j.cie.2014.10.013.
2. F. A. Al-Zahrani, “Evaluating the usable-security of healthcare software through unified technique of fuzzy logic, ANP and TOPSIS,” IEEE Access, vol. 8, pp. 109905-109916, 2020, doi: 10.1109/ACCESS.2020.3001996.
3. J. A. Alonso and M. T. Lamata, “Consistency in the analytic hierarchy process: A new approach,” International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, vol. 14, no. 4, pp. 445-459, 2006, doi: 10.1142/S0218488506004114.
4. Y. L. Alaoui, “Introduction to MCDM techniques: AHP as example,” Conference: Seminar at Islamic Financial Engineering laboratory, 2019, doi: 10.13140/RG.2.2.15536.30724.
5. C. T. Chang, W. X. Zhao, and J. Hajiyev, “An Integrated smartphone and tariff plan selection for taxiservice operators: MCDM and RStudio approach,” IEEE Access, vol. 7, pp. 31457-31472, 2019, doi: 10.1109/ACCESS.2019.2903201.
6. L. K. Dhaliwal and S. K. Sandhu, “Identification of different agro meteorological parameters responsible for enhancing sheath blight severity in the field,” Journal of Agrometeorology, vol. 14, pp. 201-206, 2012.
7. S. M. Gaikwad, P. Mulay, and R. R. Joshi, “Analytical hierarchy process to recommend an ice cream to a diabetic patient based on sugar content in it,” Procedia Computer Science, vol. 50, pp. 64-72, 2015, doi: 10.1016/j.procs.2015.04.062.
8. K. Gospel, “Implementation of an online software tool for the analytic hierarchy process (AHP-OS),” International Journal of the Analytic Hierarchy Process, vol. 10, no. 3, pp. 469-487, 2018, doi: 10.13033/ijahp.v10i3.590.
9. R. Jagabandhu and S. Saha, “Assessment of land suitability for the paddy cultivation using analytical hierarchical process (AHP): a study on Hinglo river basin, Eastern India,” Modeling Earth Systems and Environment, vol. 4, pp. 601-618, 2018.
10. Muhammad, A. Shaikh, Q. N. Naveed, and M. R. N. Qureshi, “Factors affecting academic integrity in e-learning of Saudi Arabian Universities: an investigation using Delphi and AHP,” IEEE Access, vol. 8, pp. 16259-16268, 2020, doi: 10.1109/ACCESS.2020.2967499.
11. Naseri, Hemmati, and Roghayeh, “Bean root rot management: Recommendations based on an integrated approach for plant disease control,” Rhizosphere, vol. 4, pp. 48-53, 2017, doi: 10.1016/j.rhisph.2017.07.001.
12. Naseri, “Linkages of Farmers' operations with rhizoctonia root rot spread in bean crops on a regional basis,” Journal of Phytopathology, vol. 161, no. 11-12, 2013, doi: 10.1111/jph.12140.
13. H. L. Napitupulu, “Determining the priority level of suppliers by using AHP and TOPSIS,” IOP Conference Series: Materials Science and Engineering, vol. 505, no. 1, 2019, Art. no. 012154.
14. S. Parthasarathy and S. Sharma, “Determining ERP customization choices using nominal group technique and analytical hierarchy process,” Computers in Industry, vol. 65, no. 6, pp. 1009-1017, 2014, doi: 10.1016/j.compind.2014.03.003.
15. R. Patil and S. Kumar, “A bibliometric survey on the diagnosis of plant leaf diseases using artificial intelligence,” International Journal of Modern Agriculture, vol. 9, no. 3, pp. 1111-1131, 2020.
16. R. Patil, A. Jagtap, and R. Joshi, “Integrated Management system for sugarcane disease using deep learning techniques-a review,” International Journal of Scientific and Technology Research, vol. 8, no. 11, pp. 1133-1137, 2019.
17. G. Rathee, S. Garg, G. Kaddoum, B. J. Choi, and M. S. Hossain, “Trusted orchestration for smart decision-making in internet of vehicles,” IEEE Access, vol. 8, pp. 157427-157436, 2020, doi: 10.1109/ACCESS.2020.3019795.
18. Z. Ren, Z. Xu, and H. Wang, “The strategy selection problem on artificial intelligence with an integrated VIKOR and AHP method under probabilistic dual hesitant fuzzy information,” IEEE Access, vol. 7, pp. 103979-103999, 2019, doi: 10.1109/ACCESS.2019.2931405.
19. T. L. Saaty, “Decision-making with the AHP: Why is the principal eigenvector necessary,” European Journal of Operational Research, vol. 145, no. 1, pp. 85-91, 2003, doi: 10.1016/S0377-2217(02)00227-8.
20. P. Sharma, B. K. Singh, and R. P. Singh, “Prediction of potato late blight disease based upon weather parameters using artificial neural network approach,” 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2018, pp. 1-13, doi: 10.1109/ICCCNT.2018.8494024.
21. G. Tian, H. Zhang, M. Zhou, and Z. Li, “AHP, gray correlation, and TOPSIS combined approach to green performance evaluation of design alternatives,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 7, pp. 1093-1105, 2018, doi: 10.1109/TSMC.2016.2640179.
22. X. Xu, Y. Wang, and S. Yu, “Teaching performance evaluation in smart campus,” IEEE Access, vol. 6, pp. 77754-77766, 2018, doi: 10.1109/ACCESS.2018.2884022.
23. E. Youssef, “An integrated MCDM approach for cloud service selection based on TOPSIS and BWM,” IEEE Access, vol. 8, pp. 71851-71865, 2020, doi: 10.1109/ACCESS.2020.2987111.
24. H. Zhang, G. Zhang, and Q. Jia, “Landslide susceptibility index based landslide susceptibility assessment of the pearl river,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 11, pp. 4239-4251, 2019.
25. G. Zhang, H. Jia, L. Yang, Y. Li, and J. Yang, “Research on a model of node and path selection for traffic network congestion evacuation based on complex network theory,” IEEE Access, vol. 8, pp. 7506-7517, 2020, doi: 10.1109/ACCESS.2019.2959654.