Intelligent Agronomic Advisory Model for the Prediction of Best Crop Yields

International Journal of Computer Science and Engineering

© 2024 by SSRG - IJCSE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Samuel Awuna Kile, Ogar Temunaya Ofut, Collins Udanor

10.14445/23488387/IJCSE-V11I8P108

How to Cite?

Samuel Awuna Kile, Ogar Temunaya Ofut, Collins Udanor, "Intelligent Agronomic Advisory Model for the Prediction of Best Crop Yields," SSRG International Journal of Computer Science and Engineering , vol. 11,  no. 8, pp. 61-72, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I8P108

Abstract:

Agriculture contributes approximately 40% to Nigeria's GDP, primarily driven by smallholder farmers who face challenges in standardization, decision support, and precision. This study developed an intelligent agronomic advisory model to predict optimal crop yields. Objectives included modeling farm planning workflows, presenting an intelligent agronomic model, developing a predictive system, and comparing its performance with existing systems. The study utilized analytical and empirical methodologies, employing colored petri nets and artificial neural networks for planning and prediction. The system was developed using PyCharm, Python, and MariaDB. Metrics for accuracy, speed, cost, soil pH, soil texture, and crop yield were measured. The new system achieved results of 0.25 MMRE, 600 seconds, 121.14% ROI, soil pH of 6.0, soil texture of 12-25mm, and 7.0 tons/ha crop yield, outperforming existing systems. The developed system can significantly aid smallholder farmers in enhancing crop yields, reducing poverty, and ensuring food security.

Keywords:

Advisory, Agronomic, Artificial neural networks, Colored petri nets, Intelligent, Prediction.

References:

[1] Mirzam Hasanuzzaman, Introduction to Agriculture and Agronomy, Lecture Notes on Agriculture and Agronomy, pp. 1-8, 2019. [Online]. Available: https://hasanuzzaman.weebly.com/uploads/9/3/4/0/934025/introduction_to_agriculture_and_agronomy.pdf
[2] Jock R Anderson, and Gershon Feder, “Agricultural Extension: Good Intentions and Hard Realities,” The World Bank Research Observer, vol. 19, no. 1, pp. 41-60, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[3] S.K Haruna, and Y.M.G. Abdullahi, “Training of Public Extension Agents in Nigeria and the Implications for Government’s Agricultural Transformation Agenda,” Journal of Agricultural Extension, vol. 17, no. 2, pp. 98-104, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Pawan Dahiya et al., “Intelligent Systems: Features, Challenges, Techniques, Applications and Future Scope,” Intelligent Systems and Mobile Adhoc Networks, pp. 1-7, 2007.
[Google Scholar]
[5] R. Klump, What are Intelligent Systems?, 2019. [Online] Available: https://online.lewisu.edu/mscs/resources/what-are-intelligent-systems
[6] Brandon A. Beemer, and Dawn G. Gregg, “Advisory Systems to Support Decision Making,” Handbook on Decision Support Systems 1, pp. 511-527, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] James L. Peterson, “Petri Nets,” ACM Computing Survey, vol. 9, no. 3, pp. 223-251, 1977.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Hasan Hosseini-Nasab, and Ali Sadri, “Using Stochastic Colored Petri Nets for Designing Multi-purpose Plants,” Engineering, vol. 4, no. 10, pp. 655-661, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Snehal S. Dahikar, and Sandeep V.Rode, “Agricultural Crop Yield Prediction Using Artificial Neural Network Approach,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 2, no. 1, pp. 683-686, 2014.
[Google Scholar] [Publisher Link]
[10] Sudhanshu Sekhar Panda, Daniel P. Ames, and Suranjan Panigrahi, “Application of Vegetation Indices for Agricultural Crop Yield Prediction using Neural Network Techniques,” Remote Sensing, vol. 2, no. 3, pp. 673-696, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Petteri Nevavuori, Nathaniel Narra, and Tarmo Lipping, “Crop Yield Prediction with Deep Convolutional Neural Networks,” Computers and Electronics in Agriculture, vol. 163, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Saeed Khaki, Lizhi Wang, and Sotirios V. Archontoulis, “A CNN-RNN Framework for Crop Yield Prediction,” Frontiers in Plant Science, vol. 10, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Lars Michael Kristensen, Jens Baek Jørgense, and Kurt Jensen, “Application of Coloured Petri Nets in System Development,” Lectures on Concurrency and Petri Nets, pp. 626-685, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[14] N. Shramenko, O. Pavlenko, and D. Muzylyov, “Information and Communication Technology: Case of using Petri Nets for Grain Delivery Simulation at Logistics Systems,” Proceedings of the 2nd International Workshop on Computer Modeling and Intelligent Systems, pp. 935- 949, 2019.
[Google Scholar] [Publisher Link]
[15] Senlin Guan, Morikazu Nakamura, and Takeshi Shikanai, Hybrid Petri nets and Metaheuristics Approach to Farm Work Scheduling, Advances in Petri Nets Theory and Applications, pp. 137-152, 2010.
[Google Scholar] [Publisher Link]
[16] Siti Khairunniza-Bejo, Samihah Mustaffha, and Wan Ishak Wan Ismail, “Application of Artificial Neural Network in Predicting Crop Yield: A Review,” Journal of Food Science and Engineering, vol. 4, pp. 1-9, 2014.
[Google Scholar]
[17] Fei Liu, and Monika Heiner, “Colored Petri Nets to Model and Simulate Biological Systems,” Recent Advances in Petri Nets and Concurrency, CEUR Workshop Proceedings, vol. 827, pp. 71-85, 2012.
[Google Scholar]
[18] Nandini Kavanal Balakrishnan, “Application of Artificial Neural Network and Colored Petri Nets on Earthquake Resilient Water Distribution Systems,” Master Thesis, Missouri University of Science and Technology, pp. 1-93, 2008.
[Google Scholar] [Publisher Link]
[19] Abdulbasit Ahmed, Sunday Eric Adewumi, and Victoria Yemi-Peters, “Crop Yield Prediction in Nigeria using Machine Learning Techniques: A Case Study of Southern Part of Nigeria,” UMYU Scientifica, vol. 2 no. 4, pp. 31-38, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Ersin Elbasi et al., “Crop Prediction Model using Machine Learning Algorithms,” Applied Sciences, vol. 13, no. 16, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Dhvanil Bhagat, Shrey Shah, and Rajeev Kumar Gupta, “Crop Yield Prediction using Machine Learning Approaches,” Machine Learning, Image Processing, Network Security and Data Sciences, pp. 63-74, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Rene David, and Hassane Alla, “On Hybrid Petri Nets,” Discrete Event Dynamic Systems, vol. 11, pp. 9-40, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Ivan Jordanov, “Artificial Intelligence (AI). Neural Networks,” Erasmus Presentation, University of Uppsala, pp. 1-48, 2012.[Publisher Link]