IDEAS home Printed from https://ideas.repec.org/a/eee/telpol/v46y2022i6s0308596122000726.html
   My bibliography  Save this article

Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan

Author

Listed:
  • Ahmed, Moiz Uddin
  • Hussain, Iqbal

Abstract

New avenues of technological opportunities in agriculture are opening as we are further delving deeper into the 21st century, but at the same time, new challenges are emerging. One of these challenges is the growing quantity of food demand, which is highly vital for regional trade, food security, and meeting the nutritious requirements of the population. A timely prediction with accuracy about crop yield could be valuable for greater food production and maintainability of sustainable agricultural growth. This paper presents a predictive model of wheat production using machine learning. The northern areas of Pakistan which grow wheat are selected as a case study due to their importance in the country's agricultural sector. We collected data of five years and selected the best attribute subset related to crop production. We applied twelve (12) algorithms by dividing data samples into three sets. Experimental results helped to shortlist three algorithms for the final analysis i.e. Sequential Minimal Optimization Regression (SMOreg), Multilayer Processing (MLP) and Gaussian Process (GP). The Root Mean Square (RMSE) and Percentage Absolute Difference (PAD) metrics were used to validate the results. The SMOreg obtained the lowest PAD (0.0093) and RMSE (0.5552) values. MLP was a little closer with second-lowest PAD (0.0116) and RMSE (0.737) value. The performance of GP was found lowest due to higher PAD (0.2203) and RMSE (17.7423) values. Our findings confirm the predictive ability of machine learning algorithms on a crop dataset recorded in a localized environment, which could be replicated on other crops and regions.

Suggested Citation

  • Ahmed, Moiz Uddin & Hussain, Iqbal, 2022. "Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan," Telecommunications Policy, Elsevier, vol. 46(6).
  • Handle: RePEc:eee:telpol:v:46:y:2022:i:6:s0308596122000726
    DOI: 10.1016/j.telpol.2022.102370
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308596122000726
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.telpol.2022.102370?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhenling Cui & Hongyan Zhang & Xinping Chen & Chaochun Zhang & Wenqi Ma & Chengdong Huang & Weifeng Zhang & Guohua Mi & Yuxin Miao & Xiaolin Li & Qiang Gao & Jianchang Yang & Zhaohui Wang & Youliang Y, 2018. "Pursuing sustainable productivity with millions of smallholder farmers," Nature, Nature, vol. 555(7696), pages 363-366, March.
    2. Schwartz, Robert C. & Domínguez, Alfonso & Pardo, José J. & Colaizzi, Paul D. & Baumhardt, R. Louis & Bell, Jourdan M., 2020. "A crop coefficient –based water use model with non-uniform root distribution," Agricultural Water Management, Elsevier, vol. 228(C).
    3. Alam, Gazi Mahabubul, 2021. "Does online technology provide sustainable HE or aggravate diploma disease? Evidence from Bangladesh—a comparison of conditions before and during COVID-19," Technology in Society, Elsevier, vol. 66(C).
    4. Rashid Menhas & Shahid Mahmood & Papel Tanchangya & Muhammad Nabeel Safdar & Safdar Hussain, 2019. "Sustainable Development under Belt and Road Initiative: A Case Study of China-Pakistan Economic Corridor’s Socio-Economic Impact on Pakistan," Sustainability, MDPI, vol. 11(21), pages 1-24, November.
    5. Jialing Yu & Jian Wu, 2018. "The Sustainability of Agricultural Development in China: The Agriculture–Environment Nexus," Sustainability, MDPI, vol. 10(6), pages 1-17, May.
    6. Alam, Gazi Mahabubul & Parvin, Morsheda, 2021. "Can online higher education be an active agent for change? —comparison of academic success and job-readiness before and during COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    7. A. Suruliandi & G. Mariammal & S.P. Raja, 2021. "Crop prediction based on soil and environmental characteristics using feature selection techniques," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 27(1), pages 117-140, January.
    8. Huaiyang Zhong & Xiaocheng Li & David Lobell & Stefano Ermon & Margaret L. Brandeau, 2018. "Hierarchical modeling of seed variety yields and decision making for future planting plans," Environment Systems and Decisions, Springer, vol. 38(4), pages 458-470, December.
    9. Jig Han Jeong & Jonathan P Resop & Nathaniel D Mueller & David H Fleisher & Kyungdahm Yun & Ethan E Butler & Dennis J Timlin & Kyo-Moon Shim & James S Gerber & Vangimalla R Reddy & Soo-Hyung Kim, 2016. "Random Forests for Global and Regional Crop Yield Predictions," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
    10. Khadijeh Alibabaei & Pedro D. Gaspar & Tânia M. Lima, 2021. "Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling," Energies, MDPI, vol. 14(11), pages 1-21, May.
    11. Kopper, Sarah A. & Jayne, Thomas S. & Snapp, Sieglinde S., 2020. "Sifting through the weeds: Understanding heterogeneity in fertilizer and labor response in Central Malawi," Ecological Economics, Elsevier, vol. 169(C).
    12. Prabakaran, G. & Vaithiyanathan, D. & Ganesan, Madhavi, 2021. "FPGA based effective agriculture productivity prediction system using fuzzy support vector machine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 1-16.
    13. Yin, Xiaogang & Kersebaum, Kurt Christian & Kollas, Chris & Manevski, Kiril & Baby, Sanmohan & Beaudoin, Nicolas & Öztürk, Isik & Gaiser, Thomas & Wu, Lianhai & Hoffmann, Munir & Charfeddine, Monia & , 2017. "Performance of process-based models for simulation of grain N in crop rotations across Europe," Agricultural Systems, Elsevier, vol. 154(C), pages 63-77.
    14. Kim, Daeha & Chun, Jong Ahn & Inthavong, Thavone, 2021. "Managing climate risks in a nutrient-deficient paddy rice field using seasonal climate forecasts and AquaCrop," Agricultural Water Management, Elsevier, vol. 256(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nida Iqbal & Muhammad Umair Shahzad & El-Sayed M. Sherif & Muhammad Usman Tariq & Javed Rashid & Tuan-Vinh Le & Anwar Ghani, 2024. "Analysis of Wheat-Yield Prediction Using Machine Learning Models under Climate Change Scenarios," Sustainability, MDPI, vol. 16(16), pages 1-26, August.
    2. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    3. Ayse Yavuz Ozalp & Halil Akinci, 2023. "Evaluation of Land Suitability for Olive ( Olea europaea L.) Cultivation Using the Random Forest Algorithm," Agriculture, MDPI, vol. 13(6), pages 1-22, June.
    4. Nurhuda Nizar & Ahmad Danial Zainudin & Ali Albada & Chua Mei Shan, 2024. "Forecasting Short-Term FTSE Bursa Malaysia Using WEKA," Information Management and Business Review, AMH International, vol. 16(2), pages 104-114.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).
    2. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    3. Silva, J.F. & Santos, J.L. & Ribeiro, P.F. & Marta-Pedroso, C. & Magalhães, M.R. & Moreira, F., 2024. "A farming systems approach to assess synergies and trade-offs among ecosystem services," Ecosystem Services, Elsevier, vol. 65(C).
    4. Lu, Jie & Bai, Zhaohai & Velthof, Gerard L. & Wu, Zhiguo & Chadwick, David & Ma, Lin, 2019. "Accumulation and leaching of nitrate in soils in wheat-maize production in China," Agricultural Water Management, Elsevier, vol. 212(C), pages 407-415.
    5. Yu, Yanan & He, Yong & Zhao, Xuan, 2021. "Impact of demand information sharing on organic farming adoption: An evolutionary game approach," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    6. Fuhong Zhang & Apurbo Sarkar & Hongyu Wang, 2021. "Does Internet and Information Technology Help Farmers to Maximize Profit: A Cross-Sectional Study of Apple Farmers in Shandong, China," Land, MDPI, vol. 10(4), pages 1-18, April.
    7. Lijuan Du & Li Xu & Yanping Li & Changshun Liu & Zhenhua Li & Jefferson S. Wong & Bo Lei, 2019. "China’s Agricultural Irrigation and Water Conservancy Projects: A Policy Synthesis and Discussion of Emerging Issues," Sustainability, MDPI, vol. 11(24), pages 1-20, December.
    8. Taiba Zahid & Fouzia Gillani & Usman Ghafoor & Muhammad Raheel Bhutta, 2022. "Synchromodal Transportation Analysis of the One-Belt-One-Road Initiative Based on a Bi-Objective Mathematical Model," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    9. Xuelan Li & Rui Guan, 2023. "How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?," IJERPH, MDPI, vol. 20(2), pages 1-23, January.
    10. Dorijan Radočaj & Ante Šiljeg & Rajko Marinović & Mladen Jurišić, 2023. "State of Major Vegetation Indices in Precision Agriculture Studies Indexed in Web of Science: A Review," Agriculture, MDPI, vol. 13(3), pages 1-16, March.
    11. Yang, Wenjie & Li, Yanhang & Jia, Bingli & Liu, Lei & Yuan, Aijing & Liu, Jinshan & Qiu, Weihong, 2024. "Optimized fertilization based on fallow season precipitation and the Nutrient Expert system for dryland wheat reduced environmental risks and increased economic benefits," Agricultural Water Management, Elsevier, vol. 291(C).
    12. Zhang, Bangbang & Li, Xian & Chen, Haibin & Niu, Wenhao & Kong, Xiangbin & Yu, Qiang & Zhao, Minjuan & Xia, Xianli, 2022. "Identifying opportunities to close yield gaps in China by use of certificated cultivars to estimate potential productivity," Land Use Policy, Elsevier, vol. 117(C).
    13. Liang Chi & Mengshuai Zhu & Chen Shen & Jing Zhang & Liwei Xing & Xiangyang Zhou, 2023. "Does the Winner Take All in E-Commerce of Agricultural Products under the Background of Platform Monopoly?," Agriculture, MDPI, vol. 13(2), pages 1-16, February.
    14. Sagit Barel-Shaked, 2023. "Network-based business model in the agri-food sector: A case study of Green Fingers," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(4), pages 162-170.
    15. Ustaoglu, E. & Sisman, S. & Aydınoglu, A.C., 2021. "Determining agricultural suitable land in peri-urban geography using GIS and Multi Criteria Decision Analysis (MCDA) techniques," Ecological Modelling, Elsevier, vol. 455(C).
    16. Agarwal, Vernika & Malhotra, Snigdha & Dagar, Vishal & M. R, Pavithra, 2023. "Coping with public-private partnership issues: A path forward to sustainable agriculture," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    17. Shirzad, Hossein & Barati, Ali Akbar & Ehteshammajd, Shaghayegh & Goli, Imaneh & Siamian, Narges & Moghaddam, Saghi Movahhed & Pour, Mahdad & Tan, Rong & Janečková, Kristina & Sklenička, Petr & Azadi,, 2022. "Agricultural land tenure system in Iran: An overview," Land Use Policy, Elsevier, vol. 123(C).
    18. Barbara Breza-Boruta & Justyna Bauza-Kaszewska, 2023. "Effect of Microbial Preparation and Biomass Incorporation on Soil Biological and Chemical Properties," Agriculture, MDPI, vol. 13(5), pages 1-19, April.
    19. Xiaolin Yang & Jinran Xiong & Taisheng Du & Xiaotang Ju & Yantai Gan & Sien Li & Longlong Xia & Yanjun Shen & Steven Pacenka & Tammo S. Steenhuis & Kadambot H. M. Siddique & Shaozhong Kang & Klaus But, 2024. "Diversifying crop rotation increases food production, reduces net greenhouse gas emissions and improves soil health," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    20. Tianyue Ma & Jing Li & Shuang Bai & Fangzhe Chang & Zhai Jiang & Xingguang Yan & Jiahao Shao, 2022. "Optimization and Construction of Ecological Security Patterns Based on Natural and Cultivated Land Disturbance," Sustainability, MDPI, vol. 14(24), pages 1-19, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:telpol:v:46:y:2022:i:6:s0308596122000726. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30471/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.