IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i4p513-d1362204.html
   My bibliography  Save this article

Hybrid Deep Neural Networks with Multi-Tasking for Rice Yield Prediction Using Remote Sensing Data

Author

Listed:
  • Che-Hao Chang

    (Department of Computer Science and Engineering, National Chung Hsing University, No. 145, Xingda Rd., South District, Taichung 40227, Taiwan)

  • Jason Lin

    (Department of Computer Science and Engineering, National Chung Hsing University, No. 145, Xingda Rd., South District, Taichung 40227, Taiwan)

  • Jia-Wei Chang

    (Department of Computer Science and Engineering, National Chung Hsing University, No. 145, Xingda Rd., South District, Taichung 40227, Taiwan)

  • Yu-Shun Huang

    (Crop Science Division, Agricultural Research Institute, Council of Agriculture, Executive Yuan, Taichung 41362, Taiwan)

  • Ming-Hsin Lai

    (Crop Science Division, Agricultural Research Institute, Council of Agriculture, Executive Yuan, Taichung 41362, Taiwan)

  • Yen-Jen Chang

    (Department of Computer Science and Engineering, National Chung Hsing University, No. 145, Xingda Rd., South District, Taichung 40227, Taiwan)

Abstract

Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybrid deep learning model that coordinates the outcomes of a classification model and a regression model in deep learning via the shared layers to predict the rice crop yield. Three statistical analyses on the features, including Pearson correlation coefficients (PCC), Shapley additive explanations (SHAP), and recursive feature elimination with cross-validation (RFECV), are proposed to select the most relevant ones for the predictive goal to reduce the model training time. The data preprocessing normalizes the features of the collected data into specific ranges of values and then reformats them into a three-dimensional matrix. As a result, the root-mean-square error (RMSE) of the proposed model in rice yield prediction has achieved 344.56 and an R-squared of 0.64. The overall performance of the proposed model is better than the other deep learning models, such as the multi-parametric deep neural networks (MDNNs) (i.e., RMSE = 370.80, R-squared = 0.59) and the artificial neural networks (ANNs) (i.e., RMSE = 550.03, R-squared = 0.09). The proposed model has demonstrated significant improvement in the predictive results of distinguishing high yield from low yield with 90% accuracy and 94% F1 score.

Suggested Citation

  • Che-Hao Chang & Jason Lin & Jia-Wei Chang & Yu-Shun Huang & Ming-Hsin Lai & Yen-Jen Chang, 2024. "Hybrid Deep Neural Networks with Multi-Tasking for Rice Yield Prediction Using Remote Sensing Data," Agriculture, MDPI, vol. 14(4), pages 1-21, March.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:4:p:513-:d:1362204
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/4/513/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/4/513/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ekaansh Khosla & Ramesh Dharavath & Rashmi Priya, 2020. "Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5687-5708, August.
    2. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
    3. 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.
    4. Yu, Xinran & Ergan, Semiha & Dedemen, Gokmen, 2019. "A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    3. 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).
    4. Gómez, Patricia & Shaikh, Nazrul I. & Erkoc, Murat, 2024. "Continuous improvement in the efficient use of energy in office buildings through peers effects," Applied Energy, Elsevier, vol. 360(C).
    5. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).
    6. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    7. Hong, Yejin & Yoon, Sungmin & Choi, Sebin, 2023. "Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality," Energy, Elsevier, vol. 265(C).
    8. Cai, Qingsen & Luo, XingQi & Wang, Peng & Gao, Chunyang & Zhao, Peiyu, 2022. "Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application," Applied Energy, Elsevier, vol. 305(C).
    9. Yu, Xinran & Ergan, Semiha & Dedemen, Gokmen, 2019. "A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Indy Man Kit Ho & Anthony Weldon & Jason Tze Ho Yong & Candy Tze Tim Lam & Jaime Sampaio, 2023. "Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement," IJERPH, MDPI, vol. 20(10), pages 1-15, May.
    11. Helder Fraga & Teresa R. Freitas & Marco Moriondo & Daniel Molitor & João A. Santos, 2024. "Determining the Climatic Drivers for Wine Production in the Côa Region (Portugal) Using a Machine Learning Approach," Land, MDPI, vol. 13(6), pages 1-16, May.
    12. Florian Schierhorn & Max Hofmann & Taras Gagalyuk & Igor Ostapchuk & Daniel Müller, 2021. "Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages," Climatic Change, Springer, vol. 169(3), pages 1-19, December.
    13. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
    14. Devkota, Mina & Yigezu, Yigezu Atnafe, 2020. "Explaining yield and gross margin gaps for sustainable intensification of the wheat-based systems in a Mediterranean climate," Agricultural Systems, Elsevier, vol. 185(C).
    15. Puyu Feng & Bin Wang & De Li Liu & Hongtao Xing & Fei Ji & Ian Macadam & Hongyan Ruan & Qiang Yu, 2018. "Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia," Climatic Change, Springer, vol. 147(3), pages 555-569, April.
    16. Shine, P. & Scully, T. & Upton, J. & Murphy, M.D., 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine," Applied Energy, Elsevier, vol. 250(C), pages 1110-1119.
    17. Li Fan & Shibo Fang & Jinlong Fan & Yan Wang & Linqing Zhan & Yongkun He, 2024. "Rice Yield Estimation Using Machine Learning and Feature Selection in Hilly and Mountainous Chongqing, China," Agriculture, MDPI, vol. 14(9), pages 1-18, September.
    18. Martin Kuradusenge & Eric Hitimana & Damien Hanyurwimfura & Placide Rukundo & Kambombo Mtonga & Angelique Mukasine & Claudette Uwitonze & Jackson Ngabonziza & Angelique Uwamahoro, 2023. "Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize," Agriculture, MDPI, vol. 13(1), pages 1-19, January.
    19. Zhang, Shuyang & Zhang, Lun & Zhang, Xiaosong, 2022. "Clustering based on dynamic time warping to extract typical daily patterns from long-term operation data of a ground source heat pump system," Energy, Elsevier, vol. 249(C).
    20. Chunyan Wang & Hanying Jiang & Hao Wu & Yi Liu & Siyue Guo & Ming Xu, 2023. "Scaling in urban building energy use and its influencing factors," Journal of Industrial Ecology, Yale University, vol. 27(4), pages 1076-1088, August.

    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:gam:jagris:v:14:y:2024:i:4:p:513-:d:1362204. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.