Skip to main content
SpringerLink
Log in

An accurate prediction of crop yield using hybrid deep capsule auto encoder with softmax regression

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Crop yield prediction is every nation’s most important and difficult task. Due to the frequent climate changes, farmers struggle to achieve a high yield. This work presents an effective methodology for crop yield prediction of major crop production. In the presented methodology, crop yield is predicted by considering soil health data, crop production data and rainfall data. The input soil nutrients, weather and crop production data are processed. The input data is collected from the Uttar Pradesh (UP) region. In the first phase, the soil fertility index estimates the soil quality. In the second phase, the soil quality score and other crop yield related parameters like rainfall and crop production data are taken for further processing. Initially, the input soil health, crop production, and rainfall data are pre-processed. In the pre-processing stage, data cleaning with if-null processing and min-max normalization is used for data standardization. The features are then selected using the adaptive bald eagle search optimization (ABES) approach. Finally, the crop yield prediction of sugarcane, wheat and rice crops is obtained accurately by utilizing a hybrid deep capsule auto encoder with a softmax regression (Hybrid DCAS) model. Here, the hyper-parameter tuning of the presented deep learning model is achieved by a modified Flamingo Search (MFS) optimization approach. The overall implementation is carried out on PYTHON. The performance of the developed model is compared with other models in terms of classification and prediction performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article.

References

  1. Alsattar HA, Zaidan AA, Zaidan BB (2020) Novel meta-heuristic bald eagle search optimization algorithm. Artif Intell Rev 53(3):2237–2264

    Article  Google Scholar 

  2. Bhojani SH, Bhatt N (2020) Wheat crop yield prediction using new activation functions in neural network. Neural Comput & Applic 32(17):13941–13951

    Article  Google Scholar 

  3. Bouwma I, Zinngrebe Y, Runhaar H (2019) Nature conservation and agriculture: two EU policy domains that finally meet? In EU Bioeconomy Economics and Policies: Palgrave Macmillan, Cham, II: 153–175

  4. Dang C, Liu Y, Yue H, Qian J, Zhu R (2021) Autumn crop yield prediction using data-driven approaches:-support vector machines, random forest, and deep neural network methods. Can J Remote Sens 47(2):162–181

    Article  Google Scholar 

  5. Elavarasan D, Vincent PD (2020) Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE Access 8:86886–86901

    Article  Google Scholar 

  6. Elavarasan D, Vincent PM (2021) A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters. J Ambient Intell Humaniz Comput 12(11):10009–10022

    Article  Google Scholar 

  7. FAO RICE PRICE UPDATE (2018a) Food and Agriculture Organization of the United Nations (FAO), Rome

  8. Filippi P, Jones EJ, Wimalathunge NS, Somarathna PD, Pozza LE, Ugbaje SU, Jephcott TG, Paterson SE, Whelan BM, Bishop TF (2019) An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precis Agric 20(5):1015–1029

    Article  Google Scholar 

  9. Gomez-Zavaglia A, Mejuto JC, Simal-Gandara J (2020) Mitigation of emerging implications of climate change on food production systems. Food Res Int 134:109256

    Article  Google Scholar 

  10. Gong L, Yu M, Jiang S, Cutsuridis V, Pearson S (2021) Deep learning based prediction on greenhouse crop yield combined TCN and RNN. Sensors 21(13):4537

    Article  Google Scholar 

  11. Gopal PM, Bhargavi R (2019) A novel approach for efficient crop yield prediction. Comput Electron Agric 165:104968

    Article  Google Scholar 

  12. Gulati A, Terway P, Hussain S (2021) Performance of agriculture in Uttar Pradesh. Revital Indian Agric Boosting Farmer Incomes, 175

  13. Hammer RG, Sentelhas PC, Mariano JC (2020) Sugarcane yield prediction through data mining and crop simulation models. Sugar Tech 22(2):216–225

    Article  Google Scholar 

  14. Hu MF, Liu JW, Li WM (2021) Learning optimal primary capsules by information bottleneck. In international conference on artificial neural networks, springer, Cham 519-528

  15. Iaksch J, Fernandes E, Borsato M (2021) Digitalization and big data in smart farming–a review. J Management Anal 8(2):333–349

    Google Scholar 

  16. Jain S, Shukla S, Wadhvani R (2018) Dynamic selection of normalization techniques using data complexity measures. Expert Syst Appl 106:252–262

    Article  Google Scholar 

  17. Joshua V, Priyadharson SM, Kannadasan R (2021) Exploration of machine learning approaches for paddy yield prediction in eastern part of Tamilnadu. Agronomy 11(10):2068

    Article  Google Scholar 

  18. Khaki S, Wang L (2019) Crop yield prediction using deep neural networks. Front Plant Sci 10:621

    Article  Google Scholar 

  19. Khaki S, Wang L, Archontoulis SV (2020) A cnn-rnn framework for crop yield prediction. Front Plant Sci 10:1750

    Article  Google Scholar 

  20. Khalil ZH, Abdullaev SM (2021) Neural network for grain yield predicting based multispectral satellite imagery: comparative study. Procedia Comput Sci 186:269–278

    Article  Google Scholar 

  21. Lata S (2019) Irrigation water Management for Agricultural Development in Uttar Pradesh. Springer International Publishing, India

    Book  Google Scholar 

  22. Li W, Luo Y, Zhu Q, Liu J, Le J (2008) Applications of AR*-GRNN model for financial time series forecasting. Neural Comput & Applic 17(5):441–448

    Article  Google Scholar 

  23. Luo Y, Jiang J, Zhu J, Huang Q, Li W, Wang Y, Gao Y (2022) A caps-Ubi model for protein ubiquitination site prediction. Front Plant Sci, 1582

  24. Miriyala GP, Sinha AK (2020) Prediction of crop yield using deep learning techniques: a concise review. Recent Advances in Computer Based Systems, Processes and Applications, 145–159

  25. Nevavuori P, Narra N, Lipping T (2019) Crop yield prediction with deep convolutional neural networks. Comput Electron Agric 163:104859

    Article  Google Scholar 

  26. Nosratabadi S, Szell K, Beszedes B, Imre F, Ardabili S, Mosavi A (2020) Comparative analysis of ANN-ICA and ANN-GWO for crop yield prediction. In 2020 RIVF Int Conf Comput Commun Technol (RIVF) IEEE 1-5

  27. Oikonomidis A, Catal C, Kassahun A (2022) Hybrid deep learning-based models for crop yield prediction. Appl Artif Intell, 1-18

  28. Reddy DJ, Kumar MR (2021) Crop yield prediction using machine learning algorithm. In 2021 5th international conference on intelligent computing and control systems (ICICCS), IEEE, 1466-1470

  29. Saxena S, Kumar A (2019) Economic analysis of climate change impact, adaptation and mitigation on potato farming in India with special reference to Agra district. Indian J Econ Dev, 7(3)

  30. Sharma KK, Singh AK, Dubey SK (2018) Rainfall trend analysis for crop planning under rainfed conditions in district Agra of Uttar Pradesh. MAUSAM 69(4):599–606

    Article  Google Scholar 

  31. Sharma S, Rai S, Krishnan NC (2020) Wheat crop yield prediction using deep LSTM model. arXiv preprint arXiv:2011.01498

  32. Singh S (2019) Determinants of agriculture production in Uttar Pradesh, India: a regional analysis. Res Rev Int J Multidiscip 4:1–14

    Google Scholar 

  33. Sood S, Singh H (2021) Computer vision and machine learning based approaches for food security: a review. Multimed Tools Appl 80(18):27973–27999

    Article  Google Scholar 

  34. Sun J, Di L, Sun Z, Shen Y, Lai Z (2019) County-level soybean yield prediction using deep CNN-LSTM model. Sensors 19(20):4363

    Article  Google Scholar 

  35. Talaviya T, Shah D, Patel N, Yagnik H, Shah M (2020) Implementation of artificial intelligence in agriculture for optimization of irrigation and application of pesticides and herbicides. Artif Intell Agric 4:58–73

    Google Scholar 

  36. Xi E, Bing S, Jin Y (2017) Capsule network performance on complex data. arXiv preprint arXiv:1712.03480

  37. Xu X, Gao P, Zhu X, Guo W, Ding J, Li C, Zhu M, Wu X (2019) Design of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu Province, China. Ecol Indic 101:943–953

    Article  Google Scholar 

  38. Zhiheng W, Jianhua L (2021) Flamingo search algorithm: a new swarm intelligence optimization algorithm. IEEE Access 9:88564–88582

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Modinagar, Uttar Pradesh, India

    Rajneesh Kumar & Sachi Pandey

Authors
  1. Rajneesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sachi Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sachi Pandey.

Ethics declarations

Conflict of interest

Authors have no conflict of Interest to declare.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, R., Pandey, S. An accurate prediction of crop yield using hybrid deep capsule auto encoder with softmax regression. Multimed Tools Appl 82, 15371–15393 (2023). https://doi.org/10.1007/s11042-022-13919-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13919-4

Keywords

Search

Navigation