Skip to main content
SpringerLink
Log in

Neural Network Model for Predicting Apple Yield Based on Arrival of Phenological Stage in Conjunction with Leaf disease, Soil and Weather Parameters

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Forecasting the yield of a vital crop-like apples needs to be done in a way that is both accurate and dependable to facilitate effective planning and management. There have been attempts made to forecast apple production, the majority of which have involved the utilisation of statistical methods that contain a restricted number of indicator factors or fruit counting techniques which required technical expertise and advance equipments. The suggested method utilises a neural network (NN) to forecast the yield of apple crops in an orchard. This prediction is made by considering many factors, such as the identification of phenological stage, as well as the timing of their arrival. In addition, the system takes into account soil, weather and leaf disease characteristics, which interact with the aforementioned factors. The proposed approach aims to estimate crop production across five distinct classes throughout various phenological phases. This prediction would consider factors, such as the timing of phenological stage emergence (early emergence, regular emergence, or late emergence), soil parameters, weather-related parameters, and presence or absence of leaf disease. The proposed yield prediction model shows a f1-score of 0.91, precision of 0.91, recall of 0.92, and accuracy of 92%. It is compared with other popular classical machine learning (ML) algorithms, such as Random Forest, Decision Tree, and Gradient Boosting. The challenge lies in the process of automatically predicting yields in orchards. Despite the considerable efforts dedicated to the development of automated techniques for yield estimation, the prevailing methods employed at now rely on fruit counting, which proves to be effective just within a narrow timeframe of 1–4 weeks before to harvest. This model will assist agricultural producers in making timely decisions on the implementation of contingency plans in the event of subpar or minimal yields.

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

Similar content being viewed by others

References

  1. FAOSTAT Apple Crop database. Published online 2022. https://www.fao.org/faostat/en/#data/QCL. Accessed 3 July 2023.

  2. Kour K, Gupta D, Gupta K, Juneja S, Kaur M, Alharbi AH, Lee H-N. Controlling agronomic variables of saffron crop using IoT for sustainable agriculture. Sustainability. 2022;14(9):5607.

    Article  Google Scholar 

  3. Kour K, Gupta D, Gupta K, Anand D, Elkamchouchi DH, Pérez-Oleaga CM, Ibrahim M, Goyal N. Monitoring ambient parameters in the IoT precision agriculture scenario: an approach to sensor selection and hydroponic saffron cultivation. Sensors. 2022;22:8905.

    Article  Google Scholar 

  4. Li M, Guo J, He J, et al. Possible impact of climate change on apple yield in Northwest China. Theoret Appl Climatol. 2020;139(1–2):191–203.

    Article  Google Scholar 

  5. RK Pachauri LA Meyer 2014 Climate change 2014: Synthesis report contribution of working groups I II and III to the Fifth assessment report of the intergovernmental panel on climate change IPCC Geneva Switzerland 2014 151

  6. Rai R, Joshi S, Roy S, Singh O, Chandra A. Implications of changing climate on productivity of temperate fruit crops with special reference to apple. J Hortic. 2015;2(2):1000135.

    Google Scholar 

  7. Roy P, Kislay A, Plonski PA, Luby J, Isler V. Vision-based preharvest yield mapping for apple orchards. Comput Electron Agric. 2019;16(4): 104897.

    Article  Google Scholar 

  8. Tian Y, Yang G, Wang Z, Wang H, Li E, Liang Z. Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Comput Electron Agric. 2019;157:417–26.

    Article  Google Scholar 

  9. Yu H, Song S, Ma S, Sinnott RO. Estimating Fruit Crop Yield through Deep Learning. In: Proceedings of 6th IEEE/ACM International Conference on Big Data Computing Applications and Technologies—BDCAT New Zealand. 2019. p. 145–148.

  10. Gutiérrez S, Wendel A, Underwood J. Ground based hyperspectral imaging for extensive mango yield estimation. Comput Electron Agric. 2019;157:126–35.

    Article  Google Scholar 

  11. Apolo-Apolo OE, Martínez-Guanter J, Egea G, Raja P, Pérez-Ruiz M. Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. Eur J Agron. 2020;115:126030.

    Article  Google Scholar 

  12. Kang H, Chen C. Fast implementation of real-time fruit detection in apple orchards using deep learning. Comput Electron Agric. 2020;168:105108.

    Article  Google Scholar 

  13. Gené-Mola J, et al. Fruit detection yield prediction and canopy geometric characterization using LiDAR with forced air flow. Comput Electron Agric. 2020;168:105121.

    Article  Google Scholar 

  14. Oikonomidis A, Catal C, Kassahun A. Hybrid deep learning-based models for crop yield prediction. Appl Artif Intell. 2022;36(1):2031822.

    Article  Google Scholar 

  15. Gao X, Han W, Hu Q, et al. Planting age identification and yield prediction of apple orchard using time-series spectral endmember and logistic growth model. Remote Sensing. 2023;15(3):642.

    Article  Google Scholar 

  16. Torgbor BA, Rahman MM, Brinkhoff J, Sinha P, Robson A. Integrating remote sensing and weather variables for mango yield prediction using a machine learning approach. Remote Sensing. 2023;15(12):3075.

    Article  Google Scholar 

  17. Han X, Chang L, Wang N, Kong W, Wang C. Effects of meteorological factors on apple yield based on multilinear regression analysis: a case study of Yantai Area, China. Atmosphere. 2023;14(1):183.

    Article  Google Scholar 

  18. Yıldırım Ş, Ulu B. Deep learning based apples counting for yield forecast using proposed flying robotic system. Sensors. 2023;23(13):6171.

    Article  Google Scholar 

  19. Bal F, Kayaalp F. A novel deep learning-based hybrid method for the determination of productivity of agricultural products: apple case study. IEEE Access. 2023;11:7808–21.

    Article  Google Scholar 

  20. Hahn L, Basso C, Moura-Bueno JM, et al. Yield prediction models for ‘Royal Gala’ and ‘Fuji Suprema’ Apple varieties cultivated under a subtropical climate. Agronomy. 2023;13(2):514.

    Article  Google Scholar 

  21. Singha C, Gulzar S, Swain KC, Pradhan D. Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India. J Appl Remote Sens. 2023;17(1): 014505.

    Article  Google Scholar 

  22. Fei S, Hassan MA, Xiao Y, et al. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precision Agric. 2023;24(1):187–212.

    Article  Google Scholar 

  23. Kuradusenge M, Hitimana E, Hanyurwimfura D, et al. Crop Yield prediction using machine learning models: case of Irish potato and maize. Agriculture. 2023;13(1):225.

    Article  Google Scholar 

  24. Ren Y, Li Q, Du X, et al. Analysis of corn yield prediction potential at various growth phases using a process-based model and deep learning. Plants. 2023;12(3):446.

    Article  Google Scholar 

  25. Hu J, Fan C, Wang Z, Ruan J, Wu S. Fruit detection and counting in apple orchards based on improved Yolov7 and multi-object tracking methods. Sensors. 2023;23(13):5903.

    Article  Google Scholar 

  26. Bharti DP, Banerjee R, Ahmad T, Devi S, Verma G. Artificial neural network based apple yield prediction using morphological characters. Horticulturae. 2023;9(4):436.

    Article  Google Scholar 

  27. Saddik A, Latif R, Abualkishik AZ, El Ouardi A, Elhoseny M. Sustainable yield prediction in agricultural areas based on fruit counting approach. Sustain. 2023;15(3):2707.

    Article  Google Scholar 

  28. Bali N, Singla A. Deep learning based wheat crop yield prediction model in Punjab region of North India. Appl Artif Intell. 2021;35(15):1304–28.

    Article  Google Scholar 

  29. Torsoni GB, de Oliveira Aparecido LE, dos Santos GM, Chiquitto AG, da Silva Cabral Moraes JR, de Souza Rolim G,. Soybean yield prediction by machine learning and climate. Theor Appl Climatol. 2023;151(3):1709–25.

    Article  Google Scholar 

  30. Biswal SS, Srivastava A, Sinha A, Rath A. Apple Leaf Disease Research Dataset. Kaggle. Published online 2023. https://www.kaggle.com/datasets/souravbiswal09/apple-leaf-disease-research/data.

  31. Sharma A, Kumawat LK. Understanding flowering phenology and productivity of apple in response to climate change in prime apple growing areas of the country. ICAR—Central Institute of Temperate Horticulture Srinagar India 2020 manuscript published by ICAR-CITH Srinagar.

Download references

Author information

Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India

    Rakesh Mohan Datt & Vinay Kukreja

Authors
  1. Rakesh Mohan Datt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vinay Kukreja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rakesh Mohan Datt.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

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

This article is part of the topical collection “Diverse Applications in Computing, Analytics and Networks” guest edited by Archana Mantri and Sagar Juneja.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Datt, R.M., Kukreja, V. Neural Network Model for Predicting Apple Yield Based on Arrival of Phenological Stage in Conjunction with Leaf disease, Soil and Weather Parameters. SN COMPUT. SCI. 5, 141 (2024). https://doi.org/10.1007/s42979-023-02463-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02463-z

Keywords

Search

Navigation