IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i2p267-d748592.html
   My bibliography  Save this article

A Deep Learning-Based Model to Reduce Costs and Increase Productivity in the Case of Small Datasets: A Case Study in Cotton Cultivation

Author

Listed:
  • Mohammad Amin Amani

    (School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran 1417614411, Iran)

  • Francesco Marinello

    (Department of Land, Environment, Agriculture and Forestry, University of Padova, 35020 Legnaro, Italy)

Abstract

In this paper, a deep-learning model is proposed as a viable approach to optimize the information on soil parameters and agricultural variables’ effect in cotton cultivation, even in the case of small datasets. In this study, soil is analyzed to reduce the planting costs by determining the various combinations of soil components and nutrients’ precise amounts. Such factors are essential for cotton cultivation, since their amounts are often not precisely defined, and especially traditional farming methods are characterized by excessive distribution volumes producing significant economic and environmental impact. Not only can artificial intelligence decrease the charges, but it also increases productivity and profits. For this purpose, a deep learning algorithm was selected among other machine learning algorithms by comparison based on the accuracy metric to build the predictive model. This model gets the combination of the factors amounts as input and predicts whether the cotton growth will be successful or not. The predictive model was built by this algorithm based on 13 physical and chemical factors has 98.8% accuracy.

Suggested Citation

  • Mohammad Amin Amani & Francesco Marinello, 2022. "A Deep Learning-Based Model to Reduce Costs and Increase Productivity in the Case of Small Datasets: A Case Study in Cotton Cultivation," Agriculture, MDPI, vol. 12(2), pages 1-13, February.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:267-:d:748592
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/2/267/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/2/267/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang Li & Xuewei Chao, 2020. "ANN-Based Continual Classification in Agriculture," Agriculture, MDPI, vol. 10(5), pages 1-15, May.
    2. Joanna Pluto-Kossakowska, 2021. "Review on Multitemporal Classification Methods of Satellite Images for Crop and Arable Land Recognition," Agriculture, MDPI, vol. 11(10), pages 1-16, October.
    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. Mohammad Amin Amani & Mohammad Mahdi Nasiri, 2023. "A novel cross docking system for distributing the perishable products considering preemption: a machine learning approach," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-32, July.

    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. Alper Taner & Yeşim Benal Öztekin & Hüseyin Duran, 2021. "Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut," Sustainability, MDPI, vol. 13(12), pages 1-13, June.
    2. Tan Wang & Xianbao Xu & Cong Wang & Zhen Li & Daoliang Li, 2021. "From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production," Agriculture, MDPI, vol. 11(2), pages 1-26, February.
    3. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    4. Claudia C. Tusell-Rey & Oscar Camacho-Nieto & Cornelio Yáñez-Márquez & Yenny Villuendas-Rey & Ricardo Tejeida-Padilla & Carmen F. Rey Benguría, 2022. "A Priori Determining the Performance of the Customized Naïve Associative Classifier for Business Data Classification Based on Data Complexity Measures," Mathematics, MDPI, vol. 10(15), pages 1-19, August.
    5. Dong-Chong Hsiou & Fay Huang & Fu Jie Tey & Tin-Yu Wu & Yi-Chuan Lee, 2022. "An Automated Crop Growth Detection Method Using Satellite Imagery Data," Agriculture, MDPI, vol. 12(4), pages 1-25, April.
    6. Igor Teixeira & Raul Morais & Joaquim J. Sousa & António Cunha, 2023. "Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review," Agriculture, MDPI, vol. 13(5), pages 1-24, April.
    7. Awe, Olushina Olawale & Dias, Ronaldo, 2022. "Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 14(4), 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:gam:jagris:v:12:y:2022:i:2:p:267-:d:748592. 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.