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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Yang Li & Xuewei Chao, 2020. "ANN-Based Continual Classification in Agriculture," Agriculture, MDPI, vol. 10(5), pages 1-15, May.
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    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.

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