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Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

Author

Listed:
  • Kelvin López-Aguilar

    (Doctorado en Ciencias en Agricultura Protegida, Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico)

  • Adalberto Benavides-Mendoza

    (Horticultura, Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico)

  • Susana González-Morales

    (CONACYT-Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico)

  • Antonio Juárez-Maldonado

    (Botánica, Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico)

  • Pamela Chiñas-Sánchez

    (Tecnológico Nacional de México, I. T. Saltillo, Saltillo 25280, Mexico)

  • Alvaro Morelos-Moreno

    (CONACYT-Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico)

Abstract

Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 ( v / v ) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop.

Suggested Citation

  • Kelvin López-Aguilar & Adalberto Benavides-Mendoza & Susana González-Morales & Antonio Juárez-Maldonado & Pamela Chiñas-Sánchez & Alvaro Morelos-Moreno, 2020. "Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter," Agriculture, MDPI, vol. 10(4), pages 1-14, April.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:4:p:97-:d:339774
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    References listed on IDEAS

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    1. Kaul, Monisha & Hill, Robert L. & Walthall, Charles, 2005. "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, Elsevier, vol. 85(1), pages 1-18, July.
    2. Zhang, WenJun & Bai, ChangJun & Liu, GuoDao, 2007. "Neural network modeling of ecosystems: A case study on cabbage growth system," Ecological Modelling, Elsevier, vol. 201(3), pages 317-325.
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    Cited by:

    1. Jolanta Wawrzyniak, 2020. "Application of Artificial Neural Networks to Assess the Mycological State of Bulk Stored Rapeseeds," Agriculture, MDPI, vol. 10(11), pages 1-19, November.
    2. Kazuya Maeda & Dong-Hyuk Ahn, 2021. "Estimation of Dry Matter Production and Yield Prediction in Greenhouse Cucumber without Destructive Measurements," Agriculture, MDPI, vol. 11(12), pages 1-10, November.
    3. Elzbieta Czembor & Zygmunt Kaczmarek & Wiesław Pilarczyk & Dariusz Mańkowski & Jerzy H. Czembor, 2022. "Simulating Spring Barley Yield under Moderate Input Management System in Poland," Agriculture, MDPI, vol. 12(8), pages 1-20, July.
    4. Wang, Rong & Sun, Zhaojun & Yang, Dongyan & Ma, Ling, 2022. "Simulating cucumber plant heights using optimized growth functions driven by water and accumulated temperature in a solar greenhouse," Agricultural Water Management, Elsevier, vol. 259(C).

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