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Open AccessArticle

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

1
Doctorado en Ciencias en Agricultura Protegida, Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico
2
Horticultura, Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico
3
CONACYT-Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico
4
Botánica, Universidad Autónoma Agraria Antonio Narro, Saltillo 25315, Mexico
5
Tecnológico Nacional de México, I. T. Saltillo, Saltillo 25280, Mexico
*
Author to whom correspondence should be addressed.
Agriculture 2020, 10(4), 97; https://doi.org/10.3390/agriculture10040097
Received: 19 February 2020 / Revised: 20 March 2020 / Accepted: 24 March 2020 / Published: 1 April 2020
(This article belongs to the Special Issue Innovative Agronomic Practices for Maximizing Crop Growth and Yield)
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. View Full-Text
Keywords: soft computing; simulation model; tomato yield; dry weight; training; validation soft computing; simulation model; tomato yield; dry weight; training; validation
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López-Aguilar, K.; Benavides-Mendoza, A.; González-Morales, S.; Juárez-Maldonado, A.; Chiñas-Sánchez, P.; Morelos-Moreno, A. Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter. Agriculture 2020, 10, 97.

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