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

Kelvin López-Aguilar, Adalberto Benavides-Mendoza, Susana González-Morales, Antonio Juárez-Maldonado, Pamela Chiñas-Sánchez and Alvaro Morelos-Moreno
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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

Agriculture, 2020, vol. 10, issue 4, 1-14

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.

Keywords: soft computing; simulation model; tomato yield; dry weight; training; validation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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