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.
Highlights
Quantitative interpretations of plant growth through descriptive models have been developed via two mathematical approaches known as classical and functional analysis [1]
This artificial neural network (ANN) showed the best performance (R = 0.97 and mean square error (MSE) = 0.107), with tansig and purelin transfer functions in the hidden layers and output layer, respectively (Table 1), while the ANN with a 10-8-5 topology in the hidden layers was built for the soil culture system (Figure 1b), which showed better performance (R = 0.95 and MSE = 0.049) with tansig and purelin transfer functions in the hidden layers and output layer, respectively (Table 1)
The employed feed-forward backpropagation ANNs with 7-10-7-5-2 and 7-10-8-5-2 topologies for the substrate and soil culture systems, respectively, and trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted, satisfactory simulated the aerial dry matter and the fresh fruit yield compared to the observed values
Summary
Quantitative interpretations of plant growth through descriptive models have been developed via two mathematical approaches known as classical and functional analysis [1]. ANNs are a nonlinear mapping structure based on the function of the human brain [2], offering learning capabilities. ANNs have been developed to build mathematical models that mimic the computing power of the human brain, with powerful processing capabilities that have been demonstrated in various real-world applications [3]. Agriculture offers many wide applications for ANNs [4,5,6,7,8]. The neuron is the basic working unit of an ANN. This neuron does not have a predefined meaning and evolves during the learning process in a manner that can characterize the target’s function [3]
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