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

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

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 thre...

Full description

Bibliographic Details
Main Authors: Kelvin López-Aguilar, Adalberto Benavides-Mendoza, Susana González-Morales, Antonio Juárez-Maldonado, Pamela Chiñas-Sánchez, Alvaro Morelos-Moreno
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Agriculture
Subjects:
soft computing
simulation model
tomato yield
dry weight
training
validation
Online Access:https://www.mdpi.com/2077-0472/10/4/97

Internet

https://www.mdpi.com/2077-0472/10/4/97

Similar Items