Data/hora: 02/06/2024 02:14:25

Provedor de dados: 	111	
País: 	China	
Título: 	Development of a smart machine-vision-based system to detect water stress in greenhouse tomato plants	
Autores: 	LAK, MohammadBagher.	
Data: 	2021-06-26	
Ano: 	2021	
Palavras-chave: 	Image classification; MLPNN; Normalization; PCA; Precision farming.	
Resumo: 	Timely detection of water stress in agricultural crops is important. In this paper, a smart classification algorithm was developed to detect water stress in tomato plants that were grown in the greenhouse. During the growth period, thermal and visible light images were acquired from the canopy tops in two states: (1) plants in normal conditions; and (2) plants under water stress. Images were obtained using a camera that recorded simultaneous frames of thermal and visible (red, green, and blue (RGB)) features. Based on these features, 22 parameters were defined and applied to classify the image frames. In order to develop an efficient algorithm, principal component analysis (PCA) was applied to optimize the classifying of parameters. For normalizing the data in PCA, 6 normalization methods were applied and assessed. Among them, peak normalization was the best as its PC1 and PC2 described 94% and 5% of total variation, respectively. Based on the PCA results, 9 parameters were found with most loadings as the most effective indexes that all obtained from the visible features. In other words, the thermal features were not as useful for detecting plant water stress. These parameters were used in multilayer perceptron neural networks (MLPNN) to develop the classification algorithm. The resulting mean-square error and r values for the MLPNN with ten hidden layer were 6.05×10-3 and 0.9905, respectively which shows the robustness of the classification algorithm. This algorithm accuracy was 83.3%.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.cigrjournal.org/index.php/Ejounral/article/view/6343	
Editor: 	International Commission of Agricultural and Biosystems Engineering	
Relação: 	http://www.cigrjournal.org/index.php/Ejounral/article/view/6343/3609	
Formato: 	application/pdf	
Fonte: 	Agricultural Engineering International: CIGR Journal; Vol. 23 No. 2 (2021): CIGR Journal

1682-1130	
Fonte: 	Copyright (c) 2021 Agricultural Engineering International: CIGR Journal