Machine Learning for Non-Destructive Prediction of Sunflower Leaf Area

Machine Learning for Non-Destructive Prediction of Sunflower Leaf Area

Leaf area is an important parameter in plant growth, physiology, and productive potential studies. However, its measurement using traditional methods can be limited. The search for non-destructive approaches based on leaf dimensions is essential, and machine learning offers promising alternatives fo...

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Main Authors: Joao Everthon da Silva Ribeiro, Antonio Gideilson Correia da Silva, Pablo Henrique de Almeida Oliveira, Josiana Micarla da Silva Oliveira, Alessandra Nunes da Silva, John Victor Lucas Lima, Ivan Euzebio da Silva, Ester Dos Santos Coelho, Isaque de Oliveira Leite, Elania Freire da Silva, Toshik Iarley da Silva, Lindomar Maria da Silveira, Aurelio Paes Barros Junior
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Helianthus annuus L.
artificial neural networks
regression models
alometric models
precision agriculture
Online Access:https://ieeexplore.ieee.org/document/11108270/
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Summary:Leaf area is an important parameter in plant growth, physiology, and productive potential studies. However, its measurement using traditional methods can be limited. The search for non-destructive approaches based on leaf dimensions is essential, and machine learning offers promising alternatives for accurate estimation. Therefore, the present study aimed to develop and compare linear regression models and machine learning algorithms for the non-destructive prediction of leaf area in four sunflower cultivars (BRS 323, Altis 99, Sany 66, and BRS 442). The best-performing model was also made available through an interactive and freely accessible web interface. Models based on artificial neural networks (ANN), random forest, and linear regression were developed using leaf length and width as input variables, and leaf area as the output. Model performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Willmott&#x2019;s agreement index (d). The ANN model with a 2-4-1 architecture showed the highest accuracy (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =0.9937$ </tex-math></inline-formula>; RMSE =1.1060; MAE =0.7851; MAPE =4.7916; <inline-formula> <tex-math notation="LaTeX">$d =0.9984$ </tex-math></inline-formula>), outperforming the other models. This ANN model was subsequently implemented in the web interface, offering an efficient, non-destructive tool for estimating sunflower leaf area based on linear dimensions.
ISSN:2169-3536

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