Non-Destructive Methods Based on Machine Learning for the Prediction of Sweet Potato Leaf Area: A Comparative Approach

Non-Destructive Methods Based on Machine Learning for the Prediction of Sweet Potato Leaf Area: A Comparative Approach

Leaf area is an essential parameter for studies of plant growth and physiology and is considered one of the main parameters for agricultural production. Leaf area determination methods are fundamental to understanding and predicting crop productivity. They can be classified as destructive or non-des...

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Main Authors: Joao Everthon Da Silva Ribeiro, Ester Dos Santos Coelho, Antonio Gideilson Correia Da Silva, Pablo Henrique De Almeida Oliveira, Elania Freire Da Silva, Gisele Lopes Dos Santos, Anna Kezia Soares De Oliveira, John Victor Lucas Lima, Walter Esfrain Pereira, Lindomar Maria Da Silveira, Aurelio Paes Barros
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Ipomoea batatas (L.) Lam
machine learning algorithms
non-destructive analysis
precision agriculture
leaf dimensions
Online Access:https://ieeexplore.ieee.org/document/10925361/
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