Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data

In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As these variables are important to developing an ove...

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Bibliographic Details
Main Authors:	Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Carlos Antonio da Silva Junior, Regimar Garcia dos Santos, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Lucas Prado Osco, Wesley Nunes Gonçalves, Alexsandro Monteiro Carneiro, José Marcato Junior, Hemerson Pistori, Luciano Shozo Shiratsuchi
Format:	article
Language:	EN
Published:	MDPI AG 2021
Subjects:	precision agriculture multispectral remote sensing data shallow learner deep neural network Science Q
Online Access:	https://doaj.org/article/4a9e1fabf11b42c19ae48cb439e10211
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Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data

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