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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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	precision agriculture multispectral remote sensing data shallow learner deep neural network Science Q
Acceso en línea:	https://doaj.org/article/4a9e1fabf11b42c19ae48cb439e10211
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spelling	oai:doaj.org-article:4a9e1fabf11b42c19ae48cb439e102112021-11-25T18:54:58ZPredicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data10.3390/rs132246322072-4292https://doaj.org/article/4a9e1fabf11b42c19ae48cb439e102112021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4632https://doaj.org/toc/2072-4292In 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 overall precision farming model, we propose a machine learning approach to predict DM, PH, and GY for soybean cultivars based on multispectral bands. The field experiment considered 524 genotypes of soybeans in the 2017/2018 and 2018/2019 growing seasons and a multitemporal–multispectral dataset collected by embedded sensor in an unmanned aerial vehicle (UAV). We proposed a multilayer deep learning regression network, trained during 2000 epochs using an adaptive subgradient method, a random Gaussian initialization, and a 50% dropout in the first hidden layer for regularization. Three different scenarios, including only spectral bands, only vegetation indices, and spectral bands plus vegetation indices, were adopted to infer each variable (PH, DM, and GY). The DL model performance was compared against shallow learning methods such as random forest (RF), support vector machine (SVM), and linear regression (LR). The results indicate that our approach has the potential to predict soybean-related variables using multispectral bands only. Both DL and RF models presented a strong (r surpassing 0.77) prediction capacity for the PH variable, regardless of the adopted input variables group. Our results demonstrated that the DL model (r = 0.66) was superior to predict DM when the input variable was the spectral bands. For GY, all machine learning models evaluated presented similar performance (r ranging from 0.42 to 0.44) for each tested scenario. In conclusion, this study demonstrated an efficient approach to a computational solution capable of predicting multiple important soybean crop variables based on remote sensing data. Future research could benefit from the information presented here and be implemented in subsequent processes related to soybean cultivars or other types of agronomic crops.Paulo Eduardo TeodoroLarissa Pereira Ribeiro TeodoroFábio Henrique Rojo BaioCarlos Antonio da Silva JuniorRegimar Garcia dos SantosAna Paula Marques RamosMayara Maezano Faita PinheiroLucas Prado OscoWesley Nunes GonçalvesAlexsandro Monteiro CarneiroJosé Marcato JuniorHemerson PistoriLuciano Shozo ShiratsuchiMDPI AGarticleprecision agriculturemultispectral remote sensing datashallow learnerdeep neural networkScienceQENRemote Sensing, Vol 13, Iss 4632, p 4632 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	precision agriculture multispectral remote sensing data shallow learner deep neural network Science Q
spellingShingle	precision agriculture multispectral remote sensing data shallow learner deep neural network Science Q 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 Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data
description	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 overall precision farming model, we propose a machine learning approach to predict DM, PH, and GY for soybean cultivars based on multispectral bands. The field experiment considered 524 genotypes of soybeans in the 2017/2018 and 2018/2019 growing seasons and a multitemporal–multispectral dataset collected by embedded sensor in an unmanned aerial vehicle (UAV). We proposed a multilayer deep learning regression network, trained during 2000 epochs using an adaptive subgradient method, a random Gaussian initialization, and a 50% dropout in the first hidden layer for regularization. Three different scenarios, including only spectral bands, only vegetation indices, and spectral bands plus vegetation indices, were adopted to infer each variable (PH, DM, and GY). The DL model performance was compared against shallow learning methods such as random forest (RF), support vector machine (SVM), and linear regression (LR). The results indicate that our approach has the potential to predict soybean-related variables using multispectral bands only. Both DL and RF models presented a strong (r surpassing 0.77) prediction capacity for the PH variable, regardless of the adopted input variables group. Our results demonstrated that the DL model (r = 0.66) was superior to predict DM when the input variable was the spectral bands. For GY, all machine learning models evaluated presented similar performance (r ranging from 0.42 to 0.44) for each tested scenario. In conclusion, this study demonstrated an efficient approach to a computational solution capable of predicting multiple important soybean crop variables based on remote sensing data. Future research could benefit from the information presented here and be implemented in subsequent processes related to soybean cultivars or other types of agronomic crops.
format	article
author	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
author_facet	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
author_sort	Paulo Eduardo Teodoro
title	Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data
title_short	Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data
title_full	Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data
title_fullStr	Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data
title_full_unstemmed	Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data
title_sort	predicting days to maturity, plant height, and grain yield in soybean: a machine and deep learning approach using multispectral data
publisher	MDPI AG
publishDate	2021
url	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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