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

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 22; p. 4632
• Main Authors: Teodoro, Paulo Eduardo, Teodoro, Larissa Pereira Ribeiro, Baio, Fábio Henrique Rojo, da Silva Junior, Carlos Antonio, dos Santos, Regimar Garcia, Ramos, Ana Paula Marques, Pinheiro, Mayara Maezano Faita, Osco, Lucas Prado, Gonçalves, Wesley Nunes, Carneiro, Alexsandro Monteiro, Junior, José Marcato, Pistori, Hemerson, Shiratsuchi, Luciano Shozo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2021
• Subjects: Agricultural production; Agriculture; Agronomic crops; Agronomy; Artificial intelligence; Band spectra; Computer applications; Corn; Crop yield; Crops; Cultivars; data collection; Decision making; Deep learning; deep neural network; Embedded sensors; Farmers; field experimentation; Genotypes; Grain; grain yield; Growing season; Learning algorithms; Machine learning; model validation; Multilayers; multispectral remote sensing data; Neural networks; Performance evaluation; plant height; Precision agriculture; prediction; regression analysis; Regularization; Remote sensing; Remote sensing systems; Seasons; Sensors; shallow learner; Soybeans; Spectral bands; Support vector machines; Unmanned aerial vehicles; Variables; Vegetation; Brazil
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13224632

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.