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

Lucas Prado Osco,Mayara Maezano Faita Pinheiro,Hemerson Pistori,Larissa Pereira Ribeiro Teodoro,Paulo Eduardo Teodoro,Alexsandro Monteiro Carneiro,Luciano Shozo Shiratsuchi,Ana Paula Marques Ramos,Regimar Garcia Dos Santos,Fábio Henrique Rojo Baio,Wesley Nunes Gonçalves,Carlos Antonio Da Silva Junior,José Marcato Junior

doi:10.3390/rs13224632

Abstract

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.

Highlights

Farmers must consider crop genotype management and environmental factors to obtain cultivars with maximum yield [1]
The results indicated that there was a significant interaction between the machine learning (ML) models (DL, random forest (RF), support vector machine (SVM), and linear regression (LR)) and the input settings (WL, vegetation indices (VIs), and WLVI) for the mean absolute error (MAE), root mean squared error (RMSE), and Pearson’s correlation coefficient (r) between observed and estimated values for days to maturity (DM) (Table 2) and plant height (PH) (Table 3)
The results of the significant interaction between models versus input configurations for PH and DM demonstrate that there is a variable relationship between these factors, i.e., the best model depends on the configuration used, and vice versa

Summary

Introduction

Farmers must consider crop genotype management and environmental factors (temperature, rainfall, soil, etc.) to obtain cultivars with maximum yield [1]. Predicting agronomic variables such as days to maturity (DM), plant height (PH), and grain yield (GY), is still a challenging task, especially when considering indirect methods, such as multispectral data collected with remote sensing system based on unmanned aerial vehicle (UAV). The advent of new UAV platforms promoted rapid and high-detailed mapping of multiple farmlands, which generated high amounts of data to be evaluated and incorporated to support agricultural management [2,3,4,5,6,7]. Remote sensing data has been processed with machine learning (ML) methods, which is a promising strategy to support varieties of evaluation and selection and other agricultural applications. It is possible to find a representative number of studies addressing crop yield prediction based on ML models for different crops, such as cherry tree (Prunus avium L.) [8], sugarcane (Saccharum officinarum L.) [9], wheat (Triticum aestivum L.) [10,11,12], potato (Solanum tuberosum L.) [10], coffee (Coffea arabica L.) [13], rice (Oryza sativa L.) [14], and maize (Zea mays L.) [15], among others

Methods

Results

Discussion

Conclusion