Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images

Yahui Guo,Davide Cammarano,Mario Cunha,Shouzhi Chen,Senthilnath Jayavelu,Yongshuo Fu,Xinxi Li

doi:10.3390/rs14061337

Yahui Guo, Davide Cammarano + Show 5 more

Open Access

https://doi.org/10.3390/rs14061337

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Abstract

Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods.

Highlights

The empirical line calibration method was applied for obtaining the generic equations based on the multi-spectral images on multiple days, and results showed the radiometric calibration achieved high accuracy
The commonly applied spectral and textural indices were extracted from multi-spectral images, and the linear regression analysis was conducted between these indices and soil and plant analyzer development (SPAD) values
support vector machine (SVM) and random forest (RF) were independently conducted to predict SPAD values based on optimal combinations

Summary

Introduction

The unmanned aerial vehicle (UAV) mounted with multi-sensors has attracted great attention for the easy deployment, flexibility, and high temporal (daily) and spatial (centimeterlevels) resolutions in ecological and environmental domains [1–4].Compared with satellite platforms, UAVs are generally labelled as being light weight, with optional flying altitude, flexible dates for data acquisition, and easy deployment [5–8]. UAVs are gradually becoming an alternative tool to satellite remote sensing in several applications, such as modelling, mapping, and monitoring biophysical parameters of vegetations in ecology, rangelands, forests, and agriculture [9–13]. UAVs mounted with multi-sensors are Remote Sens. The high-throughput data, such as the high spatial resolution of multi-spectral images, provide great potentials in optimizing agronomic management of input to maximize crop yields and quality in PA [23–25]

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