Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

Razieh Barzin,Ganesh C. Bora,Jac J. Varco,Hossein Lotfi

doi:10.3390/rs14010120

Abstract

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1–V2 stage) and at the V6–V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs.

Highlights

Agricultural products play a major role in feeding the world’s population and have a remarkable impact on people’s life and work by providing food, feed, and fuels
The results indicated that model had the least mean absolute error (MAE) and root mean square error and (RMSE), and the greatest R 2, it can be conclu that the support vector regression (SVR) model outperformed the other models in almost all performance measures
The results of cross-validation showed that the first quartile, median, and third quartile were almost the same for all of the machine learning (ML) models, indicating that there was no major difference between these models (Figure 8)

Summary

Introduction

Agricultural products play a major role in feeding the world’s population and have a remarkable impact on people’s life and work by providing food, feed, and fuels. It is estimated that land used for agricultural purposes expanded by around 10 million ha/y from 1980 to 2007 [1] to meet the needs of a growing population, changing diets, and emerging demand for biofuels. Nitrogen deficiency results in a decrease in leaf chlorophyll concentration, and causes a change in leaf color from dark green to light green or yellow [9]. This distinction has been associated with physiological and structural changes in cotton leaves [10], which results in an increase in leaf spectral reflectance in the visible wavelength range (400–700 nm) [11]. Near-infrared (NIR) reflectance changes due to N deficiencies, in which these wavelengths are increasingly used for estimating crop

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