Application of The Hyperspectral Remote Sensing by Au Unmanned Helicopter in Maize (Zea mays L.) Production (Part 2)

Abstract

Maize is a feed crop with high nutritive value. However, the production environment for maize is becoming increasingly severe due to labor shortage or the introduction of organic stock farming. The aim of this study is to establish a monitoring system to support the production management of a maize field. This study examines the method of forecasting maize yield, feed quality, time required to acquire information on the field, and the platform used for remote sensing. Our aim is to evaluate the maize nutritive component at the time of harvest, using remote sensing. Calibration models to estimate the concentrations of various nutritive components such as moisture, total digestible nutrients (TDN), crude protein (CP), and organic cell wall (OCW) were developed using a dataset consisting of observed concentrations and spectral data acquired from a hyperspectral imaging sensor carried in the industrial unmanned helicopter. Prior to obtaining the calibration models, the samples were divided in half as calibration (n = 30) and validation (n = 30) datasets. Calibration models were obtained using partial least squares regression (PLSR) to estimate the maize nutritive composition. The estimation accuracy of the calibration models was examined by applying cross validation, and the evaluation index (EI) method was applied for evaluating the practicality of the calibration model. The EI evaluates the standard deviation of prediction (SDP), and can provide relative evaluation among different components. Five ranks are used: (A) very high (EI, <12.4%); (B) high (EI, 12.524.9%); (C) low (EI, 25.037.4%); (D) very low (EI, 25.037.4%); and (E) estimation impossible (EI, >50.0%). A calibration model has practical accuracy when its EI rank is A, B, or C at less than 37.4%. And the coefficient of determination (R2) of the observed and estimated values for calibration and validation was calculated for each model.In the results of the calibration models, the number of latent variables for estimate nutritive components was four for CP, three for moisture, TDN, and OCW. According to the relationship between the observed and estimated values obtained by the calibration model, most samples in the scatter plot of all nutritive compositions were distributed along the 45 line. The predicted residual sum of squares (PRESS) was 0.89 (moisture),0.82 (TDN), 0.71 (CP),and 0.75 (OCW), respectively. The R2 values of the calibration for all nutritive compositions were high (R2, >0.70); also, the R2 values of the estimation tests for all nutritive compositions were high (R2, >0.79). In addition, all models were ranked B by the EI test, confirming their practicality. In conclusion, this sensing system is a useful technique for estimating the nutritive composition of a maize field.