Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

Abstract

Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS modeling approach, and GPS usage) and data mining techniques for model development. Higher-end image processing techniques are followed to establish more precision. The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques. Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota, USA. These indices were: (a) red and near-infrared (NIR) based normalized difference vegetation index (NDVI), (b) green and NIR based green vegetation index (GVI), (c) red and NIR based soil adjusted vegetation index (SAVI), and (d) red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for corn yield during 3 years (1998, 1999, and 2001) and for the pooled data of these 3 years. Initially, Back-propagation Neural Network (BPNN) models were developed, including 16 models (4 indices * 4 years including the data from the pooled years) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction. The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images. In all three years, it provided higher prediction accuracies, OPEN ACCESSRemote Sensing 2010, 2 674 coefficient of determination (r 2 ), and lower standard error of prediction than the models involving GVI, NDVI, and SAVI image information. The GVI, NDVI, and SAVI models for all three years provided average testing prediction accuracies of 24.26% to 94.85%, 19.36% to 95.04%, and 19.24% to 95.04%, respectively while the PVI models for all three years provided average testing prediction accuracies of 83.50% to 96.04%. The PVI pool model provided better average testing prediction accuracy of 94% with respect to other vegetation models, for which it ranged from 89-93%. Similarly, the PVI pool model provided coefficient of determination (r 2 ) value of 0.45 as compared to 0.31-0.37 for other index models. Log 10 data transformation technique was used to enhance the prediction ability of the PVI models of years 1998, 1999, and 2001 as it was chosen as the preferred index. Another model (Transformed PVI (Pool)) was developed using the log 10 transformed PVI image information to show its global application. The transformed PVI models provided average corn yield prediction accuracies of 90%, 97%, and 98% for years 1998, 1999, and 2001, respectively. The pool PVI transformed model provided as average testing accuracy of 93% along with r 2 value of 0.72 and standard error of prediction of 0.05 t/ha.

How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study

“…Among the various remote sensing-based vegetation measures utilized in agricultural monitoring, the Normalized Difference Vegetation Index (NDVI) is the most widely used proxy for vegetation cover and production [22][23][24][25][26]. There is a strong relationship between NDVI and agricultural yield [27,28].…”

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

“…One approach develops regression models that relate satellite-derived vegetation indices directly to historical yield data [15][16][17]. These models are essentially retrospective and are based empirically on indirect inferences, whereby changes in vegetation indices can determine variations in plant productions [14,18].…”

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

“…In addition to the five traditional regression equations (linear, exponential, power, logarithmic, and quadratic polynomial regression), two machine learning methods, i.e., a back propagation neural network (BPNN) and a support vector machine (SVM), were applied in model construction. The neural network approach has the advantages of nonlinearity, input-output mapping, adaptivity, generalization, and fault tolerance [27]. A SVM for regression analysis is accomplished by solving a convex optimization problem, more specifically a quadratic programming problem [42].…”