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

By Sudhanshu Sekhar Panda, Daniel P. Ames, Suranjan Panigrahi. Remote Sensing, 2010

385 citations recorded by the source corpus.

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, coefficient of determination (r2), 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 (r2) value of 0.45 as compared to 0.31–0.37 for other index models. Log10 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 log10 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 r2 value of 0.72 and standard error of prediction of 0.05 t/ha.

Subfields: Ecology, Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/21a8aaceb8984952950f3ebdd4bfd445813419c8

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Citations in the corpus

High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing (2016, background): “In terms of agriculture, UAVs have been used for the monitoring of water status and drought stresses in fruit trees [24]; additionally, collecting multispectral and hyperspectral imagery for use in spectral indices [25] and even chlorophyll fluorescence [26].”
Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “Panda et al. (2010) developed the BPNN model by using NDVI, green vegetation index, soil-adjusted vegetation index, and perpendicular vegetation indexes.”
Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments (2018, background): “vigour and growth dynamics, among other applications, that are important components of 154 precision agriculture (Panda et al. 2010).”