Canopy Averaged Chlorophyll Content Prediction of Pear Trees Using Convolutional Autoencoder on Hyperspectral Data

Paul, S and Poliyapram, V and Imamoglu, N and Uto, K and Nakamura, R and Kumar, DN (2020) Canopy Averaged Chlorophyll Content Prediction of Pear Trees Using Convolutional Autoencoder on Hyperspectral Data. In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13 . pp. 1426-1437.

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Abstract

Chlorophyll content is one of the essential parameters to assess the growth process of the fruit trees. This present study developed a model for estimation of canopy averaged chlorophyll content (CACC) of pear trees using the convolutional autoencoder (CAE) features of hyperspectral (HS) data. This study also demonstrated the inspection of anomaly among the trees by employing multidimensional scaling on the CAE features and detected outlier trees prior to fit nonlinear regression models. These outlier trees were excluded from the further experiments that helped in improving the prediction performance of CACC. Gaussian process regression (GPR) and support vector regression (SVR) techniques were investigated as nonlinear regression models and used for prediction of CACC. The CAE features were proven to be providing better prediction of CACC when compared with the direct use of HS bands or vegetation indices as predictors. The CACC prediction performance was improved with the exclusion of the outlier trees during training of the regression models. It was evident from the experiments that GPR could predict the CACC with better accuracy compared to SVR. In addition, the reliability of the tree canopy masks, which were utilized for averaging the features' values for a particular tree, was also evaluated.

Item Type:	Journal Article
Publication:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher:	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Additional Information:	Copyright for this article belongs to IEEE
Keywords:	Chlorophyll; Convolution; Forecasting; Forestry; Fruits; Learning systems; Orchards; Statistics; Support vector regression, Chlorophyll contents; Gaussian process regression; Hyperspectral Data; Multi-dimensional scaling; Nonlinear regression models; Prediction performance; Support vector regression (SVR); Vegetation index, Trees (mathematics), canopy; chlorophyll; multispectral image; prediction; remote sensing; tree, Pyrus
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	09 Jun 2020 11:05
Last Modified:	09 Jun 2020 11:05
URI:	http://eprints.iisc.ac.in/id/eprint/65531

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