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

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.

[1]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  P. Gong,et al.  Isolating individual trees in a savanna woodland using small footprint lidar data , 2006 .

[3]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[4]  En Zhu,et al.  Deep Clustering with Convolutional Autoencoders , 2017, ICONIP.

[5]  José Luis Rojo-Álvarez,et al.  Robust support vector regression for biophysical variable estimation from remotely sensed images , 2006, IEEE Geoscience and Remote Sensing Letters.

[6]  Kai Rannenberg,et al.  Computer and Computing Technologies in Agriculture XI , 2017, IFIP Advances in Information and Communication Technology.

[7]  J. Moreno,et al.  RETRIEVAL OF VEGETATION BIOPHYSICAL VARIABLES FROM CHRIS/PROBA DATA IN THE SPARC CAMPAING , 2004 .

[8]  P. Gamba,et al.  URBAN LAND COVER MAPPING USING HYPERSPECTRAL AND MULTISPECTRAL VHR SENSORS: SPATIAL VERSUS SPECTRAL RESOLUTION , 2005 .

[9]  D. Nagesh Kumar,et al.  Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach , 2018 .

[10]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[11]  Kaiguang Zhao,et al.  HIERARCHICAL WATERSHED SEGMENTATION OF CANOPY HEIGHT MODEL FOR MULTI-SCALE FOREST INVENTORY , 2007 .

[12]  Farid Melgani,et al.  Semisupervised PSO-SVM Regression for Biophysical Parameter Estimation , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Wei Cui,et al.  Application of a Hybrid Model Based on a Convolutional Auto-Encoder and Convolutional Neural Network in Object-Oriented Remote Sensing Classification , 2018, Algorithms.

[14]  Gustavo Camps-Valls,et al.  Retrieval of oceanic chlorophyll concentration with relevance vector machines , 2006 .

[15]  Luis Alonso,et al.  Retrieval of Vegetation Biophysical Parameters Using Gaussian Process Techniques , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Zhang Xiong,et al.  3D object retrieval with stacked local convolutional autoencoder , 2015, Signal Process..

[17]  Ben Somers,et al.  Urban tree health assessment using airborne hyperspectral and LiDAR imagery , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[18]  Daniel Cohen-Or,et al.  Outlier Detection for Robust Multi-Dimensional Scaling , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Anatoly A. Gitelson,et al.  A Comparison of Two Techniques for Nondestructive Measurement of Chlorophyll Content in Grapevine Leaves , 2008 .

[20]  Craig S. T. Daughtry,et al.  A visible band index for remote sensing leaf chlorophyll content at the canopy scale , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[21]  Jie Geng,et al.  High-Resolution SAR Image Classification via Deep Convolutional Autoencoders , 2015, IEEE Geoscience and Remote Sensing Letters.

[22]  Zhichao Wu,et al.  Hyperspectral Estimation Methods for Chlorophyll Content of Apple Based on Random Forest , 2017, CCTA.

[23]  Jan G. P. W. Clevers,et al.  Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .

[24]  Madaín Pérez Patricio,et al.  Optical Method for Estimating the Chlorophyll Contents in Plant Leaves , 2018, Sensors.

[25]  J. Kruskal.  Nonmetric multidimensional scaling: A numerical method , 1964 .

[26]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[27]  Shujing Cao,et al.  Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging , 2018, Scientific Reports.

[28]  Andreas Kamilaris,et al.  A review on the practice of big data analysis in agriculture , 2017, Comput. Electron. Agric..

[29]  A. Viña,et al.  Remote estimation of canopy chlorophyll content in crops , 2005 .

[30]  Peijun Du,et al.  Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging , 2016, Neurocomputing.

[31]  François Chollet,et al.  Keras: The Python Deep Learning library , 2018 .

[32]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[33]  Ben Somers,et al.  Multitemporal Chlorophyll Mapping in Pome Fruit Orchards from Remotely Piloted Aircraft Systems , 2019, Remote. Sens..

[34]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[35]  Shishir Gaur,et al.  Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks , 2012, Water Resources Management.

[36]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[37]  Gilberto Pastorello,et al.  Retrieval of the photochemical reflectance index for assessing xanthophyll cycle activity: a comparison of near-surface optical sensors , 2014 .

[38]  Yifei Zhang.  A Better Autoencoder for Image: Convolutional Autoencoder , 2018 .

[39]  Gang Liu,et al.  Prediction of Chlorophyll Content in Different Light Areas of Apple Tree Canopies based on the Color Characteristics of 3D Reconstruction , 2018, Remote. Sens..

[40]  Jie Geng,et al.  Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[41]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .