A Study of Processing Model and Different Application Aspects of Agricultural Image Processing

Image processing has involved its contribution to almost all area of real time applications. One of such fastest growing research area is the agricultural image processing. This kind of processing includes the work on different agricultural objects and products such as flowers, fruits, leaves etc. In this paper, these all application aspects are explored. These aspects are also defined along with the individual aspect exploration and the associated challenges. The paper has also defined a standard agricultural processing model. This model is based on the feature point analysis to perform the image recognition and classification.

[1]  Mustafa Teke,et al.  A short survey of hyperspectral remote sensing applications in agriculture , 2013, 2013 6th International Conference on Recent Advances in Space Technologies (RAST).

[2]  Anna Balenzano,et al.  Land cover classification by using multi-temporal COSMO-SkyMed data , 2011, 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp).

[3]  Christian Debes,et al.  Ensemble Learning in Hyperspectral Image Classification: Toward Selecting a Favorable Bias-Variance Tradeoff , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Zhengwei Yang,et al.  A study of MODIS and AWiFS multisensor fusion for crop classification enhancement , 2009, 2009 17th International Conference on Geoinformatics.

[5]  Mattia Marconcini,et al.  Targeted Land-Cover Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Heather McNairn,et al.  The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data to Crop Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Salah Sukkarieh,et al.  Orchard fruit segmentation using multi-spectral feature learning , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Stefano Pignatti,et al.  Statistical Classification for Assessing PRISMA Hyperspectral Potential for Agricultural Land Use , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  P. Z. Firouzabadi Performance evaluation of supervised classification of remotely sensed data for crop acreage estimation , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[10]  Shobha Sriharan,et al.  Land cover classification of SSC image: unsupervised and supervised classification using ERDAS Imagine , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Zheng Niu,et al.  Classification Using EO-1 Hyperion Hyperspectral and ETM Data , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[12]  John R. Miller,et al.  Hyperspectral Data Segmentation and Classification in Precision Agriculture: A Multi-Scale Analysis , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[13]  Lingli Wang,et al.  Cropland parcels extraction based on texture analysis and multi-spectral image classification , 2010, 2010 18th International Conference on Geoinformatics.