List of Publications:

Find my publications at Google Scholar, DBLP, or Research Gate.

Book Chapter Published

In Computer Vision and Pattern Recognition in Environmental Informatics (published by IGI Global, Release Date: September, 2015. Copyright © 2016.)

Chapter 14:
Cell Phone Image-Based Plant Disease Classification
Marion Neumann, Universtiy of Bonn, Germany
Lisa Hallau, University of Bonn, Germany
Benjamin Klatt, Central Institute for Decision Support Systems in Crop Protection, Germany
Kristian Kersting, TU Dortmund University, Germany
Christian Bauckhage, Fraunhofer IAIS, Germany

Modern communication and sensor technology coupled with powerful pattern recognition algorithms for information extraction and classification allow the development and use of integrated systems to tackle environmental problems. This integration is particularly promising for applications in crop farming, where such systems can help to control growth and improve yields while harmful environmental impacts are minimized. Thus, the vision of sustainable agriculture for anybody, anytime, and anywhere in the world can be put into reach. This chapter reviews and presents approaches to plant disease classification based on cell phone images, a novel way to supply farmers with personalized information and processing recommendations in real time. Several statistical image features and a novel scheme of measuring local textures of leaf spots are introduced. The classification of disease symptoms caused by various fungi or bacteria are evaluated for two important agricultural crop varieties, wheat and sugar beet.

Accepted in JMLR (Machine Learning Open Source Software)!

pyGPs – A Python Library for Gaussian Process Regression and Classification
Marion Neumann, CSE, Washington University, St. Louis, MO 63130, United States
Shan Huang, Fraunhofer IAIS, 53757 Sankt Augustin, Germany
Daniel E. Marthaler, Sproutling, San Francisco, CA 94111, United States
Kristian Kersting, CS, TU Dortmund University, 44221 Dortmund, Germany

We introduce pyGPs, an object-oriented implementation of Gaussian processes (GPs) for machine learning. The library provides a wide range of functionalities reaching from simple GP specification via mean and covariance and GP inference to more complex implementa- tions of hyperparameter optimization, sparse approximations, and graph based learning. Using Python we focus on usability for both “users” and “researchers”. Our main goal is to offer a user-friendly and flexible implementation of gps for machine learning. Keywords: Gaussian Processes, Python, Regression and Classification

Download the draft HERE.