Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis
In this paper, provide an application of Learning Vector Quantization (LVQ)-based techniques for solving a real-world problem. We apply LVQ for automated diagnosis of crop disease in cassava plants using features extracted from images of plants’ leaves. The problem reduces to a five class problem in which we attempt to distinguish between a leaf from a health plant and leaves representing four different viral and bacterial diseases in cassava. We discuss the problem under additional constraints that the solution must easily be deployable on a mobile device with limited processing power. In this study we explore the right configuration of type of algorithm and type of features extracted from the leaves that optimally solves the problem. We apply different variations of LVQ and compare them with standard classification techniques (Naïve Bayes, SVM and KNN). Results point to a preference of color feature representations and LVQ-based algorithms.
KeywordsPrototype-based classification LVQ GLVQ GMLVQ DLVQ Multi-class classification Feature extraction Image analysis
The authors would like to thank the Dr. Titus Alicai and Dr. Chris Omongo of the Uganda National Crop Resources Research Institute (NaCRRI), for granting us permission to access disease and pest surveillance data and for supporting the annotation of the data. This work is carried out with support from the Bill and Melinda Gates Foundation under the PEARL 1: Automated survey technology and spatial modeling of viral crop disease in cassava project.
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