Abstract
We discuss the use of matrix relevance learning, a popular extension to prototype learning algorithms, applied to a three-class classification task of diagnosing cassava diseases from spectral data. Previously this diagnosis has been done using plant image data taken with a smartphone. However for this method disease symptoms need to be visible. Unfortunately for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. This research is premised on the hypothesis that diseased crops without visible symptoms can be detected using spectral information, allowing for early interventions. In this paper, we analyze visible and near-infrared spectra captured from leaves infected with two common cassava diseases (cassava brown streak disease and cassava mosaic virus disease) found in Sub-Saharan Africa. We also take spectra from leaves of healthy plants. The spectral data come with thousands of dimensions, therefore different wavelengths are analyzed in order to identify the most relevant spectral bands for diagnosing these disease. To cope with the nominally high number of input dimensions of data, functional decomposition of the spectra is applied. The classification task is addressed using Generalized Matrix Relevance Learning Vector Quantization and compared with the standard classification techniques performed in the space of expansion coefficients.
Highlights
T HE ability to quickly diagnose disease in the field is of critical importance in most agro-reliant economies the world over
We present an approach to detection and diagnosis of cassava brown streak disease (CBSD) and cassava mosaic virus disease (CMD) based on image spectroscopy to extract representative features from example leaves manifesting these diseases, and machine learning for building the predictive models based on such data
That because in our case the model operates on 1D spectral data, there would be no way to utilise existing Convolutional Neural Network (CNN) models such as VGG-16 for 2D data directly as starting points for training; we trained this model from scratch starting from a random initialisation
Summary
T HE ability to quickly diagnose disease in the field is of critical importance in most agro-reliant economies the world over. In this study we investigate improved ways of accurately diagnosing plants in the field by leveraging a unique dataset: spectral data from plant leaves, and using improved algorithms that provide higher accuracy and a profile of wavelengths that are most important for the disease classification task. According to [1], CBSD and CMD together account for over 90% of yield losses in cassava production systems in Sub-Saharan Africa. This in turn greatly affects smallholder farmers
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