Abstract
Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%.
Highlights
Agriculture plays a significant role in the economic domain worldwide
An overview of the results for both models created before and after the outlier elimination reveals the great performance that was achieved in all the cases that were studied, scoring individual class accuracies higher than 80% in the case of the XY-fusion network (XY-F) network (Table 5) and higher than 90% in the case of the multilayer perceptron with automated relevance determination (MLP–automatic relevance determination (ARD)) artificial neural networks (ANN) (Table 6)
The performance of the models, before the outlier elimination, described in this paper are comparable with the findings of Schor et al [78] for the detection of tomato spotted wilt virus, using principal component analysis (PCA), with an overall accuracy of 90%, and Xu et al [39] for the detection of tobacco mosaic virus using a Mahalanobis distance based model
Summary
Plant diseases constitute a great threat for the agricultural sector in a worldwide scale, causing significant loss of global production [3]. Diseases in the field and greenhouse conditions are mostly addressed with the use of chemical compounds if the disease is curable This method could be proven efficient, the cost of pesticides is large and the results are questionable. It is necessary that there be targeted early detection for the efficient management of such diseases For these purpose, preventive means and post infection methods are employed, aiming to minimize the extent of the impact of disease damages. These approaches are noninvasive, since they can be applied on the same plants over time [4] These approaches can yield useful data from the spectral bands outside the visual spectrum, enhancing crop monitoring potential [5,6]
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