Automated Detection of Tetranychus urticae Koch in Citrus Leaves Based on Colour and VIS/NIR Hyperspectral Imaging

Abstract

Tetranychus urticae Koch is an important citrus pest that produces chlorotic spots on the leaves and scars on the fruit of affected trees. It is detected by visual inspection of the leaves. This work studies the potential of colour and hyperspectral imaging (400–1000 nm) under laboratory conditions as a fast and automatic method to detect the damage caused by this pest. The ability of a traditional vision system to differentiate this pest from others, such as Phyllocnistis citrella, and other leaf problems such as those caused by nutritional deficiencies, has been studied and compared with a more advanced hyperspectral system. To analyse the colour images, discriminant analysis has been used to classify the pixels as belonging to either a damaged or healthy leaves. In contrast, the hyperspectral images have been analysed using PLS DA. The rate of detection of the damage caused by T. urticae with colour images reached 92.5%, while leaves that did not present any damage were all correctly identified. Other problems such as damage by P. citrella were also correctly discriminated from T. urticae. Moreover, hyperspectral imaging allowed damage caused by T. urticae to be discriminated from healthy leaves and to distinguish between recent and mature leaves, which indicates whether it is a recent or an older infestation. Furthermore, good results were achieved in the discrimination between damage caused by T. urticae, P. citrella, and nutritional deficiencies.

Automated imaging coupled with AI-powered analysis accelerates the assessment of plant resistance to Tetranychus urticae

“…Most HTP protocols are based on low-resolution imaging using visible, multispectral, hyperspectral or uorescence cameras, which allow the recording of traits both visible and invisible to the naked eye (Goggin et al, 2015;Herrmann et al, 2012Herrmann et al, , 2015Martin and Latheef, 2018;Nieuwenhuizen et al, 2019;Uygun et al, 2020;Fraulo et al, 2009;Crockett et. al., 2014;Gonzalez-Gonzalez et al, 2021). A big advantage of such data collection is a relatively short recording time and the possibility of storing and reanalyzing data at any time (Goggin et al, 2015).…”

Detection of skin defects in loquats based on grayscale features combined with reflectance, absorbance, and Kubelka–Munk spectra

“…Hyperspectral imaging technology, as a fast and nondestructive detection technology, has been widely used in fruit detection with the advantages of the integration of spectra and image 4,5 . However, in previous studies, hyperspectral imaging has generally used its reflectance spectral parameters to identify specific damage in fruits, including bruise damage in apple, 6 chilling injury in green bell peppers, 7 fungal infection in peach, 8 and tetranychus urticae in citrus leaves 9 . Xie et al 10 used spectral reflectance information of citrus and KNN algorithm combined with selected characteristic wavelengths, to establish a citrus black spot category discrimination model, with a classification accuracy of 100%.…”

“…4,5 However, in previous studies, hyperspectral imaging has generally used its reflectance spectral parameters to identify specific damage in fruits, including bruise damage in apple, 6 chilling injury in green bell peppers, 7 fungal infection in peach, 8 and tetranychus urticae in citrus leaves. 9 Xie et al 10 used spectral reflectance information of citrus and KNN algorithm combined with selected characteristic wavelengths, to establish a citrus black spot category discrimination model, with a classification accuracy of 100%. Siedliska et al 11 processed the raw hyperspectral reflectance spectral data with second-order derivatives and selected 19 feature variables to build a back-propagation neural network discriminant model, for fungal infection of strawberry fruit, with a classification accuracy of 97%.…”