Identification of Fusarium damaged wheat kernels using image analysis

Abstract

Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (DON) content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue), very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance). The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.

Classification of Fusarium-infected and healthy wheat kernels based on features from hyperspectral images and flatbed scanner images: a comparative analysis

“…In most published studies, Fusarium-infected and healthy wheat kernels have been classified with the use of digital image analyses, but there is a general scarcity of research based on textural features. Jirsa and Polišenska [15] developed a discrimination model based on colour descriptors (R, G, B, H) which was characterised by 85% classification accuracy. Healthy wheat kernels were classified correctly in 80% of cases, and kernels with visual symptoms of a fungal infection (whitish, pinkish and normally sized kernels, and whitish, pinkish and shrivelled kernels) were discriminated with 90% total accuracy.…”

“…Fusarium may be identified mainly by molecular methods such as real-time polymerase chain reaction (RT-PCR). Other methods for fungal infection identification include microbiological methods involving fungal cultures, microscopic methods, visual assessments, and image analysis involving flatbed scanners, hyperspectral imaging and methods based on thermal properties [13][14][15][16][17][18].…”

“…Jirsa and Polišenska [15] relied on digital image analysis to distinguish between Fusarium-infected and healthy wheat kernels based on a classification model with colour descriptors R (red), G (green), B (blue) in the RGB colour space and descriptor H (Hue) in the HSL (Hue, Saturation, Luminance) colour space. Image analysis has also been used to describe discolouration of barley kernels and thus it is a highly useful method for detecting kernels affected by fungi [24].…”

Evaluation of wheat kernels infected by fungi of the genus Fusarium based on morphological features

“…In the literature, fungal infections of wheat kernels have never been evaluated based on such a large number of geometric parameters (59 linear dimensions and shape factors). Jirsa and Polišenska () also used image analysis to distinguish between Fusarium‐ infected and healthy wheat kernels. In the above study, the examined parameters were kernel length, width, perimeter, and area.…”

“…Digital image analysis involving statistical data processing methods delivers more rapid and more accurate results than conventional visual inspection. The analysis distinguishes between healthy and damaged kernels based on discrimination models containing color descriptors: R (Red), G (Green), B (Blue), and H (Hue) (Jirsa & Polišenska, ). Image analysis was applied to identify the discoloration of barley kernels caused by the sporodochia of Fusarium fungi, and it could play an important role in the process of detecting infected kernels (Fox et al, ).…”