Determination of wheat kernels damaged by Fusarium head blight using monochromatic images of effective wavelengths from hyperspectral imaging coupled with an architecture self-search deep network

Fusarium Head Blight of Wheat

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Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture

The present work shows the application of transfer learning for a pre-trained deep neural network (DNN), using a small image dataset (≈ 12,000) on a single workstation with enabled NVIDIA GPU card that takes up to 1 hour to complete the training task and archive an overall average accuracy of 94.7%. The DNN presents a 20% score of misclassification for an external test dataset. The accuracy of ...

Detection of Sprouted and Midge-damaged Wheat Kernels Using Near-infrared Hyperspectral Imaging

Sprout damage which results in poor bread making quality due to enzymatic activity of alpha-amylase is one of the important grading factors of wheat in Canada. The potential of near-infrared (NIR) hyperspectral imaging was investigated to detect sprouting of wheat kernels. Artificially sprouted, midge-damaged and healthy wheat kernels were scanned using NIR hyperspectral imaging system in the r...

Fusarium Head Blight Symptoms and Mycotoxin Levels in Single Kernels of Infected Wheat Spikes

Phylogeny and genetic diversity of Fusarium graminearum species complex associated with Fusarium head blight of wheat in Moghan plain (Iran)

Thirty-seven isolates of Fusarium graminearum species complexobtained from wheat heads with Fusarium head blight symptoms were selected and used for phylogenetic studies. They were collected from different localities of Moghan plain (Ardebil province, Iran). Partial sequences of translation elongation factor 1-alpha (TEF), putative reductase (RED) and UTP-ammonia ligase (URA) genes were amplifi...