Automatic assessment of biological control effectiveness of the egg parasitoid Trichogramma bourarachar against Cadra cautella using machine vision

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dc.contributor.author Song, Yuqi
dc.date.accessioned 2016-08-11T21:40:33Z
dc.date.available 2016-08-11T21:40:33Z
dc.date.issued 2016-08-01 en_US
dc.identifier.uri http://hdl.handle.net/2097/32892
dc.description.abstract The primary objective of this research is to achieve automatic evaluation of the efficiency of using Trichogramma bourarachae for biological control of Cadra (=Ephestia) cautella by calculating the rate of parasitization. Cadra cautella is a moth feeding as a larva on dried fruit as well as stored nuts, seeds, and other warehouse foodstuffs. It attacks dates from ripening stages while on tree, throughout storage, and until consumption. These attacks cause significant qualitative and quantitative damages, which negatively affect dates’ marketability, resulting in economic losses. To achieve this research goal, tasks were accomplished by developing image processing algorithms for detecting, identifying, and differentiating between three Cadra cautella egg categories based on the success of Trichogramma parasitization against them. The egg categories were parasitized (black and dark red), fertile (unhatched yellow), and hatched (white) eggs. Color, intensity, and shape information was obtained from digital images of Cadra eggs after they were subjected to Trichogramma parasitization and used to develop detection algorithms. Two image processing methods were developed. The first method included segmentation and extractions of color and morphological features followed by watershed delineation, and is referred to as the "Watershed Method" (WT). The second method utilized the Hough Transformation to find circular objects followed by convolution filtering, and is referred to as the "Hough Transform Method" (HT). The algorithms were developed based on 2 images and then tested on more than 40 images. The WT and the HT methods achieved correct classification rates (CCRs) of parasitized eggs of 92% and 96%, respectively. Their CCRs of yellow eggs were 48% and 94%, respectively, while for white eggs the CCRs were 42% and 73%. Both methods performed satisfactorily in detecting the parasitized eggs, but the HT outperformed the WT in detecting the unparasitized eggs. The developed detection methods will enable automatic evaluation of biological control of Cadra (=Ephestia) cautella using Trichogramma bourarachae. Moreover, with few adjustments these methods can be used in similar applications such as detecting plant diseases in terms of presence of insects or their eggs. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Date fruit en_US
dc.subject Trichogramma parasitization en_US
dc.subject Cadra egg detection en_US
dc.subject Watershed delineation en_US
dc.subject Hough transformation en_US
dc.title Automatic assessment of biological control effectiveness of the egg parasitoid Trichogramma bourarachar against Cadra cautella using machine vision en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Biological & Agricultural Engineering en_US
dc.description.advisor Naiqian Zhang en_US
dc.date.published 2016 en_US
dc.date.graduationmonth August en_US


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