Sunn Pest Control Policies and Effect of Sunn Pest Damage on Wheat Quality and Price in Turkey

Gül, Aykut; Akbay, Cuma; Direk, Mithat

doi:10.1007/s11135-005-1096-3

Cited by 18 publications

(9 citation statements)

References 9 publications

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“…As a result, many harmful consequences arise, including nutrient and weight losses, decrease in germination rate and increase in susceptibility to fungal contamination . These effects lead to lower market values and a decrease in the quality of flour used for baking . Additionally, the bread industry has great difficulty in processing flour obtained from sunn pest‐damaged grains (SDG).…”

Section: Introductionmentioning

confidence: 99%

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Detection of sunn pest‐damaged wheat grains using artificial bee colony optimization‐based artificial intelligence techniques

Sabancı

2019

J Sci Food Agric

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BACKGROUND In this study, artificial intelligence models that identify sunn pest‐damaged wheat grains (SDG) and healthy wheat grains (HWG) are presented. Svevo durum wheat cultivated in Konya province, Turkey is used for the process, with 150 HWG and 150 SDG being used for classification. Thanks to the constructed imaging setup, photos of the 300 wheat grains are obtained. Seventeen visual features of each wheat grain are extracted by image‐processing techniques and evaluated in three different groups of dimension, texture and pattern as visual parameters. Artificial bee colony (ABC) optimization‐based artificial neural network (ANN) and extreme learning machine (ELM) algorithms are implemented to classify the damaged wheat grains. RESULTS A correlation‐based feature selection (CFS) technique is also utilized to find the most effective among the 17 features. In the classification process using five selected features, the mean absolute error (MAE) and root mean square error (RMSE) values for ABC‐based ANN are calculated as 0.00174 and 0.00433 respectively. The proposed technique is integrated into graphical user interface (GUI) software to construct an effective detection system for practical use. CONCLUSION The results indicate that, thanks to the modified ANN algorithm and implemented CFS algorithm, the detection accuracy of damaged wheat grains is considerably increased. © 2019 Society of Chemical Industry

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Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4] These effects lead to lower market values and a decrease in the quality of flour used for baking. [5][6][7] Additionally, the bread industry has great difficulty in processing flour obtained from sunn pest-damaged grains (SDG). Quality protection of damaged wheat flour is an important issue for the bread industry.…”

Section: Introductionmentioning

confidence: 99%

Detection of sunn pest‐damaged wheat grains using artificial bee colony optimization‐based artificial intelligence techniques

Sabancı

2019

J Sci Food Agric

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“…These species, in particular E. integriceps and E. maura , reproduce in high numbers on grain crops and considerably reduce crop productivity. Thus, an infestation of Sunn pests ( E. integriceps , E. maura , and E. testudinaria ) might result in a 20–30% yield loss for barley and a 50–90% yield loss for wheat ( Gul et al 2006 ). Furthermore, it greatly reduces the baking quality of the flour due to gluten degradation by proteolytic enzymes ( Darkoh et al 2010 , Konarev et al 2013 ).…”

Section: Introductionmentioning

confidence: 99%

DNA barcoding and morphological analysis for rapid identification of most economically important crop-infesting Sunn pests belonging to Eurygaster Laporte, 1833 (Hemiptera, Scutelleridae)

Syromyatnikov¹,

Golub²,

Kokina³

et al. 2017

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The genus Eurygaster Laporte, 1833 includes ten species five of which inhabit the European part of Russia. The harmful species of the genus is E. integriceps. Eurygaster species identification based on the morphological traits is very difficult, while that of the species at the egg or larval stages is extremely difficult or impossible. Eurygaster integriceps, E. maura, and E. testudinaria differ only slightly between each other morphologically, E. maura and E. testudinaria being almost indiscernible. DNA barcoding based on COI sequences have shown that E. integriceps differs significantly from these closely related species, which enables its rapid and accurate identification. Based on COI nucleotide sequences, three species of Sunn pests, E. maura, E. testudinarius, E. dilaticollis, could not be differentiated from each other through DNA barcoding. The difference in the DNA sequences between the COI gene of E. integriceps and COI genes of E. maura and E. testudinarius was more than 4%. In the present study DNA barcoding of two Eurygaster species was performed for the first time on E. integriceps, the most dangerous pest in the genus, and E. dilaticollis that only inhabits natural ecosystems. The PCR-RFLP method was developed in this work for the rapid identification of E. integriceps.

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“…and Aelia spp. occurs in many countries of the Middle East, Eastern Europe and North Africa (Paulian and Popov, 1980;Gul et al, 2006) and damaged crops yield grain with a reduced bread making quality (Hariri et al, 2000). Infested grain contains a protease that breaks down the gluten structure of dough (Mirak and Mohammadi, 2004;Sivri et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Wheat Resistance to the Adult Insect of Sunn Pest, <i>Eurigaster Integriceps</i> Put

Sanaey¹,

Mirak²

2012

American Journal of Agricultural and Biological Sciences

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Sunn pest is one of the most serious pests of wheat and barley in Asia, North Africa and Eastern Europe. Using of resistant cultivars is an effective strategy for Integrated Pest Management (IPM). In order to identify the resistant wheat to sunn pest, 79 Iranian bread and durum wheat cultivars\lines were evaluated for resistance to natural infestations of sunn pest in field conditions using CRD with four replications in Karaj in two cropping seasons. Analysis of variance revealed significant differences among the genotypes for overwintered density of the adult insect and spike damage. Based on density of overwintered adult insect, cultivar Darab 2 with an average of 12.6 insects per m 2 had the highest density and was the most susceptible cultivar to pest damage and the cultivars Marvdasht, M-82-6 and Bezostaya with densities of one insect/m 2 were the most resistant wheat genotypes. The density of overwintered adult insects in oat (resistant check) was zero. Based on the results for spike damage, line S-83-13 with 80 damaged spikes per m 2 and the two cultivars MV17 and Gaspard both with 10 damaged plants per m 2 were identified as the most susceptible and the most resistant wheat genotypes, respectively. Oat showed no damage based on this index. Based on the results of this study, it can be deduced that adult insects decrease grain yield through a reduction to number of grains per spike and number of spikes/area. It was also determined that at this growth stage of the sunn pest, those cultivars, which produce late heading and early maturing, incur less damage, indicating that the sunn pest prefers those cultivars that produce plants with more height than the shorter varieties.

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Sunn Pest Control Policies and Effect of Sunn Pest Damage on Wheat Quality and Price in Turkey

Abstract: sunn pest damages, wheat, characteristic, price, quality, Turkey,

Cited by 18 publications

References 9 publications

Detection of sunn pest‐damaged wheat grains using artificial bee colony optimization‐based artificial intelligence techniques

Detection of sunn pest‐damaged wheat grains using artificial bee colony optimization‐based artificial intelligence techniques

DNA barcoding and morphological analysis for rapid identification of most economically important crop-infesting Sunn pests belonging to Eurygaster Laporte, 1833 (Hemiptera, Scutelleridae)

Wheat Resistance to the Adult Insect of Sunn Pest, <i>Eurigaster Integriceps</i> Put

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