Diyarbakır’da Buğday Ürününe Karışan Yabancı Ot Tohumlarının Belirlenmesi

Pala, Fırat; Mennan, Hüsrev; Çığ, Fatih; Dilmen, Halil

doi:10.19159/tutad.342885

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…In order to determine the amount of products taken into the sifting process, it starts with the calculation of the area(A) they occupy on the counter. Area calculation for the quantity of products according to the images obtained from the camera; it depends on the horizontal axis pixel number ( 𝑀 𝑥 ) of the computer monitor, the vertical axis pixel number (𝑀 𝑦 ), the actual lengths of the horizontal axis(𝐶 𝑥 ) and vertical axis(𝐶 𝑦 ) of the bench area viewed by the camera, and the total number of pixels(𝑃 𝑛 ) occupied by the products in the sifting process (1). The pollution/conformity(𝑃 𝑟 ) ratio of the agricultural products taken into the sifting process depends on the number of detected foreign matter/inappropriate agricultural product (𝐹 𝑛 ), the amount of the products taken into the sifting process (A) and a pollution coefficient (𝑃 𝑐 )(2).…”

Section: Discussionmentioning

confidence: 99%

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Real-time Fast Selection System with Object Recognition and TSP algorithms

DEMİR

2023

International Journal of Applied Mathematics Electronics and Computers

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The stage before the conversion of agricultural products into post-harvest consumer products is the process of separating the raw products into appropriate classes. Today, this difficult manual separating process is a process in which a large number of workers work at an intense pace on the product line and the workforce is intensively spent. Disruptions in separating as a result of carelessness cause product loss, loss of time and cost increases. In this study, as an alternative to manual separating processes, a real-time separating system, which detects the products in the factory band with object recognition methods and enables fast positioning of the separating tool on the products, works simultaneously with object recognition and traveling salesman problem algorithms has been created. In this way, a low-budget separating system is recommended for large selecting processes with a time- and cost-effective selecting model. In the study, the creation of a real-time fast separating system with the support of the traveling salesman algorithm, performance evaluation and research and findings on the fast separating model are presented.

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Section: Discussionmentioning

confidence: 99%

“…In some cases, there are other plant seeds mixed into the product collected in the field, which is undesirable [1][2][3][4][5][6][7][8]. For this, it should be ensured that the right taste and desired quality food products are obtained by selecting processes.…”

Section: Introductionmentioning

confidence: 99%

Real-time Fast Selection System with Object Recognition and TSP algorithms

DEMİR

2023

International Journal of Applied Mathematics Electronics and Computers

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“…They found that Adonis sp., Ranunculus arvensis and G. tricornutum were heavily contaminated in the Eastern Black Sea Region. Pala et al (2018) determined that the weed seeds were most intensely contaminated into the harvested wheat product belonged to the Poaceae family with 13 species. It was determined that the most common species were Avena fatua and A. sterilis in Diyarbakır province.…”

Section: Intrоduсtiоnmentioning

confidence: 99%

The Species and Intensities of Weed Seeds Obtained from Wheat Flour Mill Plants in Turkey

Karaca

Bozkan

2021

SJAFS

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Wheat production is more than 10% is in first place with potential in Konya plain of Turkey. Weed seeds mixed crop seeds cause quality and yield losses on production. This research was carried out to determine the species, intensity and frequency of weed seeds obtained from flour mill plants operating in Konya. Samples were taken from the 15 flour mill plants with working high capacity in the region. Identification of the weed seeds species in the sample was made. Comparison was made with live materials and reference documents by examining the seeds under a binocular while diagnosis of species. Also, the intensity and frequency of the species contaminated in wheat were determined. As a result of the study, 79 weed seed species belonging to 19 different families were identified. The species of families the most inclusive were Poaceae with 14 species and Leguminosae with 13 species. In order to determine intensity and frequency of species, weed seeds counted by hand, weighed in scale were recorded in laboratory. The highest weed seeds intensity as number and weight among the species were determined Galium tricornutum (rough bedstraw) with 16.16% and Aegilops cylindrica (jointed goatgrass) with 21.22% respectively. About the frequency of species, the most frequent species was Convolvulus arvensis (field bindweed) with 100%.

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“…It ranks third in Turkey in terms of wheat production. Diyarbakir province ranks fourth in Turkey in terms of wheat cultivation area (264000 ha, with 3.3% share), and ranks third in terms of production (845000 tons, with 4.2% share) (Pala et al 2018).…”

Section: Introductionmentioning

confidence: 99%

A Study on Determination of Energy Productivity and Greenhouse Gas Emissions in Wheat Production

Demirel,

Baran,

Gökdoğan

2024

SJAFS

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In this study, energy use efficiency (EUE) and greenhouse gas emissions (GHG) in wheat production were defined, and the energy equivalents (EE) of the inputs in production per unit production area, EUE and GHG values of the product were computed. The data used in the study were obtained from 175 different wheat producing enterprises in 2021 by conducting face-to-face surveys according to the proportional sampling method. In the study, the amount of direct (DE) and indirect energy (IE) use in wheat production and their shares in total energy consumption were defined. According to the results of the study, total energy input (EI) in wheat production was com-puted as 19,024.21 MJ/ha and energy output (EO) as 80,585.40 MJ/ha. It was defined that the input with the highest energy consumption was fertili-zation with a value of 8748.38 MJ/ha. This was followed by seed energy input 4626.79 MJ/ha (24.32%), fuel energy 2697.25 MJ/ha (14.18%), irriga-tion energy 2362.50 MJ/ha (12.42%), chemicals energy 269.19 MJ/ha (1.41%), machinery energy 309.52 MJ/ha (1.63%), human labor energy 10.58 MJ/ha (0.06%). EUE, energy productivity (EP), specific energy (SE) and net energy (NE) yield values were 4.24, 0.29 kg/MJ, 3.47 MJ/kg and 61561.19 MJ/ha, respectively. Total GHG emission for wheat production was computed as 3784.60 kgCO2-eq/ha. The highest share of total GHG emissions belonged to seed (59.41%). Seed was followed by irrigation (16.84%), nitrogen fertilizer use (14.60%), phosphate fertilizer use (3.99%), fuel use (3.49%), chemicals use (0.98%), machinery use (0.58%) and hu-man labor (0.10%). In addition, the GHG ratio in wheat production was computed as 0.69 kgCO2-eq/ha.

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Diyarbakır’da Buğday Ürününe Karışan Yabancı Ot Tohumlarının Belirlenmesi

Cited by 5 publications

References 7 publications

Real-time Fast Selection System with Object Recognition and TSP algorithms

Real-time Fast Selection System with Object Recognition and TSP algorithms

The Species and Intensities of Weed Seeds Obtained from Wheat Flour Mill Plants in Turkey

A Study on Determination of Energy Productivity and Greenhouse Gas Emissions in Wheat Production

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