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ŞAHİN, YAVUZ SELİM

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ŞAHİN

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YAVUZ SELİM

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2018 - 201912020 - 20231
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    Publication
    Machine learning-based detection and severity assessment of sunflower powdery mildew: A precision agriculture approach
    (Bursa Uludağ Üniversitesi, 2023-09-18) Erdinç, Atilla; BÜTÜNER, ALPEREN KAAN; ŞAHİN, YAVUZ SELİM; ERDOĞAN, HİLAL
    Sunflower powdery mildew (Golovinomyces cichoracearum (DC.) V.P. Heluta) is a substantial threat to sunflower crops, causing significant yield loss. Traditional identification methods, based on human observation, fall short in providing early disease detection and quick control. This study presents a novel approach to this problem, utilizing machine learning for the early detection of powdery mildew in sunflowers. The disease severity levels were determined by training a Decision Trees model using matrix of soil, powdery mildew, stems, and leaf images obtained from original field images. It was detected disease severity levels of 18.14% and 5.56% in test images labeled as A and C, respectively. The model's demonstrated accuracy of 85% suggests high proficiency, indicating that machine learning, specifically the DTs model, holds promising prospects for revolutionizing disease control and diseases prevention in agriculture.
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    New application method for entomopathogenic nematode Heterorhabditis bacteriophora (Poinar, 1976) (Rhabditida: Heterorhabditidae) HBH strain against Locusta migratoria (Linnaeus, 1758) (Orthoptera: Acrididae)
    (Entomological Soc Turkey, 2018-01-01) Şahin, Yavuz Selim; Bouhari, Ahcen; Ulu, Tufan Can; Sadıç, Büşra; Susurluk, İsmail Alper; ŞAHİN, YAVUZ SELİM; Bouhari, Ahcen; Ulu, Tufan Can; Sadıç, Büşra; SUSURLUK, İSMAİL ALPER; Bursa Uludağ Üniversitesi/Ziraat Fakültesi/Bitki Koruma Bölümü.; 0000-0003-3640-1474; ISX-7951-2023; CHJ-5278-2022; B-6308-2011; JGO-3717-2023; AAG-7131-2021
    Entomopathogenic nematodes (EPNs) of the families Heterorhabditidae and Steinernematidae are being used as biocontrol agents against many soil borne insect pests in agriculture. Above-ground applications against the insects are usually unsuccessful due to the lack of humidity. Therefore, EPNs rapidly lose their effectiveness. In this study, conducted in 2018 under laboratory conditions in Bursa-Turkey, a new application method was developed for the use of Heterorhabditis bacteriophora (Poinar, 1976) (Rhabditida: Heterorhabditidae) HBH hybrid strain against the migratory locust, Locusta migratoria (Linnaeus, 1758) (Orthoptera: Acrididae). A new trap system is coated with hydrophilic cotton fabric to provide the necessary humidity to allow the use of EPNs above-ground. Three different application rates of H. bacteriophora (5000, 25000 and 50000 IJs) were applied to the trap system. The fabric was inoculated with the nematodes and combined with a reservoir containing 200 ml of ringer solution. The dead and live nematodes were recorded periodically to determine their persistence on the fabric. The mortality of L. migratoria were also recorded to determine the infectivity of H. bacteriophora. The infectivity and persistence of the nematodes was sustained for more than 4 weeks by this method.