Person: ŞAHİN, YAVUZ SELİM
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ŞAHİN
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YAVUZ SELİM
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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, HİLALSunflower 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.