Akademik Veri Yönetim Sistemi
Bursa Uludağ Üniversitesi Ziraat Fakültesi Dergisi (Online), cilt.37, sa.2, ss.387-400, 2023 (Hakemli Dergi)
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.