Machine Learning-Based Detection and Severity Assessment of Sunflower Powdery Mildew: A Precision Agriculture Approach

Authors : Alperen Kaan Bütüner, Yavuz Selim Şahin, Atilla Erdinç, Hilal Erdoğan

Pages : 387-400

Doi:10.20479/bursauludagziraat.1340129

View : 157 | Download : 129

Publication Date : 2023-12-08

Article Type : Research Paper

Abstract :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.
Keywords : Karar ağaçları, makine öğrenimi, külleme, ayçiçeği, Hastalık şiddeti

ORIGINAL ARTICLE URL