Ayçiçeğinde Küllemenin Makine Öğrenimine Dayalı Tespiti ve Şiddetinin Değerlendirilmesi: Hassas Tarım Yaklaşımı

Year 2023, Volume: 37 Issue: 2, 387 - 400, 08.12.2023

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

https://doi.org/10.20479/bursauludagziraat.1340129

Cited By: 1

Abstract

Ayçiçeğinde külleme (Golovinomyces cichoracearum (DC.) V.P. Heluta), önemli ölçüde verim kaybına
neden olan, ayçiçeği ürünleri için önemli bir tehdittir. Geleneksel teşhis yöntemleri, insan gözlemine dayalı
olarak, erken hastalık tespiti ve hızlı kontrol sağlama konusunda yetersiz kalmaktadır. Bu çalışma, ayçiçeğinde küllemenin erken tespiti için makine öğrenimini kullanarak bu soruna yeni bir yaklaşım sunmaktadır. Orijinal alan görüntülerinden elde edilen fotoğraflara ait toprak, külleme, sap ve yaprak matrisleri ile Decision Trees (Karar Ağaçları) modeli eğitilerek hastalık şiddet seviyeleri tespit edilmiştir. Test görüntülerinde sırasıyla A ve C olarak etiketlenmiş hastalık şiddeti seviyeleri %18.14 ve %5.56 olarak belirlenmiştir. Modelin %85 oranında gösterdiği doğruluk, modelin yüksek düzeyde yetkinliğe ve özellikle Decision Trees modelinin tarım alanında hastalık kontrolünü ve hastalıkların önlenmesini devrimleştirmek için umut verici perspektiflere sahip olduğunu göstermektedir.

Keywords

Karar ağaçları, makine öğrenimi, külleme, ayçiçeği, Hastalık şiddeti

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

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

Decision trees, disease severity, machine learning, powdery mildew, sunflower

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