İnce Ayarlanmış CNN’ler ve Belirti-Organ Düzeyinde Etiketleme ile Arazi Görüntülerinden Kayısı Bitkisi Hastalık ve Zararlılarının Tespiti

Öz

Bitki hastalık ve zararlılarının erken ve doğru tespiti, verim ve kalite kayıplarının önlenmesi, sürdürülebilir tarımın desteklenmesi ve gıda güvenliğinin sağlanması açısından kritik öneme sahiptir. Bu çalışmada, kayısı (Prunus armeniaca) bitkilerinde hastalık ve zararlı semptomlarını gösteren 6.081 adet arazi görüntüsünden oluşan özgün bir veri seti oluşturulmuştur. Ön-eğitimli evrişimsel sinir ağı (CNN) modelleri AlexNet, GoogLeNet ve ResNet-50 sınıflandırma görevi için ince-ayarlanmıştır. Standart bir veri etiketleme stratejisi yerine, hem belirti türünü hem de etkilenen bitki organını dikkate alan ayrıntılı bir etiketleme yöntemi önerilmiştir. CNN modelleri, biri geleneksel 7-sınıflı, diğeri ise önerilen yöntemle oluşturulan 13-sınıflı olmak üzere iki ayrı veri seti üzerinde eğitilmiştir. Tüm modeller beş-katlı çapraz-doğrulama yöntemi ile değerlendirilmiştir. Tüm model ve veri seti kombinasyonları arasında en yüksek doğruluk oranı olan %93,9’a, ResNet-50 modelinin 7-sınıflı veri seti üzerinde elde ettiği sonuçla ulaşılmıştır. Önerilen etiketleme yöntemi sınıflandırma doğruluğunda küçük bir düşüşe neden olsa da, sınıf sayısının artmasına rağmen performans farkı düşük kalmıştır. Bu bulgular, yöntemin güvenilir olduğunu ve pratik uygulamalar için uygunluğunu göstermektedir.

Anahtar Kelimeler

• Kayısı (Prunus armeniaca)
• Transfer öğrenme
• Ayrıntılı etiketleme
• Bitki hastalık ve zararlı tespiti
• Evrişimsel sinir ağları (CNNs)

Proje Numarası

TAGEM/TSKAD/B/21/A9/P7/5030

Apricot Plant Disease and Pest Detection from Field Images Using Fine-Tuned CNNs and Symptom–Organ Level Labeling

Öz

Early and accurate detection of plant diseases and pests is critical to preventing yield and quality losses, supporting sustainable agriculture, and ensuring food security. In this study, a novel dataset of 6,081 field images showing disease and pest symptoms on apricot (Prunus armeniaca) plants was created. Three pre-trained convolutional neural networks (CNNs), namely AlexNet, GoogLeNet, and ResNet-50, were fine-tuned for the classification task. Instead of a standard labeling strategy, a detailed labeling method was proposed, which considers both symptom type and the affected plant organ. The CNNs were trained on two datasets: a traditional 7-class version and a 13-class version generated using the proposed method. All models were evaluated using 5-fold cross-validation. Among all model and dataset combinations, the highest accuracy of 93.9% was achieved by the ResNet-50 model on the 7-class dataset. Although the proposed labeling method resulted in a slight decrease in classification accuracy, the performance difference remained small even with more classes. These findings indicate that the method is dependable and suitable for practical applications.

Anahtar Kelimeler

• Apricot (Prunus armeniaca)
• Plant disease and pest detection
• Convolutional neural networks (CNNs)
• Transfer learning
• Detailed labeling

Destekleyen Kurum

General Directorate of Agricultural Research and Policies (TAGEM)

Proje Numarası

TAGEM/TSKAD/B/21/A9/P7/5030

Etik Beyan

This study was conducted in accordance with research and publication ethics. No experiments involving humans or animals were carried out, and no procedures requiring ethical committee approval were involved. The data used in the research were obtained from publicly available sources and/or used with the permission of the data owners. No unethical practices such as plagiarism, fabrication, falsification, duplication, salami publication, or unjustified authorship were involved in the publication process.

Teşekkür

This study was carried out as part of the completed project titled “Detection of Diseases and Pests on Apricot Trees Based on Field Images Using Deep Learning Techniques” (Project No: TAGEM/TSKAD/B/21/A9/P7/5030), funded and supported by the General Directorate of Agricultural Research and Policies (TAGEM) of the Ministry of Agriculture and Forestry of the Republic of Türkiye. We would like to thank TAGEM for their financial support. We also extend our gratitude to Erciyes University Faculty of Agriculture and the Ortaköy District Directorate of Agriculture and Forestry for their collaboration and valuable contributions during the fieldwork.

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