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Journals / Meyve Bilimi / 2026 / Cilt: 12 Sayı: 1

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

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

Yahya Altuntaş(MEYVECİLİK ARAŞTIRMA ENSTİTÜSÜ MÜDÜRLÜĞÜ)0000-0002-7472-8251
Yusuf Karakuş(KAYISI ARAŞTIRMA ENSTİTÜSÜ MÜDÜRLÜĞÜ)0000-0002-5992-1926
Pages
88–99
Publication Date
2025-07-09
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings indicate that the method is dependable and suitable for practical applications.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Early and accurate detection of plant diseases and pests is critical to preventing yield and quality losses, supporting sustainable agriculture, and ensuring food security.
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Key Findings & Data:In this study, a novel dataset of 6,081 field images showing disease and pest symptoms on apricot (Prunus armeniaca) plants was created.
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Conclusion & Impact:These findings indicate that the method is dependable and suitable for practical applications.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kayısı (Prunus armeniaca) · Transfer öğrenme · Ayrıntılı etiketleme

Abstract

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.

Özet

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.

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Cite This Article

Altuntaş, Y., Karakuş, Y. (2025). Apricot Plant Disease and Pest Detection from Field Images Using Fine-Tuned CNNs and Symptom–Organ Level Labeling. Meyve Bilimi, Cilt: 12 Sayı: 1, 88–99. https://doi.org/10.51532/meyve.1689356

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