Apricot Disease Detection with Convolutional Neural Network

Year 2026, Volume: 15 Issue: 1 , 31 - 42 , 30.03.2026

Zeynep Mine Alçin , Muzaffer Aslan

https://doi.org/10.46810/tdfd.1774549

https://izlik.org/JA25AE46UR

Abstract

Apricot is a stone fruit grown in temperate climates and possesses high economic value globally. However, diseases and pests pose substantial threats to apricot production, undermining both crop quality and overall yield. As these pressures intensify, they further compromise fruit development and reduce harvest quantities, negatively affecting market value and productivity. In particular, canker, coryneum beijerinckii, drying symptom, and monilinia laxa stand out as the four main diseases that markedly reduce quality and yield worldwide. Therefore, early diagnosis and targeted management strategies for these diseases are critically important for preventing epidemic spread and ensuring efficient resource utilization. In this study, a novel deep learning-based convolutional neural network model is proposed for the detection of diseased apricot images. The proposed CNN model was tested on a publicly available dataset, meticulously compiled under real field conditions and encompassing the aforementioned four apricot diseases. The proposed model achieved a high accuracy rate of 97.74% in the detection and classification of diseases. It provided 8.1% to 21.16% higher accuracy than traditional image processing-based approaches in the literature. Furthermore, the final model achieved 0.44% to 23.87% higher performance compared to some CNN models. These results indicate that the proposed CNN model can provide rapid and reliable decision support in disease detection.

Keywords

Detection of apricot diseases , Deep Learning , Convolutional neural networks

Evrişimli Sinir Ağı ile Kayısı Hastalıklarının Tespiti

https://izlik.org/JA25AE46UR

Abstract

Kayısı, ılıman iklimlerde yetişen ve küresel ölçekte yüksek ekonomik katma değere sahip çekirdekli bir meyvedir. Ancak hastalık ve zararlılar kayısı üretimini önemli ölçüde tehdit ederek kalite ve verimi olumsuz etkilemektedir. Özellikle kanser, çil hastalığı, kuruma belirtileri ve monilya hastalığı, dünya genelinde kalite ve verimi belirgin biçimde düşüren dört ana hastalık olarak öne çıkmaktadır. Bu nedenle, bu hastalıkların erken teşhis ve hedefe yönelik yönetim stratejileri, epidemik yayılımın önlenmesi ve kaynakların verimli kullanımı açısından kritik öneme sahiptir. Çalışmada, hastalıklı kayısı görüntülerini tespiti için derin öğrenme temelli yeni bir evrişimli sinir ağ modeli önerilmiştir. Önerilen CNN modeli, gerçek saha koşulları altında titizlikle derlenmiş ve söz konusu dört hastalığı kapsayan halka açık bir veri seti üzerinde test edilmiştir. Önerilen model, hastalıkların tespiti ve sınıflandırmasında %97.74 gibi yüksek bir doğruluk oranına ulaşmıştır. Literatürdeki geleneksel görüntü işleme tabanlı yaklaşımlarda %8.1 ile 21.16 daha yüksek doğruluk sağlamıştır. Ayrıca son model bazı CNN Modelleri ile karşılaştırıldığında %0.44 ile %23.87 oranında daha yüksek başarım elde etmiştir. Bu sonuçlar, önerilen CNN modelinin hastalık tespitinde hızlı, güvenilir karar desteği sağlayabileceğini ortaya koymaktadır.

Keywords

Kayısı hastalıkların tespiti , Derin öğrenme , Evrişimli sinir ağları

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