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

Authors : Yahya Altuntaş, Yusuf Karakuş

Pages : 88-99

Doi:10.51532/meyve.1689356

View : 170 | Download : 151

Publication Date : 2025-07-09

Article Type : Research Paper

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.
Keywords : Kayısı (Prunus armeniaca), Transfer öğrenme, Ayrıntılı etiketleme, Bitki hastalık ve zararlı tespiti, Evrişimsel sinir ağları (CNNs)

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