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

Yıl 2025, Cilt: 12 Sayı: 1, 88 - 99, 09.07.2025

Yahya Altuntaş , Yusuf Karakuş

https://doi.org/10.51532/meyve.1689356

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

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.

Destekleyen Kurum

General Directorate of Agricultural Research and Policies (TAGEM)

Proje Numarası

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

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.

Kaynakça

• Ahmad A, Saraswat D, El Gamal A, 2023. A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agricultural Technology 3(June 2022), 100083. https://doi.org/10.1016/j.atech.2022.100083
• Altuntaş Y, Cömert Z, Kocamaz AF, 2019. Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach. Computers and Electronics in Agriculture 163: 104874. https://doi.org/10.1016/j.compag.2019.104874
• Altuntaş Y, Kocamaz AF, 2021. Deep Feature Extraction for Detection of Tomato Plant Diseases and Pests based on Leaf Images. Celal Bayar University Journal of Science 17(2): 145–152. https://doi.org/10.18466/cbayarfbe.812375
• Ashurov AY, Al-Gaashani MSAM, Samee NA, Alkanhel R, Atteia G, Abdallah HA, Saleh Ali Muthanna M, 2024. Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections. Frontiers in Plant Science 15: 1–16. https://doi.org/10.3389/fpls.2024.1505857
• Falaschetti L, Manoni L, Di Leo D, Pau D, Tomaselli, V, Turchetti C, 2022. A CNN-based image detector for plant leaf diseases classification. HardwareX 12: e00363. https://doi.org/10.1016/j.ohx.2022.e00363.
• Ferentinos KP, 2018. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145: 311–318. https://doi.org/10.1016/j.compag.2018.01.009
• He K, Girshick R, Dollar P, 2019. Rethinking imageNet pre-training. Proceedings of the IEEE International Conference on Computer Vision 2019-Octob(ii): 4917–4926. https://doi.org/10.1109/ICCV.2019.00502
• He K, Zhang X, Ren S, Sun J, 2016. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 770–778.
• Kornblith S, Shlens J, Le QV, 2019. Do Better ImageNet Models Transfer Better? Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2661–2671.
• Krizhevsky A, Sutskever I, Hinton GE, 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM 60(6): 84–90. https://doi.org/10.1145/3065386
• Lecun Y, Bengio Y, Hinton G, 2015. Deep learning. Nature 521(7553): 436–444. https://doi.org/10.1038/nature14539
• Liu J, Wang X, 2021. Plant diseases and pests detection based on deep learning: a review. Plant Methods 17(1): 1–18. https://doi.org/10.1186/s13007-021-00722-9
• Mohanty SP, Hughes DP, Salathé M, 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7: 1–10. https://doi.org/10.3389/fpls.2016.01419
• Moupojou E, Tagne A, Retraint F, Tadonkemwa A, Wilfried D, Tapamo H, Nkenlifack M, 2023. FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning. IEEE Access 11: 35398–35410. https://doi.org/10.1109/ACCESS.2023.3263042
• Murphy KP, 2012. Machine learning: a probabilistic perspective. The MIT Press.
• Perumal VK, Supriyaa T, Santhosh PR, Dhanasekaran S, 2024. CNN based plant disease identification using PYNQ FPGA. Systems and Soft Computing 6: 200088. https://doi.org/10.1016/j.sasc.2024.200088
• Shafik W, Tufail A, De Silva Liyanage C, Apong RAAHM, 2024. Using transfer learning-based plant disease classification and detection for sustainable agriculture. BMC Plant Biology 24(1): 1–19. https://doi.org/10.1186/s12870-024-04825-y
• Shoaib M, Sadeghi-Niaraki A, Ali F, Hussain I, Khalid S, 2025. Leveraging deep learning for plant disease and pest detection: a comprehensive review and future directions. Frontiers in Plant Science 16: 1–19. https://doi.org/10.3389/fpls.2025.1538163
• Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A, 2015. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1–9.
• Tarım ve Orman Bakanlığı, 2022. Kayısı entegre mücadele teknik talimatı [Technical guidelines for the integrated control of apricot pests and diseases] (in Turkish).
• Too EC, Yujian L, Njuki S, Yingchun L, 2019. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture 161: 272–279. https://doi.org/10.1016/j.compag.2018.03.032
• Tugrul B, Elfatimi E, Eryigit R, 2022. Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review. Agriculture (Switzerland) 12(8). https://doi.org/10.3390/agriculture12081192
• Turkoglu M, Hanbay K, Sarac Sivrikaya I, Hanbay D, 2020. Derin Evrişimsel Sinir Ağı Kullanılarak Kayısı Hastalıklarının Sınıflandırılması. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9(1), 334–345. https://doi.org/10.17798/bitlisfen.562101
• Turkoglu, M., Yanikoğlu, B., Hanbay, D. (2022). PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection. Signal, Image and Video Processing 16(2): 301–309. https://doi.org/10.1007/s11760-021-01909-2
• Wei XS, Song YZ, Aodha O Mac, Wu J, Peng Y, Tang J, Yang J, Belongie S, 2022. Fine-Grained Image Analysis With Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12): 8927–8948. https://doi.org/10.1109/TPAMI.2021.3126648
• Yao X, Lin H, Bai D, Zhou H, 2024. A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning. Forests 15(4). https://doi.org/10.3390/f15040591