2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 2 , PP: 213-228, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Adnan M. A. Shakarji 1 * , Adem Gölcük 2
Doi: https://doi.org/10.54216/JISIoT.140217
With an average annual intake of almost 20 kilograms per person, tomatoes are the most consumed vegetable worldwide. Diseases brought on by dangerous organisms are among the most important factors adversely affecting tomato production's output and quality. Depending on the climate and environmental conditions, tomatoes can be afflicted by a variety of illnesses throughout the planting and growing phases. It is essential for tomato growers to identify possible infections and take the appropriate preventative measures. Applications of artificial intelligence have grown in popularity recently. AI is being used in agriculture to identify plant illnesses. This research uses deep learning, a branch of artificial intelligence, to categories common tomato diseases. In the beginning, samples of frequently seen tomato illnesses were gathered from tomato growers in Kirkuk. Once there were enough data, the system developed with image processing algorithms produced meaningful images. Using a CNN-based GoogleNet deep learning system, the resulting dataset was trained and diseases were classified. The results show that the deep learning system that was constructed has a high degree of success and dependability when it comes to tomato disease classification.
Deep Learning , Tomato Disease Detection , Image Processing , GoogleNet , Artificial Intelligence
[1] Şahinli MA. Comparative advantage of agriculture sector between Turkey and European Union. African Journal of Agricultural Research 2013;8:884–95.
[2] Saygili H, Sahin F, Aysan Y, Mirik M. NEW SYMPTOMS OF TOMATO SOFT ROT DISEASES IN TURKEY. Acta Horticulturae 2005:291–4. https://doi.org/10.17660/actahortic.2005.695.32.
[3] ÖZDEMİR R, DEMİRBAŞ N. Meyve ve sebze üretiminde ortaya çıkan kayıplar üzerinde etkili olan faktörler: İzmir ili örneği. Mediterranean Agricultural Sciences 2020;33:85–91. https://doi.org/10.29136/mediterranean.659011.
[4] Shin J, Chang YK, Heung B, Nguyen-Quang T, Price GW, Al-Mallahi A. A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. Computers and Electronics in Agriculture 2021;183:106042. https://doi.org/10.1016/j.compag.2021.106042.
[5] Zhang S, Wu X, You Z, Zhang L. Leaf image based cucumber disease recognition using sparse representation classification. Computers and Electronics in Agriculture 2017;134:135–41. https://doi.org/10.1016/j.compag.2017.01.014.
[6] Shah JP, Prajapati HB, Dabhi VK. A survey on detection and classification of rice plant diseases. 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) 2016:1–8. https://doi.org/10.1109/icctac.2016.7567333.
[7] Ghosh S, Singh A, Kavita, Z. Jhanjhi N, Masud M, Aljahdali S. SVM and KNN Based CNN Architectures for Plant Classification. Computers, Materials & Continua 2022;71:4257–74. https://doi.org/10.32604/cmc.2022.023414.
[8] Sibiya M, Sumbwanyambe M. A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering 2019;1:119–31. https://doi.org/10.3390/agriengineering1010009.
[9] Soylu EM, Kurt Ş, Soylu S. In vitro and in vivo antifungal activities of the essential oils of various plants against tomato grey mould disease agent Botrytis cinerea. International Journal of Food Microbiology 2010;143:183–9. https://doi.org/10.1016/j.ijfoodmicro.2010.08.015.
[10] Albayrak Ü, Gölcük A, Aktaş S. Agaricus bisporus’ta Görüntü Tabanlı Hastalık Sınıflandırması için Kapsamlı Veri Seti. Journal of Fungus 2024;15:29–42. https://doi.org/10.30708/mantar.1452976.
[11] Yasar A, Golcuk A, Sari OF. Classification of bread wheat varieties with a combination of deep learning approach. European Food Research and Technology 2023;250:181–9. https://doi.org/10.1007/s00217-023-04375-x.
[12] Golcuk A, Yasar A. Classification of bread wheat genotypes by machine learning algorithms. Journal of Food Composition and Analysis 2023;119:105253. https://doi.org/10.1016/j.jfca.2023.105253.
[13] Chaerani R, Voorrips RE. Tomato early blight (Alternaria solani): the pathogen, genetics, and breeding for resistance. Journal of General Plant Pathology 2006;72:335–47. https://doi.org/10.1007/s10327-006-0299-3.
[14] Nowicki M, Foolad MR, Nowakowska M, Kozik EU. Potato and Tomato Late Blight Caused by Phytophthora infestans: An Overview of Pathology and Resistance Breeding. Plant Disease 2012;96:4–17. https://doi.org/10.1094/pdis-05-11-0458.
[15] Çelik I, Özalp R, Çelik N, Polat I, Sülü G. Development of long pepper (Capsicum annuum L.) lines resistant to Tomato spotted wilt virus (TSWV). Acta Horticulturae 2020:37–42. https://doi.org/10.17660/actahortic.2020.1282.7.
[16] Felipe V, Romero AM, Montecchia MS, Vojnov AA, Bianco MI, Yaryura PM. Xanthomonas vesicatoria virulence factors involved in early stages of bacterial spot development in tomato. Plant Pathology 2018;67:1936–43. https://doi.org/10.1111/ppa.12905.
[17] HORUZ S, TİRENG KARUT Ş, AYSAN Y. Domates bakteriyel kanser ve solgunluk hastalığı etmeni Clavibacter michiganensis subsp. michiganensis’in tohumda aranması ve tohum uygulamalarının patojen gelişimine etkisinin belirlenmesi. Tekirdağ Ziraat Fakültesi Dergisi 2019;16:284–96. https://doi.org/10.33462/jotaf.526167.
[18] PINAR AKTEPE B. Domateste bakteriyel benek hastalığının biyolojik mücadelesinde farklı bitki aktivatörleri ve biyolojik preparatların etkisi. Mustafa Kemal Üniversitesi Tarım Bilimleri Dergisi 2021;26:355–64. https://doi.org/10.37908/mkutbd.908921.
[19] Srikanth, M., Mohan, R. J., & Naik, M. C. (2024). Neutrosophic Logic-Based Crop Yield Prediction and Risk Assessment using Least Squares Regression. Neutrosophic Systems with Applications, 23, 1-12. https://doi.org/10.61356/j.nswa.2024.23362
[20] KILIÇARSLAN S, PACAL I. Domates Yapraklarında Hastalık Tespiti İçin Transfer Öğrenme Metotlarının Kullanılması. Mühendislik Bilimleri ve Araştırmaları Dergisi 2023;5:215–22. https://doi.org/10.46387/bjesr.1273729.
[21] Montajab, S. (2023). A View through Artificial Intelligence and Its Relationships with Machine Learning and Deep Learning. Pure Mathematics for Theoretical Computer Science, 08-17. DOI: https://doi.org/10.54216/PMTCS.020101
[22] Ertem S, Özbay E. Sınıflandırma Probleminde Derin Özellik Birleştirme Yaklaşımıyla Domates Yaprağı Görüntülerinde Hastalık Tespiti. Avrupa Bilim ve Teknoloji Dergisi 2022:84–92.
[23] Metlek S, Çetiner H. Matlab Ortamında Derin Öğrenme Uygulamaları. Ankara/Turkey 2021.
