Detecting Wheat Leaf Diseases: A Deep Feature-Based Approach with Machine Learning Classification

Öz

Wheat is a rich storehouse of nutrients with many different vitamins and minerals. Wheat is one of the main cereals that meet the nutritional needs of humans and other living things and is used in the production of other foods. It can be grown in almost all regions of the world. It requires less irrigation compared to other plants. One of the most important problems in wheat cultivation is the fight against diseases. Wheat diseases cause yield losses and quality decreases as in other agricultural products. Timely and accurate diagnosis of these diseases; It is clear that it will lead to an increase in yield and quality. Detection of these diseases with the naked eye can be difficult and laborious. In this study, diseases on wheat leaves were detected using image processing techniques. The features of septoria and stripe rust diseases on wheat leaves were extracted using pre-trained networks VGG-16, VGG-19 and then classified with machine learning algorithms support vector machines (SVM), multi-layer perceptron (MLP), k-nearest neighbor (KNN). The results obtained were evaluated with performance criteria such as accuracy, sensitivity, specificity, precision and F1-Score. In the analysis, the features extracted with VGG-19 were classified with SVM method and the highest classification accuracy of 98.63% was achieved.

Anahtar Kelimeler

• Wheat Leaf Disease
• Deep Learning
• deep fetures

Kaynakça

1. A Usha Ruby, J Georga Cchandran Chellin Chaithanya, N CB, J, SJ T, & Patil R (2022). Wheat leaf disease classification using modified resnet50 convolutional neural network model [Preprint]. In Review. https://doi.org/10.21203/rs.3.rs-2130789/v1
2. Atar B (2017). Gıdamız buğdayın, geçmişten geleceğe yolculuğu. Süleyman Demirel Üniversitesi Yalvaç Akademi Dergisi 2 (1): 1-12.
3. Avuçlu E (2023). Classification of pistachio images with the resnet deep learning model. Selcuk Journal of Agricultural and Food Sciences, 2. https://doi.org/10.15316/SJAFS.2023.029
4. Bukhari HR, Mumtaz R, Inayat, S, Shafi, U, Haq, I U, Zaidi, SMH, Hafeez, M (2021). Assessing the impact of segmentation on wheat stripe rust disease classification using computer vision and deep learning. IEEE Access 9: 164986–165004. https://doi.org/10.1109/ACCESS.2021.3134196
5. Çat A, Tekin M, Çatal M, Akan K, Akar T (2017). Buğdayda sarı pas hastalığı ve dayanıklılık ıslahı çalışmaları. Mediterranean Agricultural Sciences 30(2): 97-105.
6. Catal Reis H, & Turk V (2024). Integrated deep learning and ensemble learning model for deep feature-based wheat disease detection. Microchemical Journal 197: 109790. https://doi.org/10.1016/j.microc.2023.109790
7. Çetiner H (2021). Yaprak hastalıklarının sınıflandırılabilmesi için önceden eğitilmiş ağ tabanlı derin ağ modeli. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 8(15): 442–456. https://doi.org/10.54365/adyumbd.988049
8. Cheng S, Cheng H, Yang R, Zhou J, Li Z, Shi B, Lee M, Ma Q (2023). A high performance wheat disease detection based on position information. Plants 12(5),:1191. https://doi.org/10.3390/plants12051191

9. Dikici B, Bekçioğullari MF, Açikgöz H, Korkmaz D (2022). Zeytin yaprağındaki hastalıkların sınıflandırılmasında ön eğitimli evrişimli sinir ağlarının performanslarının incelenmesi. Konya Journal of Engineering Sciences 10(3): 535–547. https://doi.org/10.36306/konjes.1078358
10. El-Sayed R, Darwish A, Hassanien AE (2023). Wheat leaf-disease detection using machine learning techniques for sustainable food quality. In A. E. Hassanien & M. Soliman (Eds.), Artificial Intelligence: A Real Opportunity in the Food Industry. Springer International Publishing, pp. 17–28. https://doi.org/10.1007/978-3-031-13702-0_2
11. Erdem E, Bozkurt F (2021). Prostat kanseri tahmini için çeşitli denetimli makine öğrenimi tekniklerinin karşılaştırılması. European Journal of Science and Technology 21: 610–620. https://doi.org/10.31590/ejosat.802810
12. Figueroa M, Hammond‐Kosack K E, & Solomon P S (2018). A review of wheat diseases—A field perspective. Molecular Plant Pathology 19(6): 1523–1536. https://doi.org/10.1111/mpp.12618
13. Gao R, Jin F, Ji M, Zuo Y (2023). Research on the method of ıdentifying the severity of wheat stripe rust based on machine vision. Agriculture 13(12): 2187. https://doi.org/10.3390/agriculture13122187
14. Getachew H (2021). Wheat leaf dataset [dataset]. Mendeley. https://doi.org/10.17632/WGD66F8N6H.1
15. Goyal L, Sharma CM, Singh A, Singh PK (2021). Leaf and spike wheat disease detection & classification using an improved deep convolutional architecture. Informatics in Medicine Unlocked 25: 100642. https://doi.org/10.1016/j.imu.2021.100642
16. Karagül K (2014). The classification of the firms traded in istanbul stock exchange by using support vector machines. Pamukkale University Journal of Engineering Sciences 20(5): 174–178. https://doi.org/10.5505/pajes.2014.63835
17. Khan H, Haq IU, Munsif M, Mustaqeem Khan SU, Lee M Y (2022). Automated wheat diseases classification framework using advanced machine learning technique. Agriculture 12(8): 1226. https://doi.org/10.3390/agriculture12081226
18. Kilinç N, Dikilitaş M, Kayim M, Ünal G (2021). Septoria yaprak leke hastalığı etmeni Zymoseptoria tritici (Desm. Quaedvlieg & Crous)’ye ait izolatların farklı sıcaklıklardaki fizyolojik ve biyokimyasal özelliklerin belirlenmesi. Harran
19. Tarım ve Gıda Bilimleri Dergisi 25(4): 469–479. https://doi.org/10.29050/harranziraat.897692
20. Long M, Hartley M, Morris RJ, Brown JKM (2023). Classification of wheat diseases using deep learning networks with field and glasshouse images. Plant Pathology 72(3): 536–547. https://doi.org/10.1111/ppa.13684
21. Mrinal K, Tathagata H, Sanjaya Shankar T (2017). Wheat leaf disease detection using image processing. International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) VI(IV): ISSN 2278-2540
22. Mustafa Z (2020). Distribution of Septoria tritici blotch disease agent Zymoseptoria tritici mating type idiomorphs in Turkey. Bitki Koruma Bülteni 60(3): 33–38. https://doi.org/10.16955/bitkorb.656918
23. Nigam S, Jain R, Marwaha S, Arora A (2021). 12 Wheat rust disease identification using deep learning. In J. M. Chatterjee, A. Kumar, P. S. Rathore, & V. Jain (Eds.), Internet of Things and Machine Learning in Agriculture (pp. 239–250). De Gruyter. https://doi.org/10.1515/9783110691276-012
24. Özkan K, Seke E, Işık Ş (2021). Wheat kernels classification using visible-near infrared camera based on deep learning. Pamukkale University Journal of Engineering Sciences 27(5): 618–626. https://doi.org/10.5505/pajes.2020.80774
25. Sarkar C, Gupta D, Gupta U, Hazarika BB (2023). Leaf disease detection using machine learning and deep learning: Review and challenges. Applied Soft Computing 145: 110534. https://doi.org/10.1016/j.asoc.2023.110534
26. Sheenam S, Khattar S, Verma T (2023). Automated wheat plant disease detection using deep learning: a multi-class classification approach. 2023 3rd International Conference on Intelligent Technologies (CONIT), 1–5. https://doi.org/10.1109/CONIT59222.2023.10205683
27. Takci H (2022). Optimum parametreler yardımıyla performansı artırılmış KNN algoritması tabanlı kalp hastalığı tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 38(1): 451–460. https://doi.org/10.17341/gazimmfd.977127
28. Toğaçar M, Ergen B, Özyurt F (2020). Evrişimsel sinir ağı modellerinde özellik seçim yöntemlerini kullanarak çiçek görüntülerinin sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 32(1): 47-56. https://doi.org/10.35234/fumbd.573630
29. Wen X, Zeng M, Chen J, Maimaiti M, Liu Q (2023). Recognition of wheat leaf diseases using lightweight convolutional neural networks against complex backgrounds. Life 13(11): 2125. https://doi.org/10.3390/life13112125
30. Xu L, Cao B, Zhao F, Ning S, Xu P, Zhang W, Hou X (2023). Wheat leaf disease identification based on deep learning algorithms. Physiological and Molecular Plant Pathology (123): 101940. https://doi.org/10.1016/j.pmpp.2022.101940