Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Abstract

Fruit and vegetable diseases have an important place in the food sector in terms of sustainable agricultural policies. Thus, ıt affects tissues, targeting and negatively impacting the food supply. In this study, Two separate Deep Learning (CNN, AlexNet) models were employed to detect this difference, visual damage and surface marker seen in fruits and vegetables. 22 strawberries and 18 tomato images were used for this analysis, and than data augmentation was implemented 600 images out of 40 images using the image reproduction broadcast. As a result, 83.3% success was achieved.

Keywords

• Deep learning
• convolutıonal neural network (cnn)
• fruit and vegetable disease

References

1. [1] Nakano, K. (1997). Application of neural networks to apple color grading. Computer and Electronics in Agriculture, 18 (2–3), 105–116.
2. [2] Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J., & Liu, C. (2014). Computer vision principles, developments and applications for external quality control of fruits and vegetables: A review. International Food Research. Elsevier Ltd. https://doi.org/10.1016/j.foodres.2014.03.012 [3] Jolly, P., & Raman, S. (2017). Analysis of Surface Defects in Apples Using Gabor Properties. In - Papers 12th International Conference on Signal Display Technology and Internet-based systems, SITIS 2016 (pp. 178-185). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SITIS.2016.36
3. [4] Aslan, M. (2021). Derin Öğrenme ile Şeftali Hastalıkların Tespiti. Avrupa Bilim ve Teknoloji Dergisi, (23), 540-546.
4. [5] Terzi, İ., Özgüven, M. M., & Yağcı, A. (2023). Derin Öğrenme Teknikleri ile Bazı Üzüm Çeşitlerinin Tespiti. Turkish Journal of Agriculture-Food Science and Technology, 11(1), 125-130.
5. [6] Sevli, O. (2022). Elma Bitkisi Hastalıklarının Derin Öğrenme İle Tespiti. International Euroasia Congress on Scientific Researches and Recent Trends 9, Antalya.
6. [7] Acar, E., Ertugrul, O. F., Aldemir, E., & Oztekin, A. (2022). Automatic identification of cassava leaf diseases utilizing morphological hidden patterns and multi-feature textures with a distributed structure-based classification approach. Journal of Plant Diseases and Protection, 129(3), 605-621.
7. [8] Banot, Mrs S. and PM, Dr. M. (2016). A fruit detection and grading system based on image processing. IJIREEICE, 4 (1), 47-52.
8. [9] Yapay Zeka ve Derin Öğrenme A-Z: TENSORFLOW https://www.udemy.com/course/yapayzeka/

9. [10] G.E. Hinton, “Learning multiple layers of representation,” Trend Cogn. Sci., vol. 11, no. 10, pp. 428-434, Oct.2007
10. [11] I. N. Aizenberg, N. N. Aizenberg, and J. Vandewalle, “Multiple- Valued Threshold Logic and Multi- Valued Neurons,” in Multi- Valued and Universal Binary Neurons, Boston, MA: Springer US, 2000, pp. 25-80.
11. [12] Targ, S. , Almeida, D., Lyman, K.(2016). Resnet in Resnet: Generalızıng Resıdual Archıtectures, Workshop track - ICLR 2016.
12. [13] Örenç, S., Emrullah, A., & Özerdem, M. S. (2022). Utilizing the ensemble of deep learning approaches to identify monkeypox disease. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 13(4), 685-691.
13. [14] Turk, O., Ozhan, D., Acar, E., Akinci, T. C., & Yilmaz, M. (2022). Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images. Zeitschrift für Medizinische Physik.
14. [15] S. Targ, D. Almeida, K. Lyman, Resnet in Resnet: Generalızıng Resıdual Archıtectures, Workshop track - ICLR 2016. Vs He, K., Zhang, X., Ren, S., Sun, J., (2015). Deep Residual Learning for Image Recognition, Microsoft Research. arXiv:1512.03385v1 [cs.CV] 10 Dec 2015.