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Saadati, Nadia, Ahmadi Teshnizi, Mohammadreza. (1404). Developing a computer vision system for classifying tree leaves. , 1(2), 101-105. doi: 10.22069/bere.2025.23451.1024
Nadia Saadati; Mohammadreza Ahmadi Teshnizi. "Developing a computer vision system for classifying tree leaves". , 1, 2, 1404, 101-105. doi: 10.22069/bere.2025.23451.1024
Saadati, Nadia, Ahmadi Teshnizi, Mohammadreza. (1404). 'Developing a computer vision system for classifying tree leaves', , 1(2), pp. 101-105. doi: 10.22069/bere.2025.23451.1024
Saadati, Nadia, Ahmadi Teshnizi, Mohammadreza. Developing a computer vision system for classifying tree leaves. , 1404; 1(2): 101-105. doi: 10.22069/bere.2025.23451.1024

Developing a computer vision system for classifying tree leaves

Biosystems Engineering and Renewable Energies
دوره 1، شماره 2، آذر 2025، صفحه 101-105    اصل مقاله (375.95 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22069/bere.2025.23451.1024
نویسندگان
Nadia Saadati* 1؛ Mohammadreza Ahmadi Teshnizi2
1Department of Agricultural Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
2Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
چکیده
The identification and classification of tree leaves hold significant importance in botanical and agricultural research. This study focuses on the development of an advanced computer vision system for classifying leaves from five distinct tree species, including pomegranate, fig, almond, raspberry, and hawthorn. The proposed system utilizes a dataset comprising 525 digital images to extract diverse features from the color domain and the gray-level co-occurrence matrix (GLCM). From each image, 126 features are extracted from the color domain and 80 features from the GLCM. The feature selection process is conducted using an advanced method that combines artificial neural networks (ANN) with ant colony optimization (ACO). This approach aids in identifying key features, including the angular second moment, angular contrast, angular maximum probability, the normalized difference index in the CMY and HSV color spaces, and the standard deviation of the first component in the YCbCr color space. Leaf classification is performed using a hybrid ANN and metaphor competition algorithm, achieving a classification accuracy of 93%. This system serves as an effective tool for precise and efficient leaf classification and has the potential to lead to significant advancements in botanical research and agricultural applications. Furthermore, the results of this study indicate that the use of intelligent methods in feature selection can enhance the accuracy and efficiency of classification models. Ultimately, this research emphasizes the importance of developing advanced techniques for leaf identification and classification, representing a crucial step toward improving existing methods in this field.

تازه های تحقیق
  • From each image, 126 features were extracted from the color domain and 80 features from the GLCM.
  • The YCbCr color space has emerged as the most effective for segmentation tasks.
  • The hybrid ANN-PSO classifier has demonstrated a remarkable accuracy rate of 93%.
  • This system holds significant promise for precise and efficient leaf classification.
کلیدواژه‌ها
Computer vision؛ Feature extraction؛ Image processing؛ Intelligent algorithms؛ Leaf classification
مراجع

Bambil, D., Pistori, H., Bao, F., Weber, V., Alves, F. M., Gonçalves, E. G., & Bortolotto, I. M. (2020). Plant species identification using color learning resources, shape, texture, through machine learning and artificial neural networks. Environment Systems and Decisions, 40(4), 480-484.‏

Çuğu, İ., Şener, E., Erciyes, Ç., Balcı, B., Akın, E., Önal, I., & Akyüz, A. O. (2017). Treelogy: A novel tree classifier utilizing deep and hand-crafted representations. arXiv preprint arXiv:1701.08291.‏   

Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2010). Digital image processing using MATLAB. Tata McGraw Hill Education.‏

Hamuda, E., Glavin, M., & Jones, E. (2016). A survey of image processing techniques for plant extraction and segmentation in the field. Computers and electronics in agriculture, 125, 184-199.‏

Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, 6, 610-621.‏

Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on pattern analysis and machine intelligence, 22(1), 4-37.‏

Kaur, S., & Kaur, P. (2019). Plant species identification based on plant leaf using computer vision and machine learning techniques. Journal of Multimedia Information System, 6(2), 49-60.‏

Kumar, N., Belhumeur, P. N., Biswas, A., Jacobs, D. W., Kress, W. J., Lopez, I. C., & Soares, J. V. (2012). Leafsnap: A computer vision system for automatic plant species identification. In Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part II 12 (pp. 502-516). Springer Berlin Heidelberg.‏

Mangaoang, E. V. D., & Samaniego, J. M. (2023). Leaf-based Classification of Important Indigenous Tree Species by Different Feature Extraction Techniques and Selected Classification Algorithms. International Journal of Advanced Computer Science and Applications, 14(1).

Mursalin, M., Hossain, M. M., Noman, M. K., & Azam, M. S. (2013). Performance analysis among different classifier including naive bayes, support vector machine and C4. 5 for automatic weeds classification. Global Journal of Computer Science and Technology Graphics & Vision, 13(3).‏

Novotný, P., & Suk, T. (2013). Leaf recognition of woody species in Central Europe. biosystems engineering, 115(4), 444-452.‏

Petrou, M. M., & Petrou, C. 2010. Image processing: the fundamentals. John Wiley & Sons.‏

Powers, D. M. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061.‏

Sabzi, S., Pourdarbani, R., & Arribas, J. I. (2020). A computer vision system for the automatic classification of five varieties of tree leaf images. Computers, 9, 6.‏

Satti, V., Satya, A., & Sharma, S. (2013). An automatic leaf recognition system for plant identification using machine vision technology. International journal of engineering science and technology, 5(4), 874.‏

Unger, J., Merhof, D., & Renner, S. (2016). Computer vision applied to herbarium specimens of German trees: testing the future utility of the millions of herbarium specimen images for automated identification. BMC Evolutionary Biology, 16, 1-7.‏

Wei Tan, J., Chang, S. W., Abdul-Kareem, S., Yap, H. J., & Yong, K. T. (2018). Deep learning for plant species classification using leaf vein morphometric. IEEE/ACM Transactions on computational biology and bioinformatics, 17, 82-90.‏

Yaswanth, T. N. S., Lakshmi, K. V., Vamsi, K. G., Supraja, D., & Vani, K. S. (2023). Classification of Medicinal Leaves using SVM. In 2023 World Conference on Communication & Computing (WCONF) (pp. 1-7).

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