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Detecting and classifying diseases in agricultural crops is crucial for ensuring crop health and maximizing yields. In the case of walnut trees, timely identification of fungal diseases is essential to prevent their spread and minimize economic losses. Traditional methods of disease diagnosis rely on visual inspection by experts, which can be time-consuming and prone to human errors. In this study, we propose an intelligent system for the detection and classification of walnut fungi diseases using machine-learning techniques. The system leverages the power of image processing and pattern recognition algorithms to automate the identification process, enabling rapid and accurate diagnosis. The system is trained using a dataset comprising high-resolution images of healthy walnut leaves and leaves affected by various fungal diseases. We extract relevant features from the images and employ state-of-the-art machine learning algorithms, such as back-propagation neural network (BPNN), to learn the complex patterns associated with each disease. To evaluate the performance of our system, we conducted extensive experiments on a real-world dataset, achieving high accuracy rates for disease detection and classification. The system successfully identifies common walnut fungi diseases, including walnut blight, anthracnose, and powdery mildew, with accuracy rates exceeding 90%. Moreover, it exhibits robustness against variations in lighting conditions and leaf orientations. Our results demonstrate the potential of machine learning techniques in revolutionizing disease diagnosis in the agricultural domain. The proposed system offers a cost-effective and scalable solution for farmers and agronomists to monitor the health of walnut trees, detect diseases at an early stage, and apply targeted treatments. By providing accurate and timely diagnoses, this technology can contribute to reducing crop losses and improving overall productivity in walnut cultivation.

Article Details

How to Cite
Miracle A, A. (2023). Intelligent Detection and Classification of Walnut Fungi Diseases Using Machine Learning . International Journal of Multidisciplinary Studies and Innovative Research, 11(2), 1412–1426.


  1. A. Rehman, L. Jingdong, B. Shahzad, A. A. Chandio, I. Hussain et al., “Economic perspectives of major field crops of Pakistan: An empirical study,” Pacific Science Review B: Humanities and Social Sciences, vol. 1, no. 3, pp. 145–158, 2015.
  2. A. Azam and M. Shafique, “Agriculture in Pakistan and its impact on economy. A review,” International Journal of Advanced Science and Technology, vol. 103, pp. 47–60, 2017.
  3. M. Sameeullah and T. Karadenİz, “Walnut production status in Pakistan,” Bahçe, vol. 46, no. 2, pp. 113–115, 2017.
  4. H. Mudasir and K. Ahmad, “Anthracnose disease of walnut—A review,” International Journal of Environment, Agriculture and Biotechnology, vol. 2, no. 5, pp. 238908, 2017.
  5. P. Pollegioni, G. V. Linden, A. Belisario, M. Gras, N. Anselmi et al., “Mechanisms governing the responses to anthracnose pathogen in Juglans spp,” Journal of Biotechnology, vol. 159, no. 4, pp. 251–264, 2012.
  6. Z. Chuanlei, Z. Shanwen, Y. Jucheng, S. Yancui and C. Jia, “Apple leaf disease identification using genetic algorithm and correlation-based feature selection method,” International Journal of Agricultural and Biological Engineering, vol. 10, no. 2, pp. 74–83, 2017.
  7. T. G. Devi and P. Neelamegam, “Image processing-based rice plant leaves diseases in Thanjavur, Tamilnadu,”
  8. Cluster Computing, vol. 22, no. 6, pp. 13415–13428, 2019.
  9. S. Khalesi, A. Mahmoudi, A. Hosainpour and A. Alipour, “Detection of walnut varieties using impact acoustics and artificial neural networks (ANNs),” Modern Applied Science, vol. 6, no. 1, pp. 43, 2012.
  10. B. Tigadi and B. Sharma, “Banana plant disease detection and grading using image processing,” International Journal of Engineering Science, vol. 6, no. 6, pp. 6512–6516, 2016.
  11. M. Bhange and H. Hingoliwala, “Smart farming: Pomegranate disease detection using image processing,”
  12. Procedia Computer Science, vol. 58, pp. 280–288, 2015.
  13. H. Waghmare, R. Kokare and Y. Dandawate, “Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system,” in Proc. 3rd Int. Conf. on Signal Processing and Integrated Networks (SPIN), Noida: Amity University, pp. 513–518, 2016.
  14. A. Awate, D. Deshmankar, G. Amrutkar, G. Amrutkar, U. Bagul et al., “Fruit disease detection using color, texture analysis and ANN,” in Proc. ICGCIoT, Greater Noida, Delhi, India, pp. 970–975, 2015.
  15. D. E. Kusumandari, M. Adzkia, S. P. Gultom, M. Turnip and A. Turnip, “Detection of strawberry plant disease based on leaf spot using color segmentation,” In Proc. 2nd Int. Conf. on Mechanical, Electronics, Computer, and Industrial Technology, vol. 1230, no. 1, pp. 012092, 2019.
  16. I. S. Areni and R. Tamin, “Image processing system for early detection of cocoa fruit pest attack,” in Proc. 3rd Int. Conf. on Mathematics, Sciences, Technology, Education and Their Applications, vol. 1244, Sulawesi, Selatan, Indonesia, pp. 1, 2019.
  17. J. Zhu, A. Wu, X. Wang and H. Zhang, “Identification of grape diseases using image analysis and BP neural networks,” Multimedia Tools and Applications, vol. 79, pp. 14539–14551, 2019.
  18. Y. Feng, H. Zhao, X. Li, X. Zhang and H. Li, “A multi-scale 3D Otsu thresholding algorithm for medical image segmentation,” Digital Signal Processing, vol. 60, pp. 186–199, 2017.
  19. D. Liu and J. Yu, “Otsu method and k-means,” in Proc. of the 2009 Ninth Int. Conf. on Hybrid Intelligent Systems, Washington, DC, United States, 1, pp. 344–349, 2009.
  20. J. Yousefi, Image binarization using Otsu thresholding algorithm. Ontario, Canada: University of Guelph, 2011.
  21. P. S. Suhasini, K. S. Krishna and I. M. Krishna, “Content based image retrieval based on different global and local color histogram methods: A survey,” Journal of the Institution of Engineers (India): Series B, vol. 98, no. 1, pp. 129–135, 2017.
  22. S. M. Singh and K. Hemachandran, “Image retrieval based on the combination of color histogram and color moment,” International Journal of Computer Applications, vol. 58, no. 3, pp. 27–34, 2012.
  23. P. S. Kumar and V. Dharun, “Extraction of texture features using GLCM and shape features using connected regions,” International Journal of Engineering and Technology, vol. 8, no. 6, pp. 2926–2930, 2016.
  24. J. Zhu, W. Ang, W. Xiushan and Z. Hao, “Identification of grape diseases using image analysis and BP neural networks,” Multimedia Tools and Applications, vol. 79, no. 21, pp. 14539–14551, 2020.
  25. B. Prakash and A. Yerpude, “Identification of mango leaf disease and control prediction using image processing and neural network,” International Journal for Scientific Research & Development, vol. 3, no. 5, pp. 794–799, 2015.
  26. S. Suthaharan, “Support vector machine,” in Machine Learning Models and Algorithms for Big Data Classification, Boston, MA: Springer, pp. 207–235, 2016.
  27. M. H. Saleem, S. Khanchi, J. Potgieter and K. M. Arif, “Image-based plant disease identification by deep learning meta-architectures,” Plants, vol. 9, no. 11, pp. p.–1451, 2020.
  28. K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and Electronics in Agriculture, vol. 145, pp. 311–318, 2018.
  29. S. B. Jadhav, V. R. Udupi and S. B. Patil, “Identification of plant diseases using convolutional neural networks,”
  30. International Journal of Information Technology, vol. 2020, no. 1, pp. 1–10, 2020.
  31. S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk and D. Stefanovic, “Deep neural networks based recognition of plant diseases by leaf image classification,” Computational Intelligence and Neuroscience, vol. 2016, no. 1, pp. 1–11, 2016.
  32. K. Subhadra and N. Kavitha, “A hybrid leaf disease detection scheme using gray co-occurance matrix support vector machine algorithm,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 2S11, pp. 2277–3878, 2019.