International Journal of Knowledge Based Computer System

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Mango Leaf Diseases Detection using Deep Learning


Affiliations
1 M.Tech. Student, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, India
2 PhD Research Scholar, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, India
3 Sr. Assistant Professor, Department of Computer Science and IT, Bhaderwah Campus, University of Jammu, Jammu and Kashmir, India
4 Professor, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, India
     

   Subscribe/Renew Journal


Diseases and pests cause great economic loss to the mango industry every year. The detection of various mango diseases is challenging for the farmers as the symptoms produced by different diseases may be very similar, and may be present simultaneously. This research paper is an attempt to provide the timely and accurate detection and identification of mango leaf diseases. Convolutional Neural Networks are end-to-end learning algorithms which perform automatic feature extraction and learn complex features directly from raw images, making them suitable for a wide variety of tasks like image classification, object detection, segmentation etc. In the proposed study, we develop a Convolutional Neural Networks based model for detection and classification of mango leaf diseases at the initial stages. Data augmentation is performed on a collected dataset. We applied data augmentation techniques like rotation, translation, reflection and scaling. Convolutional Neural Networks model has been trained on the augmented data for detection and classification of mango leaf diseases. The proposed CNN based model attains 90.36% of accuracy. The results validate that the proposed method is effective in detecting various types of mango leaf diseases and can be used as a practical tool by farmers and agriculture scientists.

Keywords

Convolution Neural Network (CNN), Crop, Deep learning, Image classification, Mango
Subscription Login to verify subscription
User
Notifications
Font Size


  • S. Gulvani, and R. Patil, “Deep learning for image based mango leaf disease detection,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8. no. 3S3, pp. 54-56. 2019.

  • M. M. Krishna, M. Neelima, M. Harshali, and M. V. G. Rao, “Image classification using deep learning,” International Journal of Engineering & Technology, vol. 7, no. 2.7, pp. 614-617, 2018.

  • G. Geetha, S. Samundeswari, G. Saranya, K. Meenakshi, and M. Nithya, “Plant leaf disease classification and detection system using machine learning,” Journal of Physics: Conference Series, vol. 1712, 2020.

  • O. Parkash, and A. K. Mishra, “Important diseases of mango and their effect on production,” Biological Memoirs, vol. 18, no. 1&2, pp. 39-55, 1992.

  • R. Saleem, J. H. Shah, M. Sharif, M. Yasmin, H.-S. Yong, and J. Cha, “Mango leaf disease recognition and classification using novel segmentation and vein pattern technique,” Appl. Sci., vol. 11, no. 24, 2021, Art. no. 11901.

  • S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, 2016.

  • S. Kumar, V. Chaudhary, and S. Chandra, “Plant disease detection using CNN,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 12, 2021.

  • J. Liu, and X. Wang, “Plant diseases and pests detection based on deep learning,” Plant Methods, vol. 17, 2021, Art. no. 22.

  • R. Rinu, and S. H. Manjula, “Plant disease detection and classification using CNN,” International Journal of Recent Technology and Engineering (IJRTE), vol. 10, no. 3, pp. 152-156, 2021.

  • U. S. Rao, R. Swathi, V. Sanjanaa, L. Arpitha, K. Chandrasekhar, Chinmayi, and P. K. Naikb, “Deep learning precision farming: Grapes and mango leaf disease detection by transfer learning,” Global Transitions Proceedings, vol. 2, no. 2, pp. 535-544, 2021.

  • H. Muresan, and M. Oltean, “Fruit recognition from images using deep learning,” Acta Univ. Sapientiae Inform., vol. 10, pp. 26-42, 2018.

  • S. Indolia, A. Goswami, S. P. Mishra, and P. Asopa, “Conceptual understanding of convolutional neural network,” Procedia Computer Science, vol. 132, pp. 679-688, 2018.

  • A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural network,” 2017.

  • R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural network: An overview and application in radiology,” Insights into Imaging, vol. 9, pp. 611-629, 2018.

  • R. Nirthika, S. Manivannan, A. Ramanan, and R. Whang, “Pooling in convolutional neural network for medical image analysis: A survey and an empirical study,” Neural Comput Appl., vol. 34, no. 7, pp. 5321-5347, 2022.

  • L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, ... and L. Farhan, “Review of deep learning: Concepts, CNN, challenges, applications, future directions,” J Big Data, vol. 8, no. 1, 2021.

  • E. Irmark, “Implementation of convolutional network approach for COVID-19 disease detection,” Physiological Genomics, vol. 52, no. 12, pp. 590-601, 2020.

  • S. Sorooshain, N. F. Aziz, A. Ahmad, S. N. Jubidin, and N. M. Mustapha, “Review on performance measurement systems,” Mediterranean Journal of Social Sciences, vol. 7, no. 1, 2016.

  • D. M. W. Powers, “Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation,” International Journal of Machine Learning Technology, vol. 2, no. 1, pp. 37-63, 2011.

  • C. Goutte, and E. Gaussier, “A probabilistic interpretation of precision, recall and F-score, with implication for evaluation,” 2014.

  • M. Sokolova, N. Japkowicz, and S. Szpakowicz, “Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation,” 2014.

  • S. Rajesh, P. Amudhavalli, and Dhanapriya A. P., “Prediction of air pollution using supervised machine learning,” Journal of Applied Information Science, vol. 10, no. 1, pp. 10-16, 2022.

  • Chinmaya H. S., J. M. Balaji, G. N. Sharma, and N. Divakar, “Dragonfly-net: Dragonfly classification using convolution neural network,” Journal of Applied Information Science, vol. 10, no. 1, pp. 60-66, 2022.


Abstract Views: 273

PDF Views: 0




  • Mango Leaf Diseases Detection using Deep Learning

Abstract Views: 273  |  PDF Views: 0

Authors

Amisha Sharma
M.Tech. Student, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, India
Rajneet Kaur Bijral
PhD Research Scholar, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, India
Jatinder Manhas
Sr. Assistant Professor, Department of Computer Science and IT, Bhaderwah Campus, University of Jammu, Jammu and Kashmir, India
Vinod Sharma
Professor, Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, India

Abstract


Diseases and pests cause great economic loss to the mango industry every year. The detection of various mango diseases is challenging for the farmers as the symptoms produced by different diseases may be very similar, and may be present simultaneously. This research paper is an attempt to provide the timely and accurate detection and identification of mango leaf diseases. Convolutional Neural Networks are end-to-end learning algorithms which perform automatic feature extraction and learn complex features directly from raw images, making them suitable for a wide variety of tasks like image classification, object detection, segmentation etc. In the proposed study, we develop a Convolutional Neural Networks based model for detection and classification of mango leaf diseases at the initial stages. Data augmentation is performed on a collected dataset. We applied data augmentation techniques like rotation, translation, reflection and scaling. Convolutional Neural Networks model has been trained on the augmented data for detection and classification of mango leaf diseases. The proposed CNN based model attains 90.36% of accuracy. The results validate that the proposed method is effective in detecting various types of mango leaf diseases and can be used as a practical tool by farmers and agriculture scientists.

Keywords


Convolution Neural Network (CNN), Crop, Deep learning, Image classification, Mango

References