Optimizing Performance in Mango Plant Leaf Disease Classification through Advanced Machine Learning Techniques
Received: 25 June 2024 | Revised: 21 August 2024 and 28 September 2024 | Accepted: 4 October 2024 | Online: 29 October 2024
Corresponding author: Sarika Khandelwal
Abstract
Leaf diseases pose a significant threat to the productivity and quality of mango crops, necessitating effective detection and management strategies. This study presents an automated system for the detection of mango leaf diseases using machine learning techniques. Using image processing methods to extract relevant features from leaf images, various machine learning models were trained to accurately classify common mango leaf diseases. This approach involved using a comprehensive dataset of diseased and healthy mango leaves, preprocessing images, and extracting features such as color, texture, and shape. Features were extracted using MobileNetV2 and EfficientNetV2. Feature fusion was performed using a dense layer. Principal component analysis was used to reduce dimensionality. These reduced features were then fed to a support vector classifier to classify the mango leaf images. Eight different classes were considered, including the seven most common diseases in mango leaves and one class for healthy ones. The proposed model achieved a remarkable accuracy of 99.83 %. These results demonstrate that machine learning models can achieve high accuracy in the early detection of mango leaf diseases. Implementing this system in agricultural practices can significantly help farmers in timely disease management, reducing crop losses, and improving mango production.
Keywords:
SVM, CNN, MobileNetV2, precision, recall, f1-score, MSEDownloads
References
S. Arivazhagan and S. V. Ligi, "Mango Leaf Diseases Identification Using Convolutional Neural Network," International Journal of Pure and Applied Mathematics, vol. 120, no. 8, pp. 11067–11079, 2018.
S. Gulavnai and R. Patil, "Deep learning for image based mango leaf disease detection," International Journal of Recent Technology and Engineering, vol. 8, no. 3S3, pp. 54–56, 2019.
S. Ali, "MangoLeafBD Dataset." Mendeley, Aug. 30, 2022.
L. F. Arauz, "Mango Anthracnose: Economic Impact and Current Options For Integrated Management," Plant Disease, vol. 84, no. 6, pp. 600–611, Jun. 2000.
A. N. Tusher, Md. T. Islam, Mst. S. R. Sammy, S. A. Hasna, and N. R. Chakraborty, "Automatic Recognition of Plant Leaf Diseases Using Deep Learning (Multilayer CNN) and Image Processing," in Third International Conference on Image Processing and Capsule Networks, 2022, pp. 130–142.
J. Wu, "Introduction to Convolutional Neural Networks," May 2017.
R. C. Ploetz, "The major diseases of mango: strategies and potential for sustainable management," Acta Horticulturae, no. 645, pp. 137–150, Feb. 2004.
J. Francis, Anto Sahaya Dhas D, and Anoop B K, "Identification of leaf diseases in pepper plants using soft computing techniques," in 2016 Conference on Emerging Devices and Smart Systems (ICEDSS), Namakkal, India, Mar. 2016, pp. 168–173.
Md. R. Mia, S. Roy, S. K. Das, and Md. A. Rahman, "Mango leaf disease recognition using neural network and support vector machine," Iran Journal of Computer Science, vol. 3, no. 3, pp. 185–193, Sep. 2020.
M. Bhange and H. A. Hingoliwala, "Smart Farming: Pomegranate Disease Detection Using Image Processing," Procedia Computer Science, vol. 58, pp. 280–288, Jan. 2015.
U. Obu, Y. Ambekar, H. Dhote, S. Wadbudhe, S. Khandelwal, and S. Dongre, "Crop Disease Detection using Yolo V5 on Raspberry Pi," in 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), Salem, India, Jun. 2023, pp. 528–533.
P. M. Mainkar, S. Ghorpade, and M. Adawadkar, "Plant leaf disease detection and classification using image processing techniques," International Journal of Innovative and Emerging Research in Engineering, vol. 2, no. 4, pp. 139–144, 2015.
S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, "A review of advanced techniques for detecting plant diseases," Computers and Electronics in Agriculture, vol. 72, no. 1, pp. 1–13, Jun. 2010.
F. Islam, M. N. Hoq, and C. M. Rahman, "Application of Transfer Learning to Detect Potato Disease from Leaf Image," in 2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), Dhaka, Bangladesh, Nov. 2019, pp. 127–130.
S. Sundari, "Leaf Disease Classification Using Artificial Neural Networks and Decision Tree Classifier," Journal of Image Processing and Pattern Recognition Programming, vol. 5, no. 1, Apr. 2018.
A. Rajbongshi, T. Khan, Md. M. Rahman, A. Pramanik, S. M. T. Siddiquee, and N. R. Chakraborty, "Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach," Indonesian Journal of Electrical Engineering and Computer Science, vol. 23, no. 3, pp. 1681–1688, Sep. 2021.
M. Merchant, V. Paradkar, M. Khanna, and S. Gokhale, "Mango Leaf Deficiency Detection Using Digital Image Processing and Machine Learning," in 2018 3rd International Conference for Convergence in Technology (I2CT), Pune, India, Apr. 2018, pp. 1–3.
S. Aftab, C. Lal, S. K. Beejal, and A. Fatima, "Raspberry Pi (Python AI) for Plant Disease Detection," International Journal of Current Research and Review, vol. 14, no. 03, pp. 36–42, 2022.
E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, "A comparative study of fine-tuning deep learning models for plant disease identification," Computers and Electronics in Agriculture, vol. 161, pp. 272–279, Jun. 2019.
S. Arya and R. Singh, "A Comparative Study of CNN and AlexNet for Detection of Disease in Potato and Mango leaf," in 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, Sep. 2019, pp. 1–6.
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," Applied Sciences, vol. 11, no. 24, Jan. 2021, Art. no. 11901.
U. P. Singh, S. S. Chouhan, S. Jain, and S. Jain, "Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease," IEEE Access, vol. 7, pp. 43721–43729, 2019.
K. Kusrini et al., "Data augmentation for automated pest classification in Mango farms," Computers and Electronics in Agriculture, vol. 179, Dec. 2020, Art. no. 105842.
U. Sanath Rao et al., "Deep Learning Precision Farming: Grapes and Mango Leaf Disease Detection by Transfer Learning," Global Transitions Proceedings, vol. 2, no. 2, pp. 535–544, Nov. 2021.
M. Tan and Q. Le, "EfficientNetV2: Smaller Models and Faster Training," in Proceedings of the 38th International Conference on Machine Learning, Jul. 2021, pp. 10096–10106.
S. Anwar, S. R. Soomro, S. K. Baloch, A. A. Patoli, and A. R. Kolachi, "Performance Analysis of Deep Transfer Learning Models for the Automated Detection of Cotton Plant Diseases," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11561–11567, Oct. 2023.
Downloads
How to Cite
License
Copyright (c) 2024 Sarika Khandelwal, Archana Raut, Harsha Vyawahare, Dipti Theng, Sheetal Dhande
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
