Severity Stage Identification and Pest Detection of Tomato Disease Using Deep Learning
DOI:
https://doi.org/10.47839/ijc.22.2.3088Keywords:
deep learning, prevention strategies, severity stage identification, disease detection, pest detectionAbstract
In Bangladesh, most people depend on agriculture for their livelihood. The country's economy and agricultural production are hampered if plants are affected by diseases. Crop pests also disrupt agricultural production. So, this paper proposes leaf disease, disease severity stage, and pest detection strategies with suggestions for prevention strategies using five notable Convolutional Neural Network models (CNN) such as VGG16, Resnet50, AlexNet, EfficientNetB2, and EfficientNetB3. This paper uses a dataset of tomato leaves consisting of 41,763 images which cover 10 classes of tomato disease, and a dataset of pests consisting of 4,271 images which cover 8 classes of pests. Firstly, these models are used for the classification of diseases and pests. Then disease and pest prevention techniques are shown. For disease and pest detection, EfficientNetB3 gives the best accuracy for training (99.85%), (99.80%), and validation (97.85%), (97.45%) respectively. For severity stage identification, AlexNet gives the best accuracy for training (69.02%) and validation (72.49%).
References
P. Sharma, P. Hans, and S. C. Gupta, “Classification of plant leaf diseases using machine learning and image preprocessing techniques,” Proceedings of the 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), January 29-31, 2020, pp. 480-484. https://doi.org/10.1109/Confluence47617.2020.9057889.
P. P. Patel, and D. B. Vaghela, “Crop diseases and pests detection using convolutional neural network,” Proceedings of the 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), February 20, 2019, pp. 1-4. https://doi.org/10.1109/ICECCT.2019.8869510.
K. Singh, S. Kumar, and P. Kaur, “Automatic detection of rust disease of Lentil by machine learning system using microscopic images,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 1, p. 660, 2019, https://doi.org/10.11591/ijece.v9i1.pp660-666.
E. Hirani, V. Magotra, J. Jain, and P. Bide, “Plant disease detection using deep learning,” Proceedings of the 2021 6th International Conference for Convergence in Technology (I2CT), April 2, 2021, pp. 1-4. https://doi.org/10.1109/I2CT51068.2021.9417910.
O. Kulkarni, “Crop disease detection using deep learning,” Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), August 16, 2018, pp. 1-4. https://doi.org/10.1109/ICCUBEA.2018.8697390.
P. Tm, A. Pranathi, K. Sai Ashritha, N. B. Chittaragi, and S. G. Koolagudi, “Tomato leaf disease detection using convolutional neural networks,” Proceedings of the 2018 Eleventh International Conference on Contemporary Computing (IC3), August 2, 2018, pp. 1-5, https://doi.org/10.1109/IC3.2018.8530532.
H. Ajra, M. K. Nahar, L. Sarkar, and Md S. Islam, “Disease detection of plant leaf using image processing and CNN with preventive measures,” Proceedings of the 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE), December 21, 2020, pp. 1-6. https://doi.org/10.1109/ETCCE51779.2020.9350890.
C. R. Rahman et al., “Identification and recognition of rice diseases and pests using convolutional neural networks,” Biosyst Eng, vol. 194, pp. 112–120, 2020, https://doi.org/10.1016/j.biosystemseng.2020.03.020.
D. Tiwari, M. Ashish, N. Gangwar, A. Sharma, S. Patel, and S. Bhardwaj, “Potato leaf diseases detection using deep learning,” Proceedings of the International Conference on Intelligent Computing and Control Systems, ICICCS 2020, May 2020, pp. 461–466. https://doi.org/10.1109/ICICCS48265.2020.9121067.
G. Zhou, W. Zhang, A. Chen, M. He, and X. Ma, “Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion,” IEEE Access, vol. 7, pp. 143190–143206, September 24, 2019, https://doi.org/10.1109/ACCESS.2019.2943454.
G. R. Beecher, “Nutrient content of tomatoes and tomato products,” Proceedings of the Society for Experimental Biology and Medicine, vol. 218, no. 2, pp. 98–100, 1998. https://doi.org/10.3181/00379727-218-44282a.
M. Sardogan, A. Tuncer, and Y. Ozen, “Plant leaf disease detection and classification based on CNN with LVQ algorithm,” Proceedings of the 2018 3rd International Conference on Computer Science and Engineering (UBMK), September 20, 2018, pp. 382-385. https://doi.org/10.1109/UBMK.2018.8566635.
Md H. Ahmed, T. Islam, and R. Rahman Ema, “A new hybrid intelligent GAACO algorithm for automatic image segmentation and plant leaf or fruit diseases identification using TSVM classifier,” Proceedings of the 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), February 7, 2019, pp. 1-6, https://doi.org/10.1109/ECACE.2019.8679219.
S. M. Hassan, A. K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, “Identification of plant-leaf diseases using CNN and transfer-learning approach,” Electronics (Switzerland), vol. 10, no. 12, p. 1388, 2021, https://doi.org/10.3390/electronics10121388.
“PlantifyDr Dataset | Kaggle,” [Online]. Available at: https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset.
J. Liu and X. Wang, “Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network,” Front Plant Sci, vol. 11, p. 898, 2020, https://doi.org/10.3389/fpls.2020.00898.
M.-L. Huang and T. C. Chuang, “A database of eight common tomato pest images,” Mendeley Data, vol. 1, 2020.
A. Gondal, M. Ijaz, K. Riaz, and A. Khan, “Effect of different doses of fungicide (Mancozeb) against alternaria leaf blight of tomato in tunnel,” J Plant Pathol Microbiol, vol. 3, no. 3, p. 2, 2012, https://doi.org/10.4172/2157-7471.1000125.
P. Kaur et al., “Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction,” Sensors, vol. 22, no. 2, p. 575, January 22, 2022, https://doi.org/10.3390/s22020575.
V. Agarwal, “Complete architectural details of all EfficientNet models towards data science,” [Online]. Available at: https://towardsdatascience.com/complete-architectural-details-of-all-efficientnet-models-5fd5b736142.
Z. P. Jiang, Y. Y. Liu, Z. E. Shao, and K. W. Huang, “An improved VGG16 model for pneumonia image classification,” Applied Sciences (Switzerland), vol. 11, no. 23, p. 11185, 2021, https://doi.org/10.3390/app112311185.
“ML: Getting started with AlexNet – GeeksforGeeks,” [Online]. Available at: https://www.geeksforgeeks.org/ml-getting-started-with-alexnet/.
“Everything you need to know about VGG16 medium,” [Online]. Available at: https://medium.com/@mygreatlearning/everything-you-need-to-know-about-vgg16-7315defb5918.
I. Z. Mukti, and D. Biswas, “Transfer learning based plant diseases detection using ResNet50,” Proceedings of the 2019 4th International Conference on Electrical Information and Communication Technology (EICT), December 20, 2019, pp. 1-6. https://doi.org/10.1109/EICT48899.2019.9068805.
A. Rastogi, “ResNet50. ResNet-50 is a convolutional neural,” [Online]. Available at: https://blog.devgenius.io/resnet50-6b42934db431.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal 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) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
