An Optimized YOLO v5 Model for Tomato Leaf Disease Classification with Field Dataset
Received: 8 September 2023 | Revised: 20 September 2023 | Accepted: 29 September 2023 | Online: 21 October 2023
Corresponding author: Rajasree Rajamohanan
Abstract
Deep learning has gained widespread adoption in various fields, including object recognition, classification, and precision agriculture. This study aimed to investigate the use of deep convolutional neural networks for the real-time identification of diseases in tomato plant leaves. A customized field dataset was constructed, consisting of several images of tomato leaves captured using a mobile phone from agricultural fields in the Kerala and Tamil Nadu regions and classified into two categories: healthy and diseased. A YOLO v5 deep learning model was trained to classify images of tomato leaves into the respective categories. This study aimed to determine the most effective hyperparameters for the classification and detection of healthy and sick leaves sections, using both proprietary and publicly available datasets. The YOLO v5 model demonstrated a notable accuracy rate of 93% when evaluated in the test dataset. This method can help farmers quickly recognize diseased leaves and prompt the implementation of preventive measures to curtail the spread of tomato plant diseases.
Keywords:
convolutional neural networks, deep learning, object detection, plant disease detection, YOLO v5Downloads
References
Ramanjot et al., "Plant Disease Detection and Classification: A Systematic Literature Review," Sensors, vol. 23, no. 10, Jan. 2023, Art. no. 4769.
R. I. Hasan, S. M. Yusuf, and L. Alzubaidi, "Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion," Plants, vol. 9, no. 10, Oct. 2020, Art. no. 1302.
S. Arivazhagan, R. N. Shebiah, S. Ananthi, and S. V. Varthini, "Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features," Agricultural Engineering International: CIGR Journal, vol. 15, no. 1, pp. 211–217, Apr. 2013.
K. Lin, L. Gong, Y. Huang, C. Liu, and J. Pan, "Deep Learning-Based Segmentation and Quantification of Cucumber Powdery Mildew Using Convolutional Neural Network," Frontiers in Plant Science, vol. 10, 2019.
J. Blumenthal, D. B. Megherbi, and R. Lussier, "Supervised machine learning via Hidden Markov Models for accurate classification of plant stress levels & types based on imaged Chlorophyll fluorescence profiles & their rate of change in time," in 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Annecy, France, Jun. 2017, pp. 211–216.
V. K. Shrivastava and M. K. Pradhan, "Rice plant disease classification using color features: a machine learning paradigm," Journal of Plant Pathology, vol. 103, no. 1, pp. 17–26, Feb. 2021.
M. Chen et al., "Three-dimensional perception of orchard banana central stock enhanced by adaptive multi-vision technology," Computers and Electronics in Agriculture, vol. 174, Jul. 2020, Art. no. 105508.
Q. Li, W. Jia, M. Sun, S. Hou, and Y. Zheng, "A novel green apple segmentation algorithm based on ensemble U-Net under complex orchard environment," Computers and Electronics in Agriculture, vol. 180, Jan. 2021, Art. no. 105900.
M. Mishra, P. Choudhury, and B. Pati, "Modified ride-NN optimizer for the IoT based plant disease detection," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 691–703, Jan. 2021.
S. Khan, M. Tufail, M. T. Khan, Z. A. Khan, and S. Anwar, "Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer," Precision Agriculture, vol. 22, no. 6, pp. 1711–1727, Dec. 2021.
C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, "Scaled-YOLOv4: Scaling Cross Stage Partial Network," in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, Jun. 2021, pp. 13024–13033.
A. I. Jajja et al., "Compact Convolutional Transformer (CCT)-Based Approach for Whitefly Attack Detection in Cotton Crops," Agriculture, vol. 12, no. 10, Oct. 2022, Art. no. 1529.
G. Niedbała, J. Kurek, B. Świderski, T. Wojciechowski, I. Antoniuk, and K. Bobran, "Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods," Agriculture, vol. 12, no. 12, Dec. 2022, Art. no. 2089.
K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis," Computers and Electronics in Agriculture, vol. 145, pp. 311–318, Feb. 2018.
L. Deng and Q. Deng, "The basic roles of indoor plants in human health and comfort," Environmental Science and Pollution Research, vol. 25, no. 36, pp. 36087–36101, Dec. 2018.
S. K. Balasundram, K. Golhani, R. R. Shamshiri, and G. Vadamalai, "Precision Agriculture Technologies for Management of Plant Diseases," in Plant Disease Management Strategies for Sustainable Agriculture through Traditional and Modern Approaches, I. Ul Haq and S. Ijaz, Eds. Cham, Switzerland: Springer International Publishing, 2020, pp. 259–278.
P. Trivedi, J. E. Leach, S. G. Tringe, T. Sa, and B. K. Singh, "Plant–microbiome interactions: from community assembly to plant health," Nature Reviews Microbiology, vol. 18, no. 11, pp. 607–621, Nov. 2020.
X. Wang, W. Yang, A. Wheaton, N. Cooley, and B. Moran, "Efficient registration of optical and IR images for automatic plant water stress assessment," Computers and Electronics in Agriculture, vol. 74, no. 2, pp. 230–237, Nov. 2010.
S. Khan, M. Narvekar, M. Hasan, A. Charolia, and A. Khan, "Image Processing based application of Thermal Imaging for Monitoring Stress Detection in Tomato Plants," in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, Aug. 2019, pp. 1111–1116.
A. Abisha and N. Bharathi, "Review on Plant health and Stress with various AI techniques and Big data," in 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), Puducherry, India, Jul. 2021, pp. 1–6.
N. S. Chandel, S. K. Chakraborty, Y. A. Rajwade, K. Dubey, M. K. Tiwari, and D. Jat, "Identifying crop water stress using deep learning models," Neural Computing and Applications, vol. 33, no. 10, pp. 5353–5367, May 2021.
R. Rajasree, C. B. C. Latha, and S. Paul, "Application of Transfer Learning with a Fine-tuned ResNet-152 for Evaluation of Disease Severity in Tomato Plants," in Mobile Computing and Sustainable Informatics, Singapore, 2022, pp. 695–710.
J. Abdulridha, R. Ehsani, A. Abd-Elrahman, and Y. Ampatzidis, "A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses," Computers and Electronics in Agriculture, vol. 156, pp. 549–557, Jan. 2019.
D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, and N. Batra, "PlantDoc: A Dataset for Visual Plant Disease Detection," in Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Hyderabad, India, Jan. 2020, pp. 249–253.
M. P. Mathew and T. Y. Mahesh, "Leaf-based disease detection in bell pepper plant using YOLO v5," Signal, Image and Video Processing, vol. 16, no. 3, pp. 841–847, Apr. 2022.
J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement." arXiv, Apr. 08, 2018.
M. A. Jasim and J. M. AL-Tuwaijari, "Plant Leaf Diseases Detection and Classification Using Image Processing and Deep Learning Techniques," in 2020 International Conference on Computer Science and Software Engineering (CSASE), Duhok, Iraq, Apr. 2020, pp. 259–265.
S. Swain, S. K. Nayak, and S. S. Barik, "A Review on Plant Leaf Diseases Detection and Classification Based on Machine Learning Models," Mukt Shabd Journal, vol. IX, no. VI, pp. 5195–5205, Jun. 2020.
M. Ranjan, M. R. Weginwar, N. Joshi, and A. B. Ingole, "Detection and classification of leaf disease using artificial neural network," International journal of technical research and applications, vol. 3, no. 3, pp. 331–333, 2015.
P. Bolliger and B. Ostermaier, "Koubachi: A mobile phone widget to enable affective communication with indoor plants," in Mobile Interaction with the Real World (MIRW 2007), Singapore, 2007, pp. 63–66.
W. Gélard, A. Herbulot, M. Devy, and P. Casadebaig, "3D Leaf Tracking for Plant Growth Monitoring," in 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, Jul. 2018, pp. 3663–3667.
P. Mishra, T. Feller, M. Schmuck, A. Nicol, and A. Nordon, "Early Detection Of Drought Stress in Arabidopsis Thaliana Utilsing a Portable Hyperspectral Imaging Setup," in 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, Netherlands, Sep. 2019, pp. 1–5.
loannis Navrozidis et al., "Olive Trees Stress Detection Using Sentinel-2 Images," in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Jul. 2019, pp. 7220–7223.
W. Ciężkowski, M. Kleniewska, and J. Chormański, "Using Landsat 8 Images for The Wetland Water Stress Calculation: Upper Biebrza Case Study," in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, Jul. 2019, pp. 6867–6870.
S. Bhugra, S. Chaudhury, and B. Lall, "Use of leaf colour for drought stress analysis in rice," in 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Patna, India, Sep. 2015, pp. 1–4.
K. Ahmed, T. R. Shahidi, S. Md. Irfanul Alam, and S. Momen, "Rice Leaf Disease Detection Using Machine Learning Techniques," in 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, Sep. 2019, pp. 1–5.
K. Zhang, Q. Wu, A. Liu, and X. Meng, "Can Deep Learning Identify Tomato Leaf Disease?," Advances in Multimedia, vol. 2018, Sep. 2018, Art. no. e6710865.
M. G. Selvaraj et al., "AI-powered banana diseases and pest detection," Plant Methods, vol. 15, no. 1, Aug. 2019, Art. no. 92.
S. R. Gopi and M. Karthikeyan, "Effectiveness of Crop Recommendation and Yield Prediction using Hybrid Moth Flame Optimization with Machine Learning," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11360–11365, Aug. 2023.
N. C. Kundur and P. B. Mallikarjuna, "Deep Convolutional Neural Network Architecture for Plant Seedling Classification," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9464–9470, Dec. 2022.
L. Loyani and D. Machuve, "A Deep Learning-based Mobile Application for Segmenting Tuta Absoluta’s Damage on Tomato Plants," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7730–7737, Oct. 2021.
R. R, C. B. C. Latha, S. Paul, A. M, and A. N, "An optimized Faster R-CNN model for Cassava Brown Streak Disease Classification," in 2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), Kalady, Ernakulam, India, Feb. 2023, pp. 94–100.
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Copyright (c) 2023 Rajamohanan Rajasree, Beulah Christalin Latha
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