Performance Analysis of Deep Transfer Learning Models for the Automated Detection of Cotton Plant Diseases
Received: 10 July 2023 | Revised: 25 July 2023 | Accepted: 4 August 2023 | Online: 21 August 2023
Corresponding author: Shoaib Rehman Soomro
Abstract
Cotton is one of the most important agricultural products and is closely linked to the economic development of Pakistan. However, the cotton plant is susceptible to bacterial and viral diseases that can quickly spread and damage plants and ultimately affect the cotton yield. The automated and early detection of affected plants can significantly reduce the potential spread of the disease. This paper presents the implementation and performance analysis of bacterial blight and curl virus disease detection in cotton crops through deep learning techniques. The automated disease detection is performed through transfer learning of six pre-trained deep learning models, namely DenseNet121, DenseNet169, MobileNetV2, ResNet50V2, VGG16, and VGG19. A total of 1362 images of local agricultural fields and 1292 images from online resources were used to train and validate the models. Image augmentation techniques were performed to increase the dataset diversity and size. Transfer learning was implemented for different image resolutions ranging from 32×32 to 256×256 pixels. Performance metrics such as accuracy, precision, recall, F1 Score, and prediction time were evaluated for each implemented model. The results indicate higher accuracy, up to 96%, for DenseNet169 and ResNet50V2 models when trained on the 256×256 pixels image dataset. The lowest accuracy, 52%, was obtained by the MobileNetV2 model when trained on low-resolution, 32×32, images. The confusion matrix analysis indicates the true-positive prediction rates higher than 91% for fresh leaves, 87% for bacterial blight, and 76% for curl virus detection for all implemented models when trained and tested on an image dataset of 128×128 pixels or higher resolution.
Keywords:
transfer learning, CNN, pretrained networks, disease detection, cotton plantsDownloads
References
X. E. Pantazi, D. Moshou, and A. A. Tamouridou, "Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers," Computers and Electronics in Agriculture, vol. 156, pp. 96–104, Jan. 2019.
M. Martineau, D. Conte, R. Raveaux, I. Arnault, D. Munier, and G. Venturini, "A survey on image-based insect classification," Pattern Recognition, vol. 65, pp. 273–284, May 2017.
Y. Toda and F. Okura, "How Convolutional Neural Networks Diagnose Plant Disease," Plant Phenomics, vol. 2019, Mar. 2019.
Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, "Identification of rice diseases using deep convolutional neural networks," Neurocomputing, vol. 267, pp. 378–384, Dec. 2017.
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.
S. Alqethami, B. Almtanni, W. Alzhrani, and M. Alghamdi, "Disease Detection in Apple Leaves Using Image Processing Techniques," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8335–8341, Apr. 2022.
S. Nuanmeesri, "A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7678–7683, Oct. 2021.
A. Gutierrez, A. Ansuategi, L. Susperregi, C. Tubío, I. Rankić, and L. Lenža, "A Benchmarking of Learning Strategies for Pest Detection and Identification on Tomato Plants for Autonomous Scouting Robots Using Internal Databases," Journal of Sensors, vol. 2019, May 2019, Art. no. e5219471.
S. Kumar et al., "A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases," Mathematical Problems in Engineering, vol. 2021, p. e1790171, Jun. 2021.
M. S. Memon, P. Kumar, and R. Iqbal, "Meta Deep Learn Leaf Disease Identification Model for Cotton Crop," Computers, vol. 11, no. 7, Jul. 2022, Art. no. 102.
A. K. Rangarajan, R. Purushothaman, and A. Ramesh, "Tomato crop disease classification using pre-trained deep learning algorithm," Procedia Computer Science, vol. 133, pp. 1040–1047, Jan. 2018.
A. M., M. Zekiwos, and A. Bruck, "Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis," Journal of Electrical and Computer Engineering, vol. 2021, Jun. 2021, Art. no. e99814375/2021/9981437.
M. R. Ahmed, "Leveraging Convolutional Neural Network and Transfer Learning for Cotton Plant and Leaf Disease Recognition," International Journal of Image, Graphics and Signal Processing, vol. 13, no. 4, pp. 47–62, Aug. 2021.
V. Rajasekar, K. Venu, S. R. Jena, R. J. Varthini, and S. Ishwarya, "Detection of Cotton Plant Diseases Using Deep Transfer Learning," Journal of Mobile Multimedia, pp. 307–324, 2022.
J. Chopda, H. Raveshiya, S. Nakum, and V. Nakrani, "Cotton Crop Disease Detection using Decision Tree Classifier," in 2018 International Conference on Smart City and Emerging Technology (ICSCET), Mumbai, India, Jan. 2018.
K. Prashar, R. Talwar, and C. Kant, "Robust Automatic Cotton Crop Disease Recognition (ACDR) Method using the Hybrid Feature Descriptor with SVM," in INDIACom-2017, New Delhi, India, Mar. 2017.
N. R. Bhimte and V. R. Thool, "Diseases Detection of Cotton Leaf Spot Using Image Processing and SVM Classifier," in 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, Jun. 2018, pp. 340–344.
J. G. Arnal Barbedo, "Plant disease identification from individual lesions and spots using deep learning," Biosystems Engineering, vol. 180, pp. 96–107, Apr. 2019.
S. P. Mohanty, D. P. Hughes, and M. Salathé, "Using Deep Learning for Image-Based Plant Disease Detection," Frontiers in Plant Science, vol. 7, 2016.
Y. Tao, F. Chang, Y. Huang, L. Ma, L. Xie, and H. Su, "Cotton Disease Detection Based on ConvNeXt and Attention Mechanisms," IEEE Journal of Radio Frequency Identification, vol. 6, pp. 805–809, 2022.
S. Kumbhar, A. Nilawar, S. Patil, B. Mahalakshmi, and M. Nipane, "Farmer Buddy-Web Based Cotton Leaf Disease Detection Using CNN," International Journal of Applied Engineering Research, vol. 14, no. 11, pp. 2662–2666, 2019.
S. A. Sabeeh and S. H. Ameen, "Detection and Classification of Leaf Disease Using Deep Learning for a Greenhouses’ Robot," Iraqi Journal of Computer, Communication, Control and System Engineering, vol. 21, no. 4, pp. 15–28, Dec. 2021.
E. A. H. Mora, V. González-Huitrón, A. E. Rodríguez-Mata, and H. R. Rangel, "Convolutional Neural Networks-based plant disease detection implemented on low-power consumption device," in 2021 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico, Aug. 2021, vol. 5, pp. 1–6.
M. H. Jumat, M. S. Nazmudeen, and A. T. Wan, "Smart farm prototype for plant disease detection, diagnosis & treatment using IoT device in a greenhouse," in 7th Brunei International Conference on Engineering and Technology 2018 (BICET 2018), Bandar Seri Begawan, Brunei, Aug. 2018, pp. 1–4.
N. Schor, A. Bechar, T. Ignat, A. Dombrovsky, Y. Elad, and S. Berman, "Robotic Disease Detection in Greenhouses: Combined Detection of Powdery Mildew and Tomato Spotted Wilt Virus," IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 354–360, Jan. 2016.
S. H. Abed, A. S. Al-Waisy, H. J. Mohammed, and S. Al-Fahdawi, "A modern deep learning framework in robot vision for automated bean leaves diseases detection," International Journal of Intelligent Robotics and Applications, vol. 5, no. 2, pp. 235–251, Jun. 2021.
M. S. P. Rahul and M. Rajesh, "Image processing based Automatic Plant Disease Detection and Stem Cutting Robot," in 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, Dec. 2020, pp. 889–894.
A. S. Vellaichamy, A. Swaminathan, C. Varun, and K. S, "Multiple Plant Leaf Disease Classification Using Densenet-121 Architecture," International Journal of Electrical Engineering and Technology, vol. 12, no. 5, May 2021.
V. Sathiesh Kumar and S. Anubha Pearline, "Real-Time Plant Species Recognition Using Non-averaged DenseNet-169 Deep Learning Paradigm," in Computer Vision and Image Processing, 2023, pp. 58–72.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," presented at the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2018, pp. 4510–4520.
M. Rahimzadeh and A. Attar, "A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2," Informatics in Medicine Unlocked, vol. 19, 2020, Art. no. 100360.
H. Qassim, A. Verma, and D. Feinzimer, "Compressed residual-VGG16 CNN model for big data places image recognition," in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, Jan. 2018, pp. 169–175.
L. Wen, X. Li, X. Li, and L. Gao, "A New Transfer Learning Based on VGG-19 Network for Fault Diagnosis," in 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Porto, Portugal, Feb. 2019, pp. 205–209.
"Cotton Plant Disease Prediction CNN (96.8% acc)." https://kaggle.com/code/aravindaraman/cotton-plant-disease-prediction-cnn-96-8-acc.
Downloads
How to Cite
License
Copyright (c) 2023 Sohail Anwar, Shoaib Rehman Soomro, Shadi Khan Baloch, Aamir Ali Patoli, Abdul Rahim Kolachi
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.
