An Ensemble of Transfer Learning based InceptionV3 and VGG16 Models for Paddy Leaf Disease Classification
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Abstract
Paddy is a crucial food crop providing essential nutrients and energy and serving more than half the global population. Diagnosing and preventing plant diseases at an early stage is crucial for the health and productivity of crops. Automated disease diagnosis eliminates the need for experts and delivers accurate outcomes. This research will diagnose paddy leaf diseases with Deep Learning technology. The diseases such as bacterial blight, blast, tungro, brown spot, and healthy leaf classes are diagnosed and classified in this study. The dataset contains 160 images from each class with 800 images. Our proposed model is an ensemble of transfer-learned InceptionV3 and VGG16 architectures, which utilizes the strength of individual models to improve overall performance. The use of transfer-learned ensemble deep learning architectures achieved impressive accuracy rates of 97.03%, 94.97%, and 98.87% for training, validation and testing respectively. The results indicating that model is not overfit and generalizes well to unseen data. The model's performance is evaluated with confusion matrix with the parameters like precision, recall, F1-score, and support. We also tested the model's performance against other proposed deep learning techniques with and without transfer learning techniques. Moreover, this research advances reliable automated disease detection systems, fostering sustainable agriculture and enhancing global food security.
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References
A. R. Bin Rahman and J. Zhang, “Trends in rice research: 2030 and beyond,” Food and Energy Security, vol. 12, no. 2, e390, 2023.
https://www.ibef.org/industry/agriculture-india.
B. S. Anami, N. N. Malvade and S. Palaiah, “Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images,” Artificial intelligence in agriculture, vol. 4, pp. 12-20, 2020.
J. A. Wani, S. Sharma, M. Muzamil, S. Ahmed, S. Sharma and S. Singh, “Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: Methodologies, applications, and challenges,”Archives of Computational methods in Engineering, vol. 29, no. 1, pp. 641-677, 2022.
S. K. Upadhyay and A. Kumar, “Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques,” Traitement du Signal, vol. 38, no. 6, pp. 1755-1766, 2021.
K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and electronics in agriculture, vol. 145, pp. 311-318, 2018.
S. Jain, R. Sahni, T. Khargonkar, H. Gupta, O. P. Verma, T. K. Sharma and H. Kim, “Automatic rice disease detection and assistance framework using deep learning and a Chatbot,” Electronics, vol. 11, no. 14, pp. 2110, 2022.
M. Sharma, C. J. Kumar and A. Deka, “Early diagnosis of rice plant disease using machine learning techniques,” Archives of Phytopathology and Plant Protection, vol. 55, no. 3, pp. 259-283, 2022.
S. P. Singh, K. Pritamdas, K. J. Devi and S. D. Devi, “Custom Convolutional Neural Network for Detection and Classification of Rice Plant Diseases,” Procedia Computer Science, vol. 218, pp. 2026-2040, 2023.
S. H. Lee, C. S. Chan, P. Wilkin and P. Remagnino, “Deep-plant: Plant identification with convolutional neural networks,” 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, pp. 452-456, 2015.
A.R.T.Z.A.I.PiconRuiz,A.I.T.O.R. Alvarez Gila, U. Irusta and J. O. N. E. Echazarra Huguet, “Why deep learning performs better than classical machine learning?,” Dyna Ingenieria E Industria, 2020.
J. R. Ubbens and I. Stavness, “Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks,” Frontiers in plant science, vol. 8, no. 1190, pp.1-11, 2017.
G. Fenu and F. M. Malloci, “Classification of Pear Leaf Diseases Based on Ensemble Convolutional Neural Networks,” AgriEngineering, vol. 5, no. 1, pp. 141-152, 2023.
A. S. Paymode and V. B. Malode, “Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG,” Artificial Intelligence in Agriculture, vol. 6, pp. 23-33, 2022.
A. T. Al-Taani and I. T. Al-Dagamseh, “Automatic detection of pneumonia using concatenated convolutional neural network,” Jordanian Journal of Computers and Information Technology (JJCIT), vol. 9, no. 2, pp. 118-136, 2023.
V. Gautam, N. K. Trivedi, A. Singh, H. G. Mohamed, I. D. Noya, P. Kaur and N. Goyal, “A transfer learning-based artificial intelligence model for leaf disease assessment,” Sustainability, vol. 14, no. 20:13610, 2022.
M. H. Saleem, J. Potgieter and K. M. Arif, “Plant disease detection and classification by deep learning,” Plants, vol. 8, no.11:468, 2019.
M. H. Saleem, J. Potgieter and K. M. Arif, “Plant disease classification: A comparative evaluation of convolutional neural networks and deep learning optimizers,” Plants, vol. 9, no. 10:1319, 2020.
M. Aggarwal, V. Khullar, N. Goyal, A. Singh, A. Tolba, E. B. Thompson and S. Kumar, “Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification,” Agriculture, vol. 13, no. 5:936, 2023.
S. Sengupta, A. Dutta, S. A. Abdelmohsen, H. A. Alyousef and M. Rahimi-Gorji, “Development of a Rice Plant Disease Classification Model in Big Data Environment,” Bioengineering, vol. 9, no. 12:758, 2022.
A. Ahmad, A. E. Gamal and D. Saraswat, “Toward Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions,” in IEEE Access, vol. 11, pp. 9042-9057, 2023.
H. T. Vo, L. D. Quach and T. N. Hoang, “Ensemble of Deep Learning Models for Multi-plant Disease Classification in Smart Farming,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 5, 2023.
M. T. Ahad, Y. Li, B. Song and T. Bhuiyan, “Comparison of CNN-based deep learning architectures for rice diseases classification,” Artificial Intelligence in Agriculture, vol. 9, pp. 22-35, 2023.
A. Petchiammal, D. Murugan and K. S. Briskline, “PaddyNet: An Improved Deep Convolutional Neural Network for Automated Disease Identification on Visual Paddy Leaf Images,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 6, 2023.
H. Qi, Y. Liang, Q. Ding and J. Zou, “Automatic identification of peanut-leaf diseases based on stack ensemble,” Applied Sciences, vol. 11, no. 4:1950, 2021.
M. Turkoglu, B. Yanikoglu and H D. anbay, “PlantDiseaseNet: Convolutional neural network ensemble for plant disease and pest detection, Signal, Image and Video Processing, vol. 16. no. 2, pp. 301-309, 2022.
J. Chen, J. Chen, D. Zhang, Y. Sun and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Computers and Electronics in Agriculture, vol. 173, p. 105393, 2020.
S. Dou, L. Wang, D. Fan, L. Miao, J. Yan and H. He, “Classification of Citrus Huanglongbing Degree Based on CBAM-MobileNetV2 and Transfer Learning,” Sensors, vol. 23, no. 12, p. 5587, 2023.
F. Arshad, M. Mateen, S. Hayat, M. Wardah, Z. Al-Huda, Y. H. Gu and M. A. Al-antari, “PLDP-Net: End-to-end hybrid deep learning framework for potato leaf disease prediction,” Alexandria Engineering Journal, vol. 78, pp. 406-418, 2023.
G. Liu, J. Peng and A. A. A. El-Latif, “SK-MobileNet: A Lightweight Adaptive Network Based on Complex Deep Transfer Learning for Plant Disease Recognition,” Arabian Journal for Science and Engineering, vol. 48, no. 2, pp. 1661-1675, 2023.
A. Nayak, S. Chakraborty and D. K. Swain, “Application of smartphone-image processing and transfer learning for rice disease and nutrient deficiency detection,” Smart Agricultural Technology, vol. 4, p. 100195, 2023.
R. Mahum, H. Munir, Z. U. N. Mughal, M. Awais, F. Sher Khan, M. Saqlain and I. Tlili, “A novel framework for potato leaf disease detection using an efficient deep learning model,” Human and Ecological Risk Assessment: An International Journal, vol. 29, no. 2, pp. 303-326, 2023.
P. Pavithra and P. Aishwarya, “Plant leaf disease detection using hybrid grasshopper optimization with modified artificial bee colony algorithm,” Multimedia Tools and Applications, pp. 1-23, 2023.
Y. M. Abd Algani, O. J. M. Caro, L. M. R. Bravo, C. Kaur, M. S. Al Ansari and B. K. Bala, “Leaf disease identification and classification using optimized deep learning,” Measurement: Sensors, vol. 25, p.100643, 2023.
A. V. Panchal, S. C. Patel, K. Bagyalakshmi, P. Kumar, I. R. Khan and M. Soni, “Image-based plant diseases detection using deep learning,” Materials Today: Proceedings, vol. 80, pp. 3500-3506, 2023.
P. S. Thakur, T. Sheorey and A. Ojha, “VGG-ICNN: A Lightweight CNN model for crop disease identification,” Multimedia Tools and Applications, vol. 82, no. 1, pp. 497-520, 2023.
S. D. Deb, R. K. Jha and S. Kumar, “ConvPlantNet: A Convolutional Neural Network based Architecture for Leaf Disease Detection in Smart Agriculture,” 2023 National Conference on Communications (NCC), Guwahati, India, pp. 1-6, 2023.
V. Malathi, M. P. Gopinath, M. Kumar, S. Bhushan and S. Jayaprakash, “Enhancing the Paddy Disease Classification by Using CrossValidation Strategy for Artificial Neural Network over Baseline Classifiers,” Journal of Sensors, vol. 2023, no. 1576960, 2023.
P. K. Sethy, “Rice Leaf Disease Image Samples,” Mendeley Data, V1, 2020. http://dx.doi.org/10.17632/fwcj7stb8r.1.
P. Alirezazadeh, M. Schirrmann and F. Stolzenburg, “Improving Deep Learning-based Plant Disease Classification with Attention Mechanism,” Gesunde Pflanzen, vol. 75, no. 1, pp. 49-59, 2023.
A. Kamilaris and F. X. Prenafeta-Boldu ́, “Deep learning in agriculture: A survey,” Computers and electronics in agriculture, vol. 147, pp. 70-90, 2018.