1
Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Chidambaram, Tamilnadu 608002, India
(vasavipallepati@gmail.com)
2
Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Chidambaram, Tamilnadu 608002, India
(charuka12@yahoo.com)
3
Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad 501301, Telangana, India
(venkatnarayanaraot@sreenidhi.edu.in)
Abstract :
Diseases in crops lead to decreased production, which can be addressed through consistent surveillance. Manual surveillance of crop diseases is both arduous and prone to mistakes. The timely identification of crop leaf diseases using Computer Vision and Artificial Intelligence can aid in minimizing the negative impact of diseases and address the limitations of continuous human surveillance. To classify chili crop diseases, this research paper introduces a new deep feature extraction model based on Transfer Learning using ResNet50, MobileNet, EfficientNetB0, and multiple classifiers. On Plant Village dataset related to the diseases of the chili crop and Private data set, the proposed method was trained and tested. And also analyzed the results by comparing the performance of the pre-trained deep learning models on original data and data filtered through the Image filtering mechanisms and proposed method on the plant village dataset and private dataset, the highest performance accuracy is 99.6% with ResNet50 and the faster CPU time for feature extraction is 29.3 seconds using MobileNet. Comparing the suggested model to the most advanced deep learning models reveals greater accuracy with fewer computational resources.
Keywords :
Plant disease detection; MobileNet; ResNet; EfficientNet; Transfer Learning; Deep feature extraction; chili diseases
References :
[1] https://tradestat.commerce.gov.in – Website accessed on 3rd August,2023.
[2] Vasavi P, Punitha A, NarayanaRao TV. Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review. Int J ElectrComputEng (IJECE) 2022 ;12(2):2079. Available from: https://ijece.iaescore.com/index.php/IJECE/article/view/25809"
[3] Hughes DP, Salathe M. An open access repository of images on plant health to enable the development of mobile disease diagnostics, 2015. Available from: http://arxiv.org/abs/1511.08060"
[4] Barbedo, Jayme. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering. 180. 96-107. 10.1016/j.biosystemseng.2019.02.002.
[5] S. V. Militante, B. D. Gerardo and N. V. Dionisio, ""Plant Leaf Detection and Disease Recognition using Deep Learning,"" 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, Taiwan, 2019, pp. 579-582, doi: 10.1109/ECICE47484.2019.8942686.
[6] Mohameth, F. ,Bingcai, C. and Sada, K. (2020) Plant Disease Detection with Deep Learning and Feature Extraction Using Plant Village. Journal of Computer and Communications, 8, 10-22. doi: 10.4236/jcc.2020.86002
[7] S. Ashok, G. Kishore, V. Rajesh, S. Suchitra, S. G. G. Sophia and B. Pavithra, ""Tomato Leaf Disease Detection Using Deep Learning Techniques,"" 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2020, pp. 979-983, doi: 10.1109/ICCES48766.2020.9137986.
[8] Chowdhury, Muhammad &Rahman, Tawsifur&Khandakar, Amith&Ayari, Mohamed & Khan, Aftab& Khan, Muhammad Salman & Al-Emadi, Nasser &Reaz, Mamun Bin Ibne& Islam, Mohammad & Ali, Sawal. (2021). Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques. AgriEngineering. 3. 294-312. 10.3390/agriengineering3020020.
[9] Bedi, Punam&Gole, Pushkar. (2021). Plant disease detection using a hybrid model based on convolutional autoencoder and convolutional neural network. Artificial Intelligence in Agriculture. 5. 90-101. 10.1016/j.aiia.2021.05.002.
[10] Paymode, Ananda&Malode, Vandana. (2022). Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG. Artificial Intelligence in Agriculture. 6. 10.1016/j.aiia.2021.12.002.
[11] S. M. Hassan and A. K. Maji, "Plant Disease Identification Using a Novel Convolutional Neural Network," in IEEE Access, vol. 10, pp. 5390-5401, 2022, doi: 10.1109/ACCESS.2022.3141371.
[12] Saeed, A., Abdel-Aziz, A. A., Mossad, A., Abdelhamid, M. A., Alkhaled, A. Y., &Mayhoub, M. (2023). Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks. Agriculture, 13(1), 139. https://doi.org/10.3390/agriculture13010139
[13] ArumugaArun, R., and S. Umamaheswari. ‘Effective Multi-Crop Disease Detection Using Pruned Complete Concatenated Deep Learning Model’. Expert Systems with Applications, vol. 213, 2023, p. 118905, https://doi.org10.1016/j.eswa.2022.118905
[14] P.L, Chithra and P, Bhavani, A Study on Various Image Processing Techniques (May 7, 2019). International Journal of Emerging Technology and Innovative Engineering Volume 5, Issue 5, May 2019, Available at SSRN: https://ssrn.com/abstract=3388008
| Style | # |
|---|---|
| MLA | Pallepati Vasavi, A. Punitha, T. VenkatNarayana Rao. "Chili Leaf Disease Detection Using Deep Feature Extraction." Journal of Intelligent Systems and Internet of Things, Vol. 9, No. 2, 2023 ,PP. 222-230 (Doi : https://doi.org/10.54216/JISIoT.090216) |
| APA | Pallepati Vasavi, A. Punitha, T. VenkatNarayana Rao. (2023). Chili Leaf Disease Detection Using Deep Feature Extraction. Journal of Journal of Intelligent Systems and Internet of Things, 9 ( 2 ), 222-230 (Doi : https://doi.org/10.54216/JISIoT.090216) |
| Chicago | Pallepati Vasavi, A. Punitha, T. VenkatNarayana Rao. "Chili Leaf Disease Detection Using Deep Feature Extraction." Journal of Journal of Intelligent Systems and Internet of Things, 9 no. 2 (2023): 222-230 (Doi : https://doi.org/10.54216/JISIoT.090216) |
| Harvard | Pallepati Vasavi, A. Punitha, T. VenkatNarayana Rao. (2023). Chili Leaf Disease Detection Using Deep Feature Extraction. Journal of Journal of Intelligent Systems and Internet of Things, 9 ( 2 ), 222-230 (Doi : https://doi.org/10.54216/JISIoT.090216) |
| Vancouver | Pallepati Vasavi, A. Punitha, T. VenkatNarayana Rao. Chili Leaf Disease Detection Using Deep Feature Extraction. Journal of Journal of Intelligent Systems and Internet of Things, (2023); 9 ( 2 ): 222-230 (Doi : https://doi.org/10.54216/JISIoT.090216) |
| IEEE | Pallepati Vasavi, A. Punitha, T. VenkatNarayana Rao, Chili Leaf Disease Detection Using Deep Feature Extraction, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 9 , No. 2 , (2023) : 222-230 (Doi : https://doi.org/10.54216/JISIoT.090216) |