Performance analysis of various deep learning models for detecting rice diseases
Abstract
A major portion of the world’s population relies on rice as a staple diet, hence rice is essential to maintaining food security worldwide. Unfortunately, rice crops are susceptible to a number of illnesses that, if detected and treated promptly, can result in significant output losses. Expert visual inspection is a time-consuming and arbitrary part of the conventional procedures for diagnosing diseases in rice. An effective method for automated illness diagnosis in agriculture has evolved in recent years: deep learning, a branch of artificial intelligence. The objective of this research is to compare AlexNet, DCNN, MobileNet, GoogleNet, VGG16, ResNet50 and Xception, these are various deep learning models in order to choose the one that would produce the highest levels of accuracy, precision recall, specificity, and F1-score for detecting rice diseases. In this study we train the model for nine different types of rice diseases named as Rice Blast (Pyricularia oryzae), Rice Sheath Blight (Rhizoctonia Solani), Bacterial Leaf Blight (Xanthomonas oryzae pv. oryzae), Tungro Disease, Rice Grassy Stunt Virus (RGSV), Rice Yellow Mottle Virus (RYMV), Bakanae Disease (Fusarium moniliforme), Brown Spot (Cochliobolus miyabeanus) and Rice Tungro Bacilliform Virus (RTBV) with 30,000 images. For this we used the secondary dataset for analyzing the performance of models. We trained the model for both normalized and non-normalized dataset. After comparing the various models we get the better result from ResNet50 model with accuracy of 97.50%.
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1. Latif G, Abdelhamid SE, Mallouhy RE, et al. Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model. Plants. 2022, 11(17): 2230. doi: 10.3390/plants11172230
2. Latif G, Alghazo J, Maheswar R, et al. Deep learning based intelligence cognitive vision drone for automatic plant diseases identification and spraying. Journal of Intelligent & Fuzzy Systems. 2020, 39(6): 8103-8114. doi: 10.3233/jifs-189132
3. Hesami M, Alizadeh M, Jones AMP, et al. Machine learning: its challenges and opportunities in plant system biology. Applied Microbiology and Biotechnology. 2022, 106(9-10): 3507-3530. doi: 10.1007/s00253-022-11963-6
4. Bari BS, Islam MN, Rashid M, et al. A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Computer Science. 2021, 7: e432. doi: 10.7717/peerj-cs.432
5. Latif G, Morsy H, Hassan A, et al. Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features. Viruses. 2022, 14(8): 1667. doi: 10.3390/v14081667
6. Koklu M, Cinar I, Taspinar YS. Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture. 2021, 187: 106285. doi: 10.1016/j.compag.2021.106285
7. Ramesh S, Vydeki D. Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm. Information Processing in Agriculture. 2020, 7(2): 249-260. doi: 10.1016/j.inpa.2019.09.002
8. Li D, Wang R, Xie C, et al. A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network. Sensors. 2020, 20(3): 578. doi: 10.3390/s20030578
9. Fei S, Hassan MA, He Z, et al. Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance. Remote Sensing. 2021, 13(12): 2338. doi: 10.3390/rs13122338
10. Zhang H, Wu C, Zhang Z, et al. ResNeSt: Split-Attention Networks. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Published online June 2022. doi: 10.1109/cvprw56347.2022.00309
11. Ramesh S, Tamilselvi M, Ramkumar G, et al. Comparison and analysis of Rice Blast disease identification in Greenhouse Controlled Environment and Field Environment using ML Algorithms. 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). Published online January 28, 2022. doi: 10.1109/accai53970.2022.9752538
12. Jena KK, Kumar Bhoi S, Mohapatra D, et al. Rice Disease Classification Using Supervised Machine Learning Approach. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). Published online November 11, 2021. doi: 10.1109/i-smac52330.2021.9641054
13. Venu Vasantha S, Samreen S, Lakshmi Aparna Y. Rice Disease Diagnosis System (RDDS). Computers, Materials & Continua. 2022, 73(1): 1895-1914. doi: 10.32604/cmc.2022.028504
14. Agrawal MM, Agrawal S. Rice plant diseases detection & classification using deep learning models: A systematic review. J Crit Rev. 2020; 7(11): 4376-4390.
15. Mohapatra D, Das N. A precise model for accurate rice disease diagnosis: a transfer learning approach. Proceedings of the Indian National Science Academy. 2023, 89(1): 162-171. doi: 10.1007/s43538-022-00149-3
16. Aggarwal M, Khullar V, Goyal N. Contemporary and Futuristic Intelligent Technologies for Rice Leaf Disease Detection. 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). Published online October 13, 2022. doi: 10.1109/icrito56286.2022.9965113
17. Alfred R, Obit JH, Chin CPY, et al. Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks. IEEE Access. 2021, 9: 50358-50380. doi: 10.1109/access.2021.3069449
18. Masood MH, Saim H, Taj M, Awais MM. Early disease diagnosis for rice crop. arXiv preprint arXiv:2004.04775. 2020 Apr 9.
19. Narmadha RP, Sengottaiyan N, Kavitha RJ. Deep Transfer Learning Based Rice Plant Disease Detection Model. Intelligent Automation & Soft Computing. 2022, 31(2): 1257-1271. doi: 10.32604/iasc.2022.020679
20. Chen J, Chen W, Zeb A, et al. Lightweight Inception Networks for the Recognition and Detection of Rice Plant Diseases. IEEE Sensors Journal. 2022, 22(14): 14628-14638. doi: 10.1109/jsen.2022.3182304
21. Hossain SM, Tanjil MdMM, Ali MAB, et al. Rice Leaf Diseases Recognition Using Convolutional Neural Networks. Lecture Notes in Computer Science. Published online 2020: 299-314. doi: 10.1007/978-3-030-65390-3_23
22. Rahman CR, Arko PS, Ali ME, et al. Identification and recognition of rice diseases and pests using convolutional neural networks. Biosystems Engineering. 2020, 194: 112-120. doi: 10.1016/j.biosystemseng.2020.03.020
23. Deng R, Tao M, Xing H, et al. Automatic Diagnosis of Rice Diseases Using Deep Learning. Frontiers in Plant Science. 2021, 12. doi: 10.3389/fpls.2021.701038
24. Aggarwal S, Suchithra M, Chandramouli N, et al. Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business. Gupta P, ed. Scientific Programming. 2022, 2022: 1-13. doi: 10.1155/2022/1757888
25. Ahmed K, Shahidi TR, Irfanul Alam SMd, et al. Rice Leaf Disease Detection Using Machine Learning Techniques. 2019 International Conference on Sustainable Technologies for Industry 40 (STI). Published online December 2019. doi: 10.1109/sti47673.2019.9068096
26. Tejaswini P, Singh P, Ramchandani M, et al. Rice Leaf Disease Classification Using Cnn. IOP Conference Series: Earth and Environmental Science. 2022, 1032(1): 012017. doi: 10.1088/1755-1315/1032/1/012017
27. Paddy Doctor: Paddy Disease Classification. Available online: https://www.kaggle.com/competitions/paddy-disease-classification/data (accessed on 20 October 2023).
28. Rice Leaf Disease Image Samples. Available online: https://data.mendeley.com/datasets/fwcj7stb8r/1 (accessed on 20 October 2023).
DOI: https://doi.org/10.32629/jai.v7i3.1282
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