Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Shrivastava, Gaurav
- Rice Plant Disease Identification Decision Support Model using Machine Learning
Abstract Views :225 |
PDF Views:94
Authors
Affiliations
1 Department of Computer Science and Engineering, Sage University, IN
1 Department of Computer Science and Engineering, Sage University, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 3 (2022), Pagination: 2619-2627Abstract
In this paper, we propose a decision support system for Indian rice farmers for identifying diseases. In a country like India, food security is an essential concern. Additionally, diseases in plants can cause a significant loss. Early-stage detection of diseases can help in improving the production of rice. In this context, first we investigate the recent contributed efforts in the field of plant disease detection by analysing plant leaves using machine learning and image processing techniques. Next, the datasets and relevant algorithms are concluded. Then, a machine learning model has been presented. The model includes the edge feature extraction using canny edge detection technique, colour features are extracted using grid colour movement, and the texture analysis is performed using Local Binary Pattern (LBP). In the next step, using the extracted features, we have prepared a combined feature vector to train the Machine Learning (ML) algorithms namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). These machine learning algorithms are organized in such a manner that the proposed decision support model can identify and differentiate the leaf plants. Additionally, it also recognizes the rice plants when we query. Secondly, the model is also able to recognize rice plant diseases. The first scenario of the experiment has been carried out using Plant Village dataset. The second scenario of experiment uses the rice plant disease dataset obtained from Kaggle with three classes. The second dataset used which is known as the Mendeley dataset which contains five different diseases as class labels. The experimental study with the implemented system confirms the superiority of ANN to be used with the proposed decision support system as compared to the SVM algorithm in terms of accuracy and time consumption. Finally, future work has also been highlighted.Keywords
Plant Disease Detection, Machine Learning, Image Processing, Food Security, Early Disease DetectionReferences
- FAO in India, “FAO in India”, Available at http://www.fao.org/india/fao-in-india/india-at-a-glance/en/, Available at 2021.
- K. Golhani, S.K. Balasundram, G. Vadamalai and B. Pradhan, “A Review of Neural Networks in Plant Disease Detection using Hyperspectral Data”, Information Processing in Agriculture, Vol. 5, pp. 354-371, 2018 [3] S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk and D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification”, Computational Intelligence and Neuroscience, Vol. 2016, pp. 1-11, 2016.
- U. Shruthi, V. Nagaveni and B.K. Raghavendra, “A Review on Machine Learning Classification Techniques for Plant Disease Detection”, Proceedings of International Conference on Advanced Computing and Communication Systems, pp. 1-6, 2019.
- Q.H. Cap, H. Tani, H. Uga, S. Kagiwada and H. Iyatomi, “Super-Resolution for Practical Automated Plant Disease Diagnosis System”, Proceedings of International Conference on Advanced Computing and Communication Systems, pp. 441-449, 2019.
- E. Fujita and Y. Kawasaki, “Basic Investigation on a Robust and Practical Plant Diagnostic System”, Proceedings of International Conference on Machine Learning and Applications, pp. 330-339, 2016.
- P. Sharma, Y.P.S. Berwal and W. Ghai, “Performance Analysis of Deep Learning CNN Models for Disease Detection in Plants using Image Segmentation”, Information Processing in Agriculture, Vol. 6, pp. 2214-3173, 2019.
- A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg and D.P. Hughes, “Deep Learning for Image-Based Cassava Disease Detection”, Wireless Communications and Mobile Computing, Vol. 2017, pp. 1-8, 2017.
- K.P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis”, Computers and Electronics in Agriculture, Vol. 145, pp. 311-318, 2018
- E.C. Too, L. Yujian, S. Njuki and L. Yingchun, “A Comparative Study of Fine-Tuning Deep Learning Models for Plant Disease Identification”, Computers and Electronics in Agriculture, Vol. 145, pp. 455-463, 2018.
- G. Wang, Y. Sun and J. Wang, “Automatic Image-Based Plant Disease Severity Estimation using Deep Learning”, Computational Intelligence and Neuroscience, Vol. 2017, pp. 1-8, 2017.
- D.O. Shamkuwar, G. Thakre, A.R. More, K.S. Gajakosh and M.O. Yewale, “An Expert System for Plant Disease Diagnosis by using Neural Network”, International Research Journal of Engineering and Technology, Vol. 5, No. 4, pp. 369-372, 2018.
- S. Ramesh, R. Hebbar, M. Niveditha, R. Pooja, N.P. Bhat, N. Shashank and P.V. Vinod, “Plant Disease Detection using Machine Learning”, Proceedings of International Conference on Computer and Communications, pp. 1-14, 2018.
- S.D. Khirade and A.B. Patil, “Plant Disease Detection using Image Processing”, Proceedings of International Conference on Computer and Communications, pp. 555-568, 2015.
- V. Singh and A.K. Misra, “Detection of Plant Leaf Diseases using Image Segmentation and Soft Computing Techniques”, Information Processing in Agriculture, Vol. 4, pp. 41-49, 2017.
- M. Islam, A. Dinh, K. Wahid and P. Bhowmik, “Detection of Potato Diseases using Image Segmentation and Multiclass Support Vector Machine”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-12, 2017.
- D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat and N. Batra, “PlantDoc: A Dataset for Visual Plant Disease Detection”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-12, 2020.
- S.S. Kumar and B.K. Raghavendra, “Diseases Detection of Various Plant Leaf Using Image Processing Techniques: A Review”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-13, 2019.
- S.A. Nandhini, R. Hemalatha, S. Radha and K. Indumathi, “Web Enabled Plant Disease Detection System for Agricultural Applications using WMSN”, Wireless Personal Communications, Vol. 76, pp. 725-740, 2018.
- H. Pourazar, F. Samadzadegan and F.D. Javan, “Aerial Multispectral Imagery for Plant Disease Detection: Radiometric Calibration Necessity Assessment”, European Journal of Remote Sensing, Vol. 52, No. 3, pp. 17-31, 2019.
- J.P. Shah, H.B. Prajapati and V. K. Dabhi, “A Survey on Detection and Classification of Rice Plant Diseases”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-8, 2016.
- P.S. Garud and R. Devi, “Detection of Diseases on Plant Leaf with the Help of Image Processing”, International Journal of Environmental Science and Technology, Vol. 4, No. 8, pp. 1-13, 2017.
- M. Ray, A. Ray, S. Dash, A. Mishra, K.G. Achary, S. Nayak and S. Singh, “Fungal Disease Detection in Traditional Assays, Novel Diagnostic Techniques and Biosensors”, Biosensors and Bioelectronics, Vol. 87, pp. 708-723, 2017.
- G. Dhingra, V. Kumar and H.D. Joshi, “Study of Digital Image Processing Techniques for Leaf Disease Detection and Classification”, Multimedia Tools and Applications, Vol. 78, pp. 1-14, 2017.
- X. Nie, L. Wang, H. Ding and M. Xu, “Strawberry Verticillium Wilt Detection Network Based on Multi-Task Learning and Attention”, Vol. 7, IEEE Access, pp. 170003-170011, 2019
- G. Owomugisha, E. Nuwamanya, J.A. Quinn, M. Biehl and E. Mwebaze, “Early Detection of Plant Diseases using Spectral Data”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-13, 2020.
- S. Iniyan, R. Jebakumar, P. Mangalraj, M. Mohit and A. Nanda, “Plant Disease Identification and Detection using Support Vector Machines and Artificial Neural Networks”, Proceedings of International Conference on Advances in Intelligent Systems and Computing, pp. 15-27, 2020
- K.P. Panigrahi, H. Das, A.K. Sahoo and S.C. Moharana, “Maize Leaf Disease Detection and Classification using Machine Learning Algorithms”, Proceedings of International Conference on Advances in Intelligent Systems and Computing, pp. 659-669, 2020.
- Plant Village, Available at https://www.kaggle.com/abdallahalidev/plantvillage-dataset/version/1, Accessed at 2021.
- Rice Leaf Disease, Available at https://www.kaggle.com/vbookshelf/rice-leaf-diseases, Accessed at 2021.
- Canny Edge Detection, Available at https://www.cse.iitd.ac.in/~pkalra/col783-2017/canny.pdf, Accessed at 2009.
- M. Nosrati, R. Karimi and M. Hariri, “Detecting Circular Shapes from Areal Images using Median Filter and CHT”, Global Journal of Computer Science and Technology, Vol 2, No. 1, pp. 49-54, 2012.
- Z. Guo, L. Zhang and D. Zhang, “A Completed Modeling of Local Binary Pattern Operator for Texture Classification”, IEEE Transactions on Image Processing, Vol. 19, No. 6, pp. 1657-1663, 2010
- Y.G. Jiang, J. Yang, C.W. Ngo and A.G. Hauptmann, “Representations of Key Point-Based Semantic Concept Detection: A Comprehensive Study”, IEEE Transactions on Multimedia, Vol. 12, No. 1, pp. 42-53, 2008. Plants:
- Secure Storage and Data Sharing Scheme Using Private Blockchain-Based HDFS Data Storage for Cloud Computing
Abstract Views :114 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, SAGE University, Indore, Madhya Pradesh, IN
1 Department of Computer Science and Engineering, SAGE University, Indore, Madhya Pradesh, IN
Source
International Journal of Computer Networks and Applications, Vol 10, No 1 (2023), Pagination: 28-38Abstract
The storage of a vast quantity of data in the cloud, which is then delivered via the internet, enables Cloud Computing to make doing business easier by providing smooth access to the data and eliminating device compatibility limits. Data that is in transit, on the other hand, may be intercepted by a man-in-the-middle attack, a known plain text assault, a selected cypher text attack, a related key attack, or a pollution attack. Uploading data to a single cloud might, as a result, increase the likelihood that the secret data would be damaged. A distributed file system extensively used in huge data analysis for frameworks such as Hadoop is known as the Hadoop Distributed File System, more commonly referred to as HDFS. Because with HDFS, it is possible to manage enormous volumes of data while using standard hardware that is not very costly. On the other hand, HDFS has several security flaws that might be used for malicious purposes. This highlights how critical it is to implement stringent security measures to make it easier for users to share files inside Hadoop and to have a reliable system in place to validate the shared files' validity claims. The major focus of this article is to discuss our efforts to improve the security of HDFS by using an approach made possible by blockchain technology (hereafter referred to as BlockHDFS). To be more precise, the proposed BlockHDFS uses the Hyperledger Fabric platform, which was developed for business applications, to extract the most value possible from the data inside files to provide reliable data protection and traceability in HDFS. In the results section, the performance of AES is superior to that of other encryption algorithms because it ranges from 1.2 milliseconds to 1.9 milliseconds. In contrast, DES ranges from 1.3 milliseconds to 3.1 milliseconds, three milliseconds to 3.6 millimetres, RC2 milliseconds to 3.9 milliseconds, and RSA milliseconds to 1.4 milliseconds, with data sizes ranging from 910 kilos.Keywords
Cloud Computing, Hadoop Distributed File System, Blockchain, Authenticity, Data Security, DES, AES.References
- G. Kumar et al., "A Novel Framework for Fog Computing: Lattice-Based Secured Framework for Cloud Interface," in IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7783-7794, Aug. 2020, doi: 10.1109/JIOT.2020.2991105.
- X. Liu, G. Yang, Y. Mu and R. H. Deng, "Multi-User Verifiable Searchable Symmetric Encryption for Cloud Storage," in IEEE Transactions on Dependable and Secure Computing, vol. 17, no. 6, pp. 1322-1332, 1 Nov.-Dec. 2020, doi: 10.1109/TDSC.2018.2876831.
- Benet, J. Ipfs-content addressed, versioned, p2p file system. arXiv 2014, arXiv:1407.3561.
- J. Wei, X. Chen, X. Huang, X. Hu and W. Susilo, "RS-HABE: Revocable-Storage and Hierarchical Attribute-Based Access Scheme for Secure Sharing of e-Health Records in Public Cloud," in IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 5, pp. 2301-2315, 1 Sept.-Oct. 2021, doi: 10.1109/TDSC.2019.2947920.
- H. Wang, L. Feng, Y. Ji, B. Shao and R. Xue, "Toward Usable Cloud Storage Auditing, Revisited," in IEEE Systems Journal, vol. 16, no. 1, pp. 693-700, March 2022, doi: 10.1109/JSYST.2021.3055021.
- Apache Hadoop, URL, http://hadoop.apache.org, 2006.
- K. Shvachko, H. Kuang, S. Radia, R. Chansler, The hadoop distributed file system, in: 2010 IEEE 26th Sym- Posium on Mass Storage Systems and Technologies (MSST); 3–7 May 2010; Incline Village, NV, USA, IEEE, Piscataway, NJ, USA, 2010, pp. 1–10.
- S.A. Weil, S.A. Brandt, E.L. Miller, D.D.E. Long, C. Maltzahn, Ceph: a scalable, high performance dis- tributed file system, in: Proceedings of the 7th Symposium on Operating Systems Design and Implementation, OSDI '06; 6–8 Nov 2006; Seattle, WA, USA, USENIX Association, Berkeley, CA, USA, 2006, pp. 307–320.
- F. Schmuck, R. Haskin, Gpfs: a shared-disk file system for large computing clusters, in: Proceedings of the 1st USENIX Conference on File and Storage Technologies, FAST '02; 28–30 Jan 2002; Monterey, CA, USA, USENIX Association, Berkeley, CA, USA, 2002.
- C. Ungureanu, B. Atkin, A. Aranya, et al., HydraFS: A high-throughput file system for the hydrastor content-addressable storage system, in: Proceedings of the 8th USENIX Conference on File and Storage Technologies, FAST'10; 23–26 Feb 2010; San Jose, CA, USA, USENIX Association, Berkeley, CA, USA, 2010, pp. 225–239.
- J. Dean, S. Ghemawat, Mapreduce: simplified data processing on large clusters, Commun. ACM 51 (1) (2008) 107–113.
- M. Zaharia, R.S. Xin, P. Wendell, T. Das, M. Armbrust, A. Dave, X. Meng, J. Rosen, S. Venkataraman, M.J. Franklin, et al., Apache spark: a unified engine for big data processing, Commun. ACM 59 (11) (2016) 56–65.
- X. Chen, L. Hu, L. Liu, J. Chang and D. L. Bone, "Breaking Down Hadoop Distributed File Systems Data Analytics Tools: Apache Hive vs. Apache Pig vs. Pivotal HWAQ," 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), 2017, pp. 794-797, doi: 10.1109/CLOUD.2017.117.
- M.N. Vora, Hadoop-hbase for large-scale data, in: Proceedings of 2011 International Conference on Computer Science and Network Technology; 24–26 Dec 2011; Harbin, China, IEEE, Piscataway, NJ, USA, 2011, pp. 601–605.
- F. Shahid, H. Ashraf, A. Ghani, S. A. K. Ghayyur, S. Shamshirband and E. Salwana, "PSDS–Proficient Security Over Distributed Storage: A Method for Data Transmission in Cloud," in IEEE Access, vol. 8, pp. 118285-118298, 2020, doi: 10.1109/ACCESS.2020.3004433.
- J. Tang, J. Nie, Z. Xiong, J. Zhao, Y. Zhang and D. Niyato, "Slicing-Based Reliable Resource Orchestration for Secure Software-Defined Edge-Cloud Computing Systems," in IEEE Internet of Things Journal, vol. 9, no. 4, pp. 2637-2648, 15 Feb.15, 2022, doi: 10.1109/JIOT.2021.3107490.
- A. Saini, Q. Zhu, N. Singh, Y. Xiang, L. Gao and Y. Zhang, "A Smart-Contract-Based Access Control Framework for Cloud Smart Healthcare System," in IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5914-5925, 1 April1, 2021, doi: 10.1109/JIOT.2020.3032997.
- S. Sengupta and S. S. Bhunia, "Secure Data Management in Cloudlet Assisted IoT Enabled e-Health Framework in Smart City," in IEEE Sensors Journal, vol. 20, no. 16, pp. 9581-9588, 15 Aug.15, 2020, doi: 10.1109/JSEN.2020.2988723.
- Z. Su et al., "Secure and Efficient Federated Learning for Smart Grid With Edge-Cloud Collaboration," in IEEE Transactions on Industrial Informatics, vol. 18, no. 2, pp. 1333-1344, Feb. 2022, doi: 10.1109/TII.2021.3095506.
- S. Srinivasan, "Cloud load balancing: Blockchain deployment at integrated DopCloud synthesis on Healthcare data," 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), 2020, pp. 364-369, doi: 10.1109/ICSSIT48917.2020.9214084.
- G. Senthilkumar and M. P. Chitra, "A Novel hybrid heuristic-metaheuristic Load balancing algorithm for Resource allocationin IaaS-cloud computing," 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), 2020, pp. 351-358, doi: 10.1109/ICSSIT48917.2020.9214280.
- J. Cai, Y. Hu and Y. Li, "Research on the Method of Building a Secure Cloud Storage Platform," 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), 2022, pp. 17-20, doi: 10.1109/IWECAI55315.2022.00011.
- B. Tang and G. Fedak, "WukaStore: Scalable, Configurable and Reliable Data Storage on Hybrid Volunteered Cloud and Desktop Systems," in IEEE Transactions on Big Data, vol. 8, no. 1, pp. 85-98, 1 Feb. 2022, doi: 10.1109/TBDATA.2017.2758791.
- J. Li, H. Yan and Y. Zhang, "Efficient Identity-Based Provable Multi-Copy Data Possession in Multi-Cloud Storage," in IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 356-365, 1 Jan.-March 2022, doi: 10.1109/TCC.2019.2929045.
- Gupta, A. K. Singh, C. -N. Lee and R. Buyya, "Secure Data Storage and Sharing Techniques for Data Protection in Cloud Environments: A Systematic Review, Analysis, and Future Directions," in IEEE Access, vol. 10, pp. 71247-71277, 2022, doi: 10.1109/ACCESS.2022.3188110.
- J. -N. Liu et al., "Enabling Efficient, Secure and Privacy-Preserving Mobile Cloud Storage," in IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 3, pp. 1518-1531, 1 May-June 2022, doi: 10.1109/TDSC.2020.3027579.
- X. Li, T. Xiang, Y. Mu, F. Guo and Z. Yao, "C-Wall: Conflict-Resistance in Privacy-Preserving Cloud Storage," in IEEE Transactions on Cloud Computing, doi: 10.1109/TCC.2022.3171772.
- M. A. M. Ahsan, I. Ali, M. Imran, M. Y. I. B. Idris, S. Khan and A. Khan, "A Fog-Centric Secure Cloud Storage Scheme," in IEEE Transactions on Sustainable Computing, vol. 7, no. 2, pp. 250-262, 1 April-June 2022, doi: 10.1109/TSUSC.2019.2914954.
- K. Lee, J. Kim, J. Kwak and Y. Kim, "Dynamic Multi-Resource Optimization for Storage Acceleration in Cloud Storage Systems," in IEEE Transactions on Services Computing, doi: 10.1109/TSC.2022.3173333.
- G. Revathy, P. Muruga Priya, R. Saranya and C. Ramchandran, "Cloud Storage and Authenticated Access For Intelligent Medical System," 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022, pp. 53-56, doi: 10.1109/ICCMC53470.2022.9753765.
- C. Liang, L. Deng, J. Zhu, Z. Cao and C. Li, "Cloud Storage I/O Load Prediction Based on XB-IOPS Feature Engineering," 2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2022, pp. 54-60, doi: 10.1109/BigDataSecurityHPSCIDS54978.2022.00020.
- Y. Yang, Y. Chen, F. Chen and J. Chen, "An Efficient Identity-Based Provable Data Possession Protocol With Compressed Cloud Storage," in IEEE Transactions on Information Forensics and Security, vol. 17, pp. 1359-1371, 2022, doi: 10.1109/TIFS.2022.3159152.
- V. J. Sosa-Sosa, A. Barron, J. L. Gonzalez-Compean, J. Carretero and I. Lopez-Arevalo, "Improving Performance and Capacity Utilisation in Cloud Storage for Content Delivery and Sharing Services," in IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 439-450, 1 Jan.-March 2022, doi: 10.1109/TCC.2020.2968444.
- J. Ni, K. Zhang, Y. Yu and T. Yang, "Identity-Based Provable Data Possession From RSA Assumption for Secure Cloud Storage," in IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 3, pp. 1753-1769, 1 May-June 2022, doi: 10.1109/TDSC.2020.3036641.
- Z. Ullah, B. Raza, H. Shah, S. Khan and A. Waheed, "Towards Blockchain-Based Secure Storage and Trusted Data Sharing Scheme for IoT Environment," in IEEE Access, vol. 10, pp. 36978-36994, 2022, doi: 10.1109/ACCESS.2022.3164081.
- K. B. Jyothilakshmi, V. Robins, and A. S. Mahesh, "A comparative analysis between hyperledger fabric and ethereum in medical sector: A systematic review," in Sustainable Communication Networks and Application. Singapore: Springer, 2022, pp. 67–86.
- G Shrivastava and S. Patel, “Hybrid Confidentiality Framework for Secured Cloud Computing” in 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT), 07-09 October 2022, 10.1109/GCAT55367.2022.9972165.
- A Deep Learning Model for Improving the Rice Plant Disease Detection Performance
Abstract Views :95 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, Sage University, IN
1 Department of Computer Science and Engineering, Sage University, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 1 (2023), Pagination: 2775-2781Abstract
Rice is one of the most utilized grains in India. It is a seasonal crop which mostly grows between June to October. This crop mostly grows in natural conditions and its production has a significant influence on different diseases in the plant. Early stage detection of diseases can help in improving the production. In this paper, an analysis and study on deep learning models for getting accurate rice plant disease detection is presented. In this context, first the recent contributions on detecting the diseases by analysing the plant leaf images are reviewed. Then, a comparison among sequential model and 2D-CNN model has been performed. The experimental analysis demonstrates that 2D-CNN outperforms as compared to the simple sequential model. The experiments are extended by including the different image feature selection models. In order to extract features, sobel based edge detection, Local Binary Pattern (LBP) based texture analysis and their combinations i.e. sobel and LBP, Sobel, LBP and color, and a combination of color and sobel are used. The experiments are performed on Kaggle based rice plant disease detection dataset and the performance in terms of precision, recall, f1-score and accuracy has been measured. The experimental evaluation highlights two major points (1) the CNN does not require additional features for better classification consequences (2) the highly trained models are able to respond faster as compared to less trained models. Based on the obtained performance, a more accurate model for plant disease detection is designed.Keywords
Plant Disease Detection, Machine Learning, Image Processing, Food Security, Early Disease DetectionReferences
- FAO, “FAO in India”, Available at http://www.fao.org/india/fao-in-india/india-at-a-glance/en/, Accessed at 2021.
- K. Golhani, S.K. Balasundram, G. Vadamalai and B. Pradhan, “A Review of Neural Networks in Plant Disease Detection using Hyperspectral Data”, Information Processing in Agriculture, Vol. 5, pp. 354-371, 2018.
- S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk and D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification”, Computational Intelligence and Neuroscience, Vol. 2016, pp. 1-11, 2016.
- U. Shruthi, V. Nagaveni and B.K. Raghavendra, “A Review on Machine Learning Classification Techniques for Plant Disease Detection”, Proceedings of International Conference on Advanced Computing and Communication Systems, pp. 1-13, 2019.
- Q.H. Cap, H. Tani, H. Uga, S. Kagiwada and H. Iyatomi, “Super-Resolution for Practical Automated Plant Disease Diagnosis System”, Proceedings of International Conference on Advanced Computing, pp. 1-13, 2019.
- E. Fujita and Y. Kawasaki, “Basic Investigation on a Robust and Practical Plant Diagnostic System”, Proceedings of International Conference on Machine Learning and Applications, pp. 1-8, 2016.
- P. Sharma, Y.P.S. Berwal and W. Ghai, “Performance Analysis of Deep Learning CNN Models for Disease Detection in Plants using Image Segmentation”, Information Processing in Agriculture, Vol. 34, pp. 2214-3173, 2019.
- A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg and D.P. Hughes, “Deep Learning for Image-Based Cassava Disease Detection”, Vol. 8, No. 2, pp. 1-13, 2017.
- K.P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis”, Computers and Electronics in Agriculture, Vol. 145, pp. 311-318, 2018.
- E.C. Too, L. Yujian, S. Njuki and L. Yingchun, “A Comparative Study of Fine-Tuning Deep Learning Models for Plant Disease Identification”, Computers and Electronics in Agriculture, Vol. 143, pp. 252-264, 2018.
- G. Wang, Y. Sun and J. Wang, “Automatic Image-Based Plant Disease Severity Estimation using Deep Learning”, Computational Intelligence and Neuroscience, Vol. 2017, pp. 1-8, 2017.
- D.O. Shamkuwar, G. Thakre, A.R. More, K.S. Gajakosh and M.O. Yewale, “An Expert System for Plant Disease Diagnosis by using Neural Network”, International Research Journal of Engineering and Technology, Vol. 5, No. 4, pp. 1-13, 2018.
- S. Ramesh, R. Hebbar, M. Niveditha, R. Pooja, N.P. Bhat, N. Shashank and P.V. Vinod, “Plant Disease Detection using Machine Learning”, Proceedings of International Conference on Design Innovations for 3Cs Compute Communicate Control, pp. 1-12, 2018.
- S.D. Khirade and A.B. Patil, “Plant Disease Detection using Image Processing”, Proceedings of International Conference on Computing Communication Control and Automation, pp. 1-13, 2015.
- V. Singh and A.K. Misra, “Detection of Plant Leaf Diseases using Image Segmentation and Soft Computing Techniques”, Information Processing in Agriculture, Vol. 4, pp. 41-49, 2017.
- M. Islam, A. Dinh, K. Wahid and P. Bhowmik, “Detection of Potato Diseases using Image Segmentation and Multiclass Support Vector Machine”, Proceedings of International Conference on Electrical and Computer Engineering, pp. 1-13, 2017.
- D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat and N. Batra, “PlantDoc: A Dataset for Visual Plant Disease Detection”, Proceedings of International Conference on Computing Applications, pp. 5-7, 2020.
- S.S. Kumar and B.K. Raghavendra, “Diseases Detection of Various Plant Leaf Using Image Processing Techniques: A Review”, Proceedings of International Conference on Advanced Computing and Communication Systems, pp. 1-13, 2019.
- S.A. Nandhini, R. Hemalatha, S. Radha and K. Indumathi, “Web Enabled Plant Disease Detection System for Agricultural Applications using WMSN”, Wireless Personal Communications, Vol. 98, pp. 1-13, 2019.
- H. Pourazar, F. Samadzadegan and F.D. Javan, “Aerial Multispectral Imagery for Plant Disease Detection:
- Radiometric Calibration Necessity Assessment”, European Journal of Remote Sensing, Vol. 52, No. 3, pp. 17-31, 2019.
- J.P. Shah, H.B. Prajapati and V.K. Dabhi, “A Survey on Detection and Classification of Rice Plant Diseases”, Proceedings of International Conference on Advanced Computing and Communications, pp. 1-13, 2016.
- P.S. Garud and R. Devi, “Detection of Diseases on Plant Leaf with the Help of Image Processing”, International Journal of Engineering Technology Science and Research, Vol. 4, No. 8, pp. 1-13, 2017.
- M. Ray, A. Ray, S. Dash, A. Mishra, K.G. Achary, S. Nayak and S. Singh, “Fungal Disease Detection in Plants: Traditional Assays, Novel Diagnostic Techniques and Biosensors”, Biosensors and Bioelectronics, Vol. 87, pp. 708-723, 2017.
- G. Dhingra, V. Kumar and H. D. Joshi, “Study of Digital Image Processing Techniques for Leaf Disease Detection and Classification”, Multimedia Tools and Applications, Vol. 78, pp. 1-18, 2017.
- X. Nie, L. Wang, H. Ding and M. Xu, “Strawberry Verticillium Wilt Detection Network Based on Multi-Task Learning and Attention”, Proceedings of International Conference on Advanced Computing, pp. 1-17, 2017.
- G. Owomugisha, E. Nuwamanya, J.A. Quinn, M. Biehl and E. Mwebaze, “Early Detection of Plant Diseases using Spectral Data”, Proceedings of International Conference on Computing Machinery, pp. 7-9, 2020.
- S. Iniyan, R. Jebakumar, P. Mangalraj, M. Mohit and A. Nanda, “Plant Disease Identification and Detection using Support Vector Machines and Artificial Neural Networks”, Advances in Intelligent Systems and Computing, Vol. 1056, 2020.
- K.P. Panigrahi, H. Das, A.K. Sahoo and S.C. Moharana, “Maize Leaf Disease Detection and Classification using Machine Learning Algorithms”, Advances in Intelligent Systems and Computing, Vol. 1119, 2020.
- Rice Leaf Disease, Available at https://www.kaggle.com/vbookshelf/rice-leaf-diseases, Accessed at 2022.
- G. Shrivastava and H. Patidar, “Rice Plant Disease Identification Decision Support Model using Machine Learning”, ICTACT Journal on Soft Computing, Vol. 12, No. 3, pp. 2619-2627, 2022