Hybrid Approach for Biotic Stress Severity Estimation using Random Forest Regressor
Keywords:
ES-DeepNet, Ensembling, Disease Diagnosis, Severity Assessment, Thresholding, Random Forest Regressor.Abstract
Biotic stresses significantly impact paddy cultivation, necessitating accurate and timely disease diagnosis. Manual supervision might not capture the early symptoms of the biotic stress associated with certain diseases. Hence, it requires the best disease management strategies that help to perform good practices under paddy crop cultivation. The proposed work presents a novel approach integrating ensemble convolutional neural networks (ES-DeepNet) predictions and disease severity features for enhanced paddy disease classification and severity prediction for the paddy doctor dataset. ES-DeepNet, comprised of VGG16, Xception, MobileNet, and baseline models, effectively extracts robust features thus improving classification accuracy. Disease severity is assessed quantitatively, and extracted features are integrated into the model with ES-DeepNet model predictions using a random forest regressor. The method demonstrated its effectiveness in both disease classification and severity estimation. This study combines deep learning and machine learning techniques, that contribute to precision agriculture by providing a comprehensive solution for paddy disease management.
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S. M. Jaisakthi, P. Mirunalini, D. Thenmozhi and Vatsala, "Grape Leaf Disease Identification using Machine Learning Techniques," 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2019, pp. 1-6, doi: 10.1109/ICCIDS.2019.8862084.
Jayswal, Hardikkumar& Chaudhari, Jitendra. (2020). Plant Leaf Disease Detection and Classification using Conventional Machine Learning and Deep Learning. 1094-1102.
Ghosal, S., Blystone, D., Singh, A. K., Ganapathysubramanian, B., Singh, A., and Sarkar, S., “An explainable deep machine vision framework for plant stress phenotyping”, Proceedings of the National Academy of Science, vol. 115, no. 18, pp. 4613–4618, 2018. doi:10.1073/pnas.1716999115
Kaur, A., Guleria, K. & Trivedi, N.K. A deep learning-based model for biotic rice leaf disease detection. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18730-x
Dumoulin, Vincent, and Francesco Visin. "A guide to convolution arithmetic for deep learning." arXiv preprint arXiv:1603.07285 (2016).
Alnaggar, Y.A., Sebaq, A., Amer, K., Naeem, E., Elhelw, M. (2023). Rice Plant Disease Detection and Diagnosis Using Deep Convolutional Neural Networks and Multispectral Imaging. In: Fournier-Viger, P., Hassan, A., Bellatreche, L. (eds) Model and Data Engineering. MEDI 2022. Lecture Notes in Computer Science, vol 13761. Springer, Cham. https://doi.org/10.1007/978-3-031-21595-7_2
Arunangshu Pal, Vinay Kumar, AgriDet: Plant Leaf Disease severity classification using agriculture detection framework, Engineering Applications of Artificial Intelligence, Volume 119, 2023, 105754, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2022.105754.
Yu Jiang, Changying Li. Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review. Plant Phenomics. 2020;2020:DOI:10.34133/2020/4152816
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
"Junde Chen, Jinxiu Chen, Defu Zhang, Yuandong Sun, Y.A. Nanehkaran, Using deep transfer learning for image-based plant disease identification, Computers and Electronics in Agriculture, Volume 173,2020,105393,ISSN 0168-1699, https://doi.org/10.1016/j.compag.2020.105393"
Hassan, S.M.; Maji, A.K.; Jasiński, M.; Leonowicz, Z.; Jasińska, E. Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach. Electronics 2021, 10, 1388. https://doi.org/10.3390/electronics10121388
Simhadri, Chinna Gopi &Kondaveeti, Hari. (2023). Transfer Learning for Rice Leaf Disease Detection. 509-515. 10.1109/ICAIS56108.2023.10073711.
Chen J, Zhang D, Nanehkaran YA, Li D. Detection of rice plant diseases based on deep transfer learning. J Sci Food Agric. 2020 May;100(7):3246-3256. doi: 10.1002/jsfa.10365. Epub 2020 Mar 14. PMID: 32124447.
"Naveen N. Malvade, Rajesh Yakkundimath, Girish Saunshi, Mahantesh C. Elemmi, ParashuramBaraki, A comparative analysis of paddy crop biotic stress classification using pre-trained deep neural networks, Artificial Intelligence in Agriculture, Volume 6,2022, Pages 167-175, ISSN 2589-7217, https://doi.org/10.1016/j.aiia.2022.09.001."
YohannesAgegnehuBezabh, BiniyamMulugetaAbuhayi, AlekaMeleseAyalew, HabtamuAyenewAsegie, Classification of pumpkin disease by using a hybrid approach, Smart Agricultural Technology, Volume 7,2024,100398, ISSN 2772-3755,https://doi.org/10.1016/j.atech.2024.100398.
Lamba, S., Kukreja, V., Baliyan, A., Rani, S., & Ahmed, S.H. (2023). A Novel Hybrid Severity Prediction Model for Blast Paddy Disease Using Machine Learning. Sustainability.
Saminathan, K., B. Sowmiya and Devi M Chithra. “Multiclass Classification of Paddy Leaf Diseases Using Random Forest Classifier.” Journal of Image and Graphics (2023):
Naik, H.S., Zhang, J., Lofquist, A. et al. A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant Methods 13, 23 (2017). https://doi.org/10.1186/s13007-017-0173-7
Zhang G, Xu T, Tian Y, Feng S, Zhao D, Guo Z. Classification of rice leaf blast severity using hyperspectral imaging. Sci Rep. 2022 Nov 17;12(1):19757. doi: 10.1038/s41598-022-22074-7. PMID: 36396749; PMCID: PMC9672119.
Jindal, V., Kukreja, V., Mehta, S., Bordoloi, D., & Singh, V. (2023). Severity-Level Assessment of Groundnut Leaf Diseases: A Federated Learning and CNN Approach. 2023 4th IEEE Global Conference for Advancement in Technology (GCAT), 1-6.
Yang, B., Li, M., Li, F. et al. A novel plant type, leaf disease, and severity identification framework using CNN and transformer with multi-label method. Sci Rep 14, 11664 (2024). https://doi.org/10.1038/s41598-024-62452-x
Bedi P, Gole P, Marwaha S. PDSE-Lite: lightweight framework for plant disease severity estimation based on Convolutional Autoencoder and Few-Shot Learning. Front Plant Sci. 2024 Jan 8;14:1319894. doi: 10.3389/fpls.2023.1319894. PMID: 38259916; PMCID: PMC10800669.
M.A. Ganaie, Minghui Hu, A.K. Malik, M. Tanveer, P.N. Suganthan, Ensemble deep learning: A review, Engineering Applications of Artificial Intelligence, Volume 115,2022, 105151, ISSN 0952-1976,https://doi.org/10.1016/j.engappai.2022.105151.
A. Acharya, A. Muvvala, S. Gawali, R. Dhopavkar, R. Kadam and A. Harsola, "Plant Disease detection for paddy crop using Ensemble of CNNs," 2020 IEEE International Conference for Innovation in Technology (INOCON), Bangluru, India, 2020, pp. 1-6, doi: 10.1109/INOCON50539.2020.9298295.
Chen, J., Zeb, A., Nanehkaran, Y.A. et al. Stacking ensemble model of deep learning for plant disease recognition. J Ambient Intell Human Comput 14, 12359–12372 (2023). https://doi.org/10.1007/s12652-022-04334-6
Citation: Lamba, S., Baliyan, A., Kukreja, V., Tripathy, R. (2023). An Ensemble (CNN-LSTM) Model for Severity Detection of Bacterial Blight Rice Disease. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore. https://doi.org/10.1007/978-981-19-7982-8_14
Kundur, N. C. ., &Mallikarjuna, P. B. . (2022). Ensemble Efficient Net and ResNet model for Crop Disease Identification. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 378–390. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2273
M.A. Ganaie, Minghui Hu, A.K. Malik, M. Tanveer, P.N. Suganthan, Ensemble deep learning: A review, Engineering Applications of Artificial Intelligence, Volume 115,2022, 105151, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2022.105151.
Bruno A, Moroni D, Dainelli R, Rocchi L, Morelli S, Ferrari E, Toscano P, Martinelli M. Improving plant disease classification by adaptive minimal ensembling. Front ArtifIntell. 2022 Sep 8;5:868926. doi: 10.3389/frai.2022.868926. PMID: 36160929; PMCID: PMC9499023.
J. Deng, W. Dong, R. Socher, L. -J. Li, Kai Li, and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database," 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 2009, pp. 248-255, doi: 10.1109/CVPR.2009.5206848.
Tin Kam Ho, "Random decision forests," Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 1995, pp. 278-282 vol.1, doi: 10.1109/ICDAR.1995.598994.
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