Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Deep learning system for paddy plant disease detection and classification

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Automatic detection and analysis of rice crop diseases is widely required in the farming industry, which can be utilized to avoid squandering financial and other resources, reduce yield losses, and improve treatment efficiency, resulting in healthier crop output. An automated approach was proposed for accurately detecting and classifying diseases from a supplied photograph. The proposed system for the recognition of rice plant diseases adopts a computer vision–based approach that employs the techniques of image processing, machine learning, and deep learning, reducing the reliance on conventional methods to protect paddy crops from diseases like bacterial leaf blight, false smut, brown leaf spot, rice blast, and sheath rot, the five primary diseases that frequently plague the Indian rice fields. Following image pre-processing, image segmentation is employed to determine the diseased section of the paddy plant, with the diseases listed above being identified purely on the basis of their visual contents. An integration of a support vector machine classifier and convolutional neural networks are used to recognize and classify specific varieties of paddy plant diseases. With ReLU and softmax functions, the suggested deep learning–based strategy attained the highest validation accuracy of 0.9145. Following recognition, a predictive remedy is recommended, which can assist agriculture-related individuals and organizations in taking suitable measures to combat these diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ahmed, F., Al-Mamun, H. A., Bari, A. H., Hossain, E., & Kwan, P. (2012). Classification of crops and weeds from digital images: A support vector machine approach. Crop Protection, 40, 98–104.

    Article  Google Scholar 

  • Aldino, A., Darwis, D., Prastowo, A., & Sujana, C. (2021). Implementation of k-means algorithm for clustering corn planting feasibility area in South Lampung Regency. In Journal of Physics: Conference Series (p. 012038). IOP Publishing volume 1751.

  • Anne-Katrin Mahlein, U. S., Oerke, Erich-Christian., & Dehne, H.-W. (2012). Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology, 133, 197–209.

    Article  Google Scholar 

  • Arora, P., Varshney, S., et al. (2016). Analysis of k-means and k-medoids algorithm for big data. Procedia Computer Science, 78, 507–512.

    Article  Google Scholar 

  • Athiraja, A., & Vijayakumar, P. (2020). Banana disease diagnosis using computer vision and machine learning methods. Journal of Ambient Intelligence and Humanized Computing, (pp. 1–20).

  • AYDïLEK, ï. B. (2018). Examining effects of the support vector machines kernel types on biomedical data classification. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1–4). IEEE.

  • Bakar, M. A., Abdullah, A., Rahim, N. A., Yazid, H., Misman, S., & Masnan, M. (2018). Rice leaf blast disease detection using multi-level colour image thresholding. Journal of Telecommunication Electronic and Computer Engineering (JTEC), 10, 1–6.

    Google Scholar 

  • Bharati, P., & Pramanik, A. (2020). Deep learning techniques–R-CNN to mask R-CNN: A survey. In Computational Intelligence in Pattern Recognition (pp. 657–668). Springer.

  • Banu, C. K., & Vishnupriya, M. (2021). Awareness and use of krishibhavan services by the farmers: A study from Kerala, India. Library Philosophy and Practice, (pp. 1–13).

  • Barbedo, J. G. A. (2016). A review on the main challenges in automatic plant disease identification based on visible range images. Biosystems Engineering, 144, 52–60.

    Article  Google Scholar 

  • Barga, R., Fontama, V., & Tok, W. H. (2015a). Introducing Microsoft Azure machine learning. In Predictive Analytics with Microsoft Azure Machine Learning (pp. 21–43). Springer.

  • Barga, R., Fontama, V., Tok, W. H., & Cabrera-Cordon, L. (2015b). Predictive analytics with Microsoft Azure machine learning. Springer.

  • Bigirimana, V. D. P., Hua, G. K., Nyamangyoku, O. I., & Höfte, M. (2015). Rice sheath rot: An emerging ubiquitous destructive disease complex. Frontiers in Plant Science, 6, 1066.

    Article  Google Scholar 

  • Borge, S., & Poonia, N. (2020). Review on Amazon Web Services, Google Cloud Provider and Microsoft Windows Azure. Advance and Innovative Research, (p. 53).

  • Brownlee, J. (2019). Deep learning for computer vision: Image classification, object detection, and face recognition in Python. Machine Learning Mastery.

  • Chai, R. (2021). Otsu’s image segmentation algorithm with memory-based fruit fly optimization algorithm. Complexity, 2021.

  • Chaki, J., & Dey, N. (2019). A beginner’s guide to image shape feature extraction techniques. London: CRC Press.

  • Chatterjee, S., Suman, A., Gaurav, R., Banerjee, S., Singh, A. K., Ghosh, B. K., Mandal, R. K., Biswas, M., & Maji, D. (2021). Retinal blood vessel segmentation using edge detection method. In Journal of Physics: Conference Series (p. 012008). IOP Publishing volume 1717.

  • Cheng, H.-D., Jiang, X. H., Sun, Y., & Wang, J. (2001). Color image segmentation: Advances and prospects. Pattern Recognition, 34, 2259–2281.

    Article  Google Scholar 

  • Chukwu, S., Rafii, M., Ramlee, S., Ismail, S., Hasan, M., Oladosu, Y., Magaji, U., Akos, I., & Olalekan, K. (2019). Bacterial leaf blight resistance in rice: A review of conventional breeding to molecular approach. Molecular Biology Reports, 46, 1519–1532.

    Article  CAS  Google Scholar 

  • Cisternas, I., Velásquez, I., Caro, A., & Rodríguez, A. (2020). Systematic literature review of implementations of precision agriculture. Computers and Electronics in Agriculture, 176, 105626.

    Article  Google Scholar 

  • Crop pest surveillance system. (2019). https://keralaagriculture.gov.in/. Retrieved 25 Feb 2019.

  • Dhingra, G., Kumar, V., & Joshi, H. D. (2018). Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications, 77, 19951–20000.

    Article  Google Scholar 

  • Ebrahimi, M., Khoshtaghaza, M., Minaei, S., & Jamshidi, B. (2017). Vision-based pest detection based on SVM classification method. Computers and Electronics in Agriculture, 137, 52–58.

    Article  Google Scholar 

  • Eskandarpour, R., & Khodaei, A. (2017). Leveraging accuracy-uncertainty tradeoff in SVM to achieve highly accurate outage predictions. IEEE Transactions on Power Systems, 33, 1139–1141.

    Article  Google Scholar 

  • Gandhi, N., & Armstrong, L. J. (2016). A review of the application of data mining techniques for decision making in agriculture. In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I) (pp. 1–6).

  • Garea, A. S., Heras, D. B., & Argüello, F. (2019). Caffe CNN-based classification of hyperspectral images on GPU. The Journal of Supercomputing, 75, 1065–1077.

    Article  Google Scholar 

  • Hassanien, A. E., Gaber, T., Mokhtar, U., & Hefny, H. (2017). An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Computers and Electronics in Agriculture, 136, 86–96.

    Article  Google Scholar 

  • Iniyan, S., Jebakumar, R., Mangalraj, P., Mohit, M., & Nanda, A. (2020). Plant disease identification and detection using support vector machines and artificial neural networks. In Artificial Intelligence and Evolutionary Computations in Engineering Systems (pp. 15–27). Springer.

  • James, P., Thomas, J., & Alex, N. (2015). A survey on soft biometrics and their application in person recognition at a distance. In 2015 International Conference on Soft-Computing and Networks Security (ICSNS) (pp. 1–5). IEEE.

  • Jiao, C., Heitzler, M., & Hurni, L. (2021). A survey of road feature extraction methods from raster maps. Transactions in GIS, 25, 2734–2763.

    Article  Google Scholar 

  • Joalland, S., Screpanti, C., Liebisch, F., Varella, H. V., Gaume, A., & Walter, A. (2017). Comparison of visible imaging, thermography and spectrometry methods to evaluate the effect of Heterodera schachtii inoculation on sugar beets. Plant Methods, 13, 1–14.

    Article  Google Scholar 

  • Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A. D., & Ortiz-Barredo, A. (2017). Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture, 138, 200–209.

    Article  Google Scholar 

  • Joppa, L. N. (2017). The case for technology investments in the environment.

  • Joshi, A. A., & Jadhav, B. D. (2016). Monitoring and controlling rice diseases using image processing techniques. In 2016 International Conference on Computing, Analytics and Security Trends (CAST) (pp. 471–476).

  • Kaundal, R., Kapoor, A. S., & Raghava, G. P. (2006). Machine learning techniques in disease forecasting: A case study on rice blast prediction. BMC Bioinformatics, 7, 1–16.

    Article  Google Scholar 

  • Khirade, S. D., & Patil, A. B. (2015). Plant disease detection using image processing. In 2015 International Conference on Computing Communication Control and Automation (pp. 768–771).

  • Kim, W.-S., Lee, D.-H., & Kim, Y.-J. (2020). Machine vision-based automatic disease symptom detection of onion downy mildew. Computers and Electronics in Agriculture, 168, 105099.

    Article  Google Scholar 

  • Kirk, W., & Wharton, P. (2012). Brown leaf spot. Michigan State University Extension Bull. E, 3182.

  • Kumar, M. (2016). Impact of climate change on crop yield and role of model for achieving food security. Environmental Monitoring and Assessment, 188, 1–14.

    Article  Google Scholar 

  • Kumar, G. R., Nagamani, K., & Babu, G. A. (2020). A framework of dimensionality reduction utilizing PCA for neural network prediction. In Advances in Data Science and Management (pp. 173–180). Bhubaneswar: Springer.

  • Kurita, T. (2019). Principal component analysis (PCA). Computer Vision: A Reference Guide, (pp. 1–4).

  • Li, D., Shi, G., Kong, W., Wang, S., & Chen, Y. (2020). A leaf segmentation and phenotypic feature extraction framework for multiview stereo plant point clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2321–2336.

    Article  Google Scholar 

  • Li, Y., Nie, J., & Chao, X. (2020). Do we really need deep CNN for plant diseases identification? Computers and Electronics in Agriculture, 178, 105803.

    Article  Google Scholar 

  • Liu, W., & Wang, G.-L. (2016). Plant innate immunity in rice: A defense against pathogen infection. National Science Review, 3, 295–308.

    Article  CAS  Google Scholar 

  • Liu, X., Deng, Z., & Yang, Y. (2019). Recent progress in semantic image segmentation. Artificial Intelligence Review, 52, 1089–1106.

    Article  Google Scholar 

  • Lopes, L. A., Machado, V. P., Rabêlo, R. A., Fernandes, R. A., & Lima, B. V. (2016). Automatic labelling of clusters of discrete and continuous data with supervised machine learning. Knowledge-Based Systems, 106, 231–241.

    Article  Google Scholar 

  • Lu, J., Tan, L., & Jiang, H. (2021). Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture, 11, 707.

    Article  Google Scholar 

  • Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., & Fritschi, F. B. (2020). Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237, 111599.

    Article  Google Scholar 

  • Mhathesh, T., Andrew, J., Martin Sagayam, K., & Henesey, L. (2021). A 3D convolutional neural network for bacterial image classification. In Intelligence in big data technologies–beyond the hype (pp. 419–431). Springer.

  • Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., et al. (2015). Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, 35, 1–25.

    Article  Google Scholar 

  • Mikołajczyk, A., & Grochowski, M. (2018). Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW) (pp. 117–122). IEEE.

  • Mustafa, W. A., & Kader, M. M. M. A. (2018). Binarization of document images: A comprehensive review. In Journal of Physics: Conference Series (p. 012023). IOP Publishing volume 1019.

  • Mustafa, W. A., Khairunizam, W., Ibrahim, Z., Shahriman, A., & Razlan, Z. M. (2018). A review of different segmentation approach on non uniform images. In 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) (pp. 1–6). IEEE.

  • Nagaraju, M., Chawla, P., et al. (2020). Systematic review of deep learning techniques in plant disease detection. International Journal of System Assurance Engineering and Management, 11, 547–560.

    Google Scholar 

  • Nettleton, D. F., Katsantonis, D., Kalaitzidis, A., Sarafijanovic-Djukic, N., Puigdollers, P., & Confalonieri, R. (2019). Predicting rice blast disease: machine learning versus process-based models. BMC Bioinformatics, 20, 1–16.

    Article  Google Scholar 

  • Nidhis, A., Pardhu, C. N. V., Reddy, K. C., & Deepa, K. (2019). Cluster based paddy leaf disease detection, classification and diagnosis in crop health monitoring unit. In Computer Aided Intervention and Diagnostics in Clinical and Medical Images (pp. 281–291). Springer.

  • Nixon, M., & Aguado, A. (2019). Feature extraction and image processing for computer vision. Academic press.

  • Patel, N., Parida, B., Venus, V., Saha, S., & Dadhwal, V. (2012). Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data. Environmental monitoring and assessment, 184, 7153–7163.

    Article  CAS  Google Scholar 

  • Patil, J. K., & Kumar, R. (2017). Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Engineering in Agriculture, Environment and Food, 10, 69–78.

    Article  Google Scholar 

  • Pinki, F. T., Khatun, N., & Islam, S. M. M. (2017). Content based paddy leaf disease recognition and remedy prediction using support vector machine. In 2017 20th International Conference of Computer and Information Technology (ICCIT) (pp. 1–5).

  • Pradeep, A., Thomas, J., Pradeep, A., & Thomas, J. (2015). Performance assessment for students using different defuzzification techniques. International Journal for Innovative Research in Science and Technology, 2, 43–53.

    Google Scholar 

  • Prakoso, P. B., & Sari, Y. (2019). Vehicle detection using background subtraction and clustering algorithms. Telkomnika, 17.

  • Rahman, C. R., Arko, P. S., Ali, M. E., Khan, M. A. I., Wasif, A., Jani, M. R., & Kabir, M. S. (2018). Identification and recognition of rice diseases and pests using deep convolutional neural networks. CoRR, abs/1812.01043.

  • Raj, E. D., & Babu, L. D. (2017). A survey on topological properties, network models and analytical measures in detecting influential nodes in online social networks. International Journal of Web Based Communities, 13, 137–156.

    Article  Google Scholar 

  • Raj, E. D., & Babu, L. D. (2018). A firefly inspired game dissemination and QoS-based priority pricing strategy for online social network games. International Journal of Bio-Inspired Computation, 11, 202–217.

    Article  Google Scholar 

  • Raj, E. D., Nivash, J., Nirmala, M., & Babu, L. D. (2014). A scalable cloud computing deployment framework for efficient MapReduce operations using Apache YARN. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1–6). IEEE.

  • Rajab, M., Woolfson, M., & Morgan, S. (2004). Application of region-based segmentation and neural network edge detection to skin lesions. Computerized Medical Imaging and Graphics, 28, 61–68.

    Article  CAS  Google Scholar 

  • Rojas-Domínguez, A., Padierna, L. C., Valadez, J. M. C., Puga-Soberanes, H. J., & Fraire, H. J. (2017). Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis. IEEE Access, 6, 7164–7176.

    Article  Google Scholar 

  • Rosipal, R., Girolami, M., Trejo, L. J., & Cichocki, A. (2001). Kernel PCA for feature extraction and de-noising in nonlinear regression. Neural Computing & Applications, 10, 231–243.

    Article  Google Scholar 

  • Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Dehne, H.-W., & Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74, 91–99.

    Article  Google Scholar 

  • Sachar, S., & Kumar, A. (2021). Survey of feature extraction and classification techniques to identify plant through leaves. Expert Systems with Applications, 167, 114181.

    Article  Google Scholar 

  • Sandhya, V., & Hegde, N. P. (2021). Periocular segmentation using k-means clustering algorithm and masking. In Smart Computing Techniques and Applications (pp. 315–322). Springer.

  • Sengupta, S., & Das, A. K. (2017). Particle swarm optimization based incremental classifier design for rice disease prediction. Computers and Electronics in Agriculture, 140, 443–451.

    Article  Google Scholar 

  • Shah, J. P., Prajapati, H. B., & Dabhi, V. K. (2016). A survey on detection and classification of rice plant diseases. In 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) (pp. 1–8).

  • Sharma, P., Berwal, Y. P. S., & Ghai, W. (2020). Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Information Processing in Agriculture, 7, 566–574.

    Article  Google Scholar 

  • Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 1–48.

    Article  Google Scholar 

  • Shruthi, U., Nagaveni, V., & Raghavendra, B. (2019). A review on machine learning classification techniques for plant disease detection. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) (pp. 281–284). IEEE.

  • Singh, V., & Misra, A. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4, 41–49.

    Article  Google Scholar 

  • Solorio-Fernández, S., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2020). A review of unsupervised feature selection methods. Artificial Intelligence Review, 53, 907–948.

    Article  Google Scholar 

  • Sony, S., Dunphy, K., Sadhu, A., & Capretz, M. (2021). A systematic review of convolutional neural network-based structural condition assessment techniques. Engineering Structures, 226, 111347.

    Article  Google Scholar 

  • Su, T., Xu, H., & Zhou, X. (2019). Particle swarm optimization-based association rule mining in big data environment. IEEE Access, 7, 161008–161016.

    Article  Google Scholar 

  • Srdjan Sladojevic, A. A. D. C., Marko Arsenovic, & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, 11.

  • Tan, K., Lee, W. S., Gan, H., & Wang, S. (2018). Recognising blueberry fruit of different maturity using histogram oriented gradients and colour features in outdoor scenes. Biosystems Engineering, 176, 59–72.

    Article  Google Scholar 

  • Tharwat, A., Hassanien, A. E., & Elnaghi, B. E. (2017). A BA-based algorithm for parameter optimization of support vector machine. Pattern Recognition Letters, 93, 13–22. Pattern Recognition Techniques in Data Mining.

  • Thomas, J., & Raj, E. D. (2021). Effectual single image dehazing with color correction transform and dark channel prior. In International Conference on Information Processing (pp. 29–41). Springer.

  • Thomas, J., & Raj, E. D. (2022). Deep learning and multimodal artificial neural network architectures for disease diagnosis and clinical applications. Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems, (pp. 227–253).

  • Tiwari, V., Joshi, R. C., & Dutta, M. K. (2021). Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Ecological Informatics, 63, 101289.

    Article  Google Scholar 

  • Tripathi, M. (2021). Analysis of convolutional neural network based image classification techniques. Journal of Innovative Image Processing (JIIP), 3, 100–117.

    Article  Google Scholar 

  • Wang, Y. (2018). Improved OTSU and adaptive genetic algorithm for infrared image segmentation. In 2018 Chinese Control And Decision Conference (CCDC) (pp. 5644–5648). IEEE.

  • Wang, Z., Wang, K., Yang, F., Pan, S., & Han, Y. (2018). Image segmentation of overlapping leaves based on Chan-Vese model and Sobel operator. Information Processing in Agriculture, 5, 1–10.

    Article  Google Scholar 

  • Wang, W.-M., Fan, J., Jeyakumar, J. M. J., & Jia, Y. (2019). Rice false smut: An increasing threat to grain yield and quality. Protecting rice grains in the post-genomic era. London: IntechOpen, (pp. 89–108).

  • Wani, J. A., Sharma, S., Muzamil, M., Ahmed, S., Sharma, S., & Singh, S. (2021). Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: Methodologies, applications, and challenges. Archives of Computational Methods in Engineering, (pp. 1–37).

  • Wankhede, S. S., & Armstrong, L. J. (2017). Characterising the impact of drought on Jowar (Sorghum spp) crop yield using Bayesian networks. In International Conference on Intelligent Systems Design and Applications (pp. 979–987). Springer.

  • Yadav, S. S., & Jadhav, S. M. (2019). Deep convolutional neural network based medical image classification for disease diagnosis. Journal of Big Data, 6, 1–18.

    Article  Google Scholar 

  • Yang, C. (2021). Plant leaf recognition by integrating shape and texture features. Pattern Recognition, 112, 107809.

    Article  Google Scholar 

  • Yang, X., & Guo, T. (2017). Machine learning in plant disease research. European Journal of BioMedical Research, 3, 6–9.

    Article  Google Scholar 

  • Zaremba, W., Sutskever, I., & Vinyals, O. (2014). Recurrent neural network regularization. arXiv preprint arXiv:1409.2329

  • Zeng, F., & Liu, L. (2013). Contrast enhancement of mammographic images using guided image filtering. In Chinese conference on image and graphics technologies (pp. 300–306). Springer.

  • Zhang, Z., Fu, H., Dai, H., Shen, J., Pang, Y., & Shao, L. (2019b). ET-Net: A generic edge-attention guidance network for medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 442–450). Springer.

  • Zhang, S., Wang, H., Huang, W., & You, Z. (2018). Plant diseased leaf segmentation and recognition by fusion of superpixel, k-means and PHOG. Optik, 157, 866–872.

    Article  Google Scholar 

  • Zhang, S., You, Z., & Wu, X. (2019). Plant disease leaf image segmentation based on superpixel clustering and EM algorithm. Neural Computing and Applications, 31, 1225–1232.

    Article  Google Scholar 

  • Zhu, Y., Sun, W., Cao, X., Wang, C., Wu, D., Yang, Y., & Ye, N. (2019). TA-CNN: Two-way attention models in deep convolutional neural network for plant recognition. Neurocomputing, 365, 191–200.

    Article  Google Scholar 

Download references

Funding

This work is supported by the AI for Earth Microsoft Azure Compute Grant.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology, Kottayam, Kerala, India

    Amritha Haridasan, Jeena Thomas & Ebin Deni Raj

Authors
  1. Amritha Haridasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeena Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ebin Deni Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ebin Deni Raj.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Haridasan, A., Thomas, J. & Raj, E.D. Deep learning system for paddy plant disease detection and classification. Environ Monit Assess 195, 120 (2023). https://doi.org/10.1007/s10661-022-10656-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-022-10656-x

Keywords

Search

Navigation