• editor.aipublications@gmail.com
  • Track Your Paper
  • Contact Us
  • ISSN: 2456-8015

International Journal Of Medical, Pharmacy And Drug Research(IJMPD)

Survey on Plant Disease Detection using Deep Learning based Frameworks

Surajit Mandal

International Journal of Medical, Pharmacy and Drug Research(IJMPD), Vol-7,Issue-2, March - April 2023, Pages 27-36 , 10.22161/ijmpd.7.2.2

Download | Downloads : 1 | Total View : 150

Article Info: Received: 03 Feb 2023; Received in revised form: 04 Mar 2023; Accepted: 20 Mar 2023; Available online: 30 Mar 2023


Early identification of plant diseases is crucial as they can hinder the growth of their respective species. Although many machine learning models have been utilised for detecting and classifying plant diseases. The advent of deep Learning, a subset of machine learning, has revolutionised this field by offering greater accuracy. Therefore, deep learning has the potential to greatly enhance the accuracy of plant disease detection and classification. Recent research progress on the use of deep learning technology in the identification of crop leaf diseases is reviewed in this article. The current trends and challenges in plant leaf disease detection using advanced imaging techniques and deep learning are presented. This survey aims to provide a valuable resource for the researchers investigating the detection of plant diseases and detection of those using state of the art models for ease of saving time and cost. Additionally, the article also addresses some of the current challenges and issues in the detection process that need to be resolved.

Plant Disease Detection, Deep Learning, Survey, Convolutional Neural Network, Agriculture.

[1] J. Fletcher, F. D, and L. Je, “Healthy plants: necessary for a balanced ‘One Health’ concept.,” Veterinaria Italiana, vol. 45, no. 1, pp. 79–95, Jan. 2009.
[2] “One Health Basics | One Health | CDC.” https://www.cdc.gov/onehealth/basics/ (accessed Jan. 15, 2023).
[3] “About,” Food and Agriculture Organization of the United Nations. https://www.fao.org/plant-health-2020/about/en/ (accessed Jan. 15, 2023).
[4] S. Savary, L. Willocquet, S. J. Pethybridge, P. D. Esker, N. McRoberts, and A. Nelson, “The global burden of pathogens and pests on major food crops,” Nature Ecology and Evolution, vol. 3, no. 3, pp. 430–439, Feb. 2019, doi: 10.1038/s41559-018-0793-y.
[5] “Climate change fans spread of pests and threatens plants and crops, new FAO study.” https://www.fao.org/news/story/en/item/1402920/icode/ (accessed Jan. 15, 2023).
[6] M. A. Ebrahimi, M. H. Khoshtaghaza, S. Minaei, and B. Jamshidi, “Vision-based pest detection based on SVM classification method,” Computers and Electronics in Agriculture, vol. 137, pp. 52–58, May 2017, doi: 10.1016/j.compag.2017.03.016.
[7] A. P. J and G. GOPAL, “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network.” Mendeley Data, Apr. 18, 2019. [Online]. Available: https://data.mendeley.com/datasets/tywbtsjrjv/1 doi: 10.17632/tywbtsjrjv.1
[8] D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, and N. Batra, “PlantDoc: A Dataset for Visual Plant Disease Detection.” Association for Computing Machinery, 2020. [Online]. Available: https://doi.org/10.1145/3371158.3371196 doi:10.1145/3371158.3371196
[9] J. G. A. Barbedo et al., “Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases,” IEEE Latin America Transactions, vol. 16, no. 6, pp. 1749–1757, Aug. 2018, doi: 10.1109/tla.2018.8444395.
[10] A. Ahmad, “CD&S Dataset.” Aug. 01, 2021. [Online]. Available: https://osf.io/s6ru5/ doi:10.17605/OSF.IO/S6RU5
[11] M. A. Khan et al., “Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection,” Applied Sciences, vol. 12, no. 2, p. 593, Jan. 2022, doi: 10.3390/app12020593.
[12] S. Zhang, X. Wu, Z.-H. You, and L. Zhang, “Leaf image based cucumber disease recognition using sparse representation classification,” Computers and Electronics in Agriculture, vol. 134, pp. 135–141, Mar. 2017, doi: 10.1016/j.compag.2017.01.014.
[13] R. Mahum et al., “A novel framework for potato leaf disease detection using an efficient deep learning model,” Human and Ecological Risk Assessment, pp. 1–24, Apr. 2022, doi: 10.1080/10807039.2022.2064814.
[14] D. P. Hughes and M. Salath, “An open access repository of images on plant health to enable the development of mobile disease diagnostics,” CoRR, vol. abs/1511.08060, 2015, [Online]. Available: https://arxiv.org/abs/1511.08060
[15] J. A. Pandian, V. Kumar, O. Geman, M. Hnatiuc, M. Arif, and K. K, “Plant Disease Detection Using Deep Convolutional Neural Network,” Applied Sciences, vol. 12, no. 14, p. 6982, Jul. 2022, doi: 10.3390/app12146982.
[16] A. Ahmad, A. E. Gamal, and D. Saraswat, “Toward Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions,” IEEE Access, vol. 11, pp. 9042–9057, Jan. 2023, doi: 10.1109/access.2023.3240100.
[17] H. Ulutaş and V. Aslantaş, “Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model,” Electronics, vol. 12, no. 4, p. 827, Feb. 2023, doi: 10.3390/electronics12040827.
[18] M. Francis, K. S. M. Anbananthen, D. Chelliah, S. Kannan, S. Subbiah, and J. Krishnan, “Smart Farm-Care using a Deep Learning Model on Mobile Phones,” Emerging Science Journal, vol. 7, no. 2, pp. 480–497, Feb. 2023, doi: 10.28991/esj-2023-07-02-013.
[19] G. Hu, H. Wu, Y. Zhang, and M. Wan, “Data for: A Low Shot Learning Method for Tea Leaf’s Disease Identification.” Mendeley Data, Jun. 27, 2019. [Online]. Available: https://data.mendeley.com/datasets/dbjyfkn6jr/1 doi: 10.17632/dbjyfkn6jr.1
[20] V. Preet Kour and S. Arora, “PlantaeK: A leaf database of native plants of Jammu and Kashmir.” Mendeley Data, Oct. 29, 2019. [Online]. Available: https://data.mendeley.com/datasets/t6j2h22jpx/2 doi: 10.17632/t6j2h22jpx.2
[21] R. A. Krohling, G. J. M. Esgario, and J. A. Ventura, “BRACOL - A Brazilian Arabica Coffee Leaf images dataset to identification and quantification of coffee diseases and pests.” Mendeley Data, 2019. [Online]. Available: https://data.mendeley.com/datasets/yy2k5y8mxg/1 doi: 10.17632/yy2k5y8mxg.1
[22] J. Parraga-Alava, K. Cusme, A. Loor, and E. Santander, “RoCoLe: A robusta coffee leaf images dataset.” Mendeley Data, 2019. [Online]. Available: https://data.mendeley.com/datasets/c5yvn32dzg/2 doi: 10.17632/c5yvn32dzg.2
[23] L. G. Divyanth, A. Ahmad, and D. Saraswat, “A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery,” Smart Agricultural Technology, vol. 3, p. 100108, Aug. 2022, doi: 10.1016/j.atech.2022.100108.
[24] A. Ahmad, D. Saraswat, A. E. Gamal, and G. S. Johal, “CD&S Dataset: Handheld Imagery Dataset Acquired Under Field Conditions for Corn Disease Identification and Severity Estimation,” arXiv (Cornell University), Oct. 2021, doi: 10.48550/arxiv.2110.12084.
[25] R. N. Nandi, A. H. Palash, N. Siddique, and M. G. Zilani, “Device-friendly Guava fruit and leaf disease detection using deep learning,” arXiv (Cornell University), Sep. 2022, doi: 10.48550/arxiv.2209.12557.
[26] A. Rajbongshi, S. Sazzad, R. Shakil, B. Akter, and U. Sara, “A comprehensive guava leaves and fruits dataset for guava disease recognition,” Data in Brief, vol. 42, p. 108174, Apr. 2022, doi: 10.1016/j.dib.2022.108174.
[27] M. Long, M. Hartley, R. J. Morris, and J. H. Brown, “Classification of wheat diseases using deep learning networks with field and glasshouse images,” Plant Pathology, Nov. 2022, doi: 10.1111/ppa.13684.
[28] M. C. Long, “Wheat disease images (small dataset),” Zenodo, Jan. 2023, doi: 10.5281/zenodo.7573133.
[29] T. Wiesner-Hanks et al., “Image set for deep learning: field images of maize annotated with disease symptoms,” BMC Research Notes, vol. 11, no. 1, Jul. 2018, doi: 10.1186/s13104-018-3548-6.
[30] M. Ghosh, S. M. Obaidullah, F. Gherardini, and M. Zdimalova, “Classification of Geometric Forms in Mosaics Using Deep Neural Network,” Journal of Imaging, vol. 7, no. 8, p. 149, Aug. 2021, doi: 10.3390/jimaging7080149.
[31] M. Ghosh, H. Mukherjee, S. M. Obaidullah, and K. Roy, “STDNet: A CNN-based approach to single-/mixed-script detection,” Innovations in Systems and Software Engineering, Apr. 2021, doi: 10.1007/s11334-021-00395-6.
[32] M. Ghosh, S. B. Roy, H. Mukherjee, S. M. Obaidullah, X.-Z. Gao, and K. Roy, “Movie Title Extraction and Script Separation Using Shallow Convolution Neural Network,” IEEE Access, vol. 9, pp. 125184–125201, Sep. 2021, doi: 10.1109/access.2021.3110858.
[33] M. Ghosh, G. Baidya, H. Mukherjee, S. M. Obaidullah, and K. Roy, “A Deep Learning-Based Approach to Single/Mixed Script-Type Identification,” Lecture Notes in Networks and Systems, pp. 121–132, Nov. 2021, doi: 10.1007/978-981-16-4287-6_9.
[34] M. Ghosh, S. Chatterjee, H. Mukherjee, S. Sen, and S. M. Obaidullah, “Text/Non-text Scene Image Classification Using Deep Ensemble Network,” Advances in Intelligent Systems and Computing, pp. 561–570, Nov. 2021, doi: 10.1007/978-981-16-5207-3_47.
[35] M. Ghosh, S. B. Roy, H. Mukherjee, S. M. Obaidullah, K. C. Santosh, and K. Roy, “Understanding movie poster: transfer-deep learning approach for graphic-rich text recognition,” The Visual Computer, vol. 38, no. 5, pp. 1645–1664, Mar. 2021, doi: 10.1007/s00371-021-02094-6.
[36] S. M. Obaidullah, M. Ghosh, H. Mukherjee, K. Roy, and U. Pal, “SEN: Stack Ensemble Shallow Convolution Neural Network for Signature-based Writer Identification,” 2022 26th International Conference on Pattern Recognition (ICPR), Aug. 2022, doi: 10.1109/icpr56361.2022.9956456.
[37] V. Gnanaprakash, N. Kanthimathi, and N. Saranya, “Automatic number plate recognition using deep learning,” IOP Conference Series, vol. 1084, no. 1, p. 012027, Mar. 2021, doi: 10.1088/1757-899x/1084/1/012027.
[38] R. Dixit, R. Kushwah, and S. Pashine, “Handwritten Digit Recognition using Machine and Deep Learning Algorithms,” International Journal of Computer Applications, vol. 176, no. 42, pp. 27–33, Jul. 2020, doi: 10.5120/ijca2020920550.
[39] I. Kamran, S. Naz, M. I. Razzak, and M. Imran, “Handwriting dynamics assessment using deep neural network for early identification of Parkinson’s disease,” Future Generation Computer Systems, vol. 117, pp. 234–244, Apr. 2021, doi: 10.1016/j.future.2020.11.020.
[40] A. Lasker, M. Ghosh, S. M. Obaidullah, C. Chakraborty, T. Goncalves, and K. Roy, “Ensemble Stack Architecture for Lungs Segmentation from X-ray Images,” Springer eBooks, pp. 3–11, Jan. 2022, doi: 10.1007/978-3-031-21753-1_1.
[41] A. Lasker, M. Ghosh, S. M. Obaidullah, C. Chakraborty, and K. Roy, “LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery,” Multimedia Tools and Applications, Dec. 2022, doi: 10.1007/s11042-022-14247-3.
[42] A. Lasker, M. Ghosh, S. M. Obaidullah, C. Chakraborty, and K. Roy, “A Deep Learning-based Framework for COVID-19 Identification using Chest X-Ray Images,” River Publishers eBooks, pp. 23–46, Feb. 2023, doi: 10.1201/9781003393658-2.
[43] L. Hao, F.-Y. Dao, Z.-X. Guan, H. Yang, Y.-W. Li, and H. Lin, “Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method,” Briefings in Bioinformatics, vol. 22, no. 4, Jul. 2021, doi: 10.1093/bib/bbaa255.
[44] A. R. Khan, M. Hussain, and M. I. Malik, “Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network,” Complexity, vol. 2021, pp. 1–8, Mar. 2021, doi: 10.1155/2021/5512243.
[45] L. Li and S. Zhang, “Plant Disease Detection and Classification by Deep Learning—A Review,” IEEE Access, vol. 9, pp. 56683–56698, Apr. 2021, doi: 10.1109/access.2021.3069646.
[46] A. Ahmad, D. Saraswat, and A. E. Gamal, “A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools,” Smart Agricultural Technology, vol. 3, p. 100083, Jun. 2022, doi: 10.1016/j.atech.2022.100083.
[47] C. F. Jackulin and S. Murugavalli, “A comprehensive review on detection of plant disease using machine learning and deep learning approaches,” Measurement: Sensors, vol. 24, p. 100441, Dec. 2022, doi: 10.1016/j.measen.2022.100441.
[48] M. Loey, A. El-Sawy, and M. Afify, “Deep Learning in Plant Diseases Detection for Agricultural Crops: A Survey,” International Journal of Service Science, Management, Engineering, and Technology, vol. 11, no. 2, pp. 41–58, Apr. 2020, doi: 10.4018/ijssmet.2020040103.
[49] P. Vasavi, A. Punitha, and T. V. N. Rao, “Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review,” International Journal of Power Electronics and Drive Systems, vol. 12, no. 2, p. 2079, Apr. 2022, doi: 10.11591/ijece.v12i2.pp2079-2086.
[50] M. Saleem, J. Potgieter, and K. M. Arif, “Plant Disease Detection and Classification by Deep Learning,” Plants, vol. 8, no. 11, p. 468, Oct. 2019, doi: 10.3390/plants8110468.
[51] L. Jinzhu, L. Tan, and H. Jiang, “Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification,” Agriculture, vol. 11,