A Review on Advances in Automated Plant Disease Detection
Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.
“Agriculture in India: Information about Indian Agriculture & Its Importance,” https://www.ibef.org/industry/agriculture-india.aspx, July 28, 2021.
J. G. A. Barbedo, “Digital Image Processing Techniques for Detecting, Quantifying and Classifying Plant Diseases,” SpringerPlus, vol. 2, no. 1, 660, December 2013.
M. Sharif, M. A. Khan, Z. Iqbal, M. F. Azam, M. I. U. Lali, and M. Y. Javed, “Detection and Classification of Citrus Diseases in Agriculture Based on Optimized Weighted Segmentation and Feature Selection,” Computers and Electronics in Agriculture, vol. 150, pp. 220-234, July 2018.
M. G. Selvaraj, A.Vergara, H. Ruiz, N. Safari, S. Elayabalan, W. Ocimati, et al., “AI-Powered Banana Diseases and Pest Detection,” Plant Methods, vol. 15, no. 1, 92, December 2019.
H. Ali, M. I. Lali, M. Z. Nawaz, M. Sharif, and B. A. Saleem, “Symptom Based Automated Detection of Citrus Diseases Using Color Histogram and Textural Descriptors,” Computers and Electronics in Agriculture, vol. 138, pp. 92-104, May 2017.
M. Islam, A. Dinh, K. Wahid, and P. Bhowmik, “Detection of Potato Diseases Using Image Segmentation and Multiclass Support Vector Machine,” IEEE 30th Canadian Conference on Electrical and Computer Engineering, April 2017, pp. 1-4.
D. Hughes and M. Salathé, “An Open Access Repository of Images on Plant Health to Enable the Development of Mobile Disease Diagnostics,” https://arxiv.org/ftp/arxiv/papers/1511/1511.08060.pdf, April 12, 2016.
M. Zhang and Q. Meng, “Automatic Citrus Canker Detection from Leaf Images Captured in Field,” Pattern Recognition Letters, vol. 32, no. 15, pp. 2036-2046, November 2011.
A. E. Hassanien, T. Gaber, U. Mokhtar, and H. Hefny, “An Improved Moth Flame Optimization Algorithm Based on Rough Sets for Tomato Diseases Detection,” Computers and Electronics in Agriculture, vol. 136, pp. 86-96, April 2017.
V. Singh and A. K. Misra, “Detection of Plant Leaf Diseases Using Image Segmentation and Soft Computing Techniques,” Information Processing in Agriculture, vol. 4, no. 1, pp. 41-49, March 2017.
J. G. A. Barbedo, L. V. Koenigkan, and T. T. Santos, “Identifying Multiple Plant Diseases Using Digital Image Processing,” Biosystems Engineering, vol. 147, pp. 104-116, July 2016.
E. Omrani, B. Khoshnevisan, S. Shamshirband, H. Saboohi, N. B. Anuar, and M. H. N. M. Nasir, “Potential of Radial Basis Function-Based Support Vector Regression for Apple Disease Detection,” Measurement, vol. 55, pp. 512-519, September 2014.
S. Phadikar, J. Sil, and A. K. Das, “Rice Diseases Classification Using Feature Selection and Rule Generation Techniques,” Computers and Electronics in Agriculture, vol. 90, pp. 76-85, January 2013.
J. G. A. Barbedo, “An Automatic Method to Detect and Measure Leaf Disease Symptoms Using Digital Image Processing,” Plant Disease, vol. 98, no. 12, pp. 1709-1716, December 2014.
A. Camargo and J. S. Smith, “An Image-Processing Based Algorithm to Automatically Identify Plant Disease Visual Symptoms,” Biosystems Engineering, vol. 102, no. 1, pp. 9-21, January 2009.
A. Camargo and J. S. Smith, “Image Pattern Classification for the Identification of Disease Causing Agents in Plants,” Computers and Electronics in Agriculture, vol. 66, no. 2, pp. 121-125, May 2009.
A. Johannes, A. Picon, A. Alvarez-Gila, J. Echazarra, S. Rodriguez-Vaamonde, A. D. Navajas, et al., “Automatic Plant Disease Diagnosis Using Mobile Capture Devices, Applied on a Wheat Use Case,” Computers and Electronics in Agriculture, vol. 138, pp. 200-209, June 2017.
A. K. Mahlein, “Plant Disease Detection by Imaging Sensors—Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping,” Plant Disease, vol. 100, no. 2, pp. 241-251, February 2016.
Y. Tian and L. Zhang, “Study on the Methods of Detecting Cucumber Downy Mildew Using Hyperspectral Imaging Technology,” Physics Procedia, vol. 33, pp. 743-750, 2012.
E. Bauriegel, A. Giebel, M. Geyer, U. Schmidt, and W. B. Herppich, “Early Detection of Fusarium Infection in Wheat Using Hyper-Spectral Imaging,” Computers and Electronics in Agriculture, vol. 75, no. 2, pp. 304-312, February 2011.
J. G. Barbedo, C. S. Tibola, and J. M. Fernandes, “Detecting Fusarium Head Blight in Wheat Kernels Using Hyperspectral Imaging,” Biosystems Engineering, vol. 131, pp. 65-76, March 2015.
J. Li, X. Rao, and Y. Ying, “Detection of Common Defects on Oranges Using Hyperspectral Reflectance Imaging,” Computers and Electronics in Agriculture, vol. 78, no. 1, pp. 38-48, August 2011.
J. Huang, H. Liao, Y. Zhu, J. Sun, Q. Sun, and X. Liu, “Hyperspectral Detection of Rice Damaged by Rice Leaf Folder (Cnaphalocrocis Medinalis),” Computers and Electronics in Agriculture, vol. 82, pp. 100-107, March 2012.
J. C. Zhang, R. L. Pu, J. H. Wang, W. J. Huang, L. Yuan, and J. H. Luo, “Detecting Powdery Mildew of Winter Wheat Using Leaf Level Hyperspectral Measurements,” Computers and Electronics in Agriculture, vol. 85, pp. 13-23, July 2012.
T. Rumpf, A. K. Mahlein, U. Steiner, E. C. Oerke, H. W. Dehne, and L. Plümer, “Early Detection and Classification of Plant Diseases with Support Vector Machines Based on Hyperspectral Reflectance,” Computers and Electronics in Agriculture, vol. 74, no. 1, pp. 91-99, October 2010.
A. K. Mahlein, T. Rumpf, P. Welke, H. W. Dehne, L. Plümer, U. Steiner, et al., “Development of Spectral Indices for Detecting and Identifying Plant Diseases,” Remote Sensing of Environment, vol. 128, pp. 21-30, January 2013.
Y. Shi, W. Huang, J. Luo, L. Huang, and X. Zhou, “Detection and Discrimination of Pests and Diseases in Winter Wheat Based on Spectral Indices and Kernel Discriminant Analysis,” Computers and Electronics in Agriculture, vol. 141, pp. 171-180, September 2017.
D. Cui, Q. Zhang, M. Li, G. L. Hartman, and Y. Zhao, “Image Processing Methods for Quantitatively Detecting Soybean Rust from Multispectral Images,” Biosystems Engineering, vol. 107, no. 3, pp. 186-193, November 2010.
N. Aleixos, J. Blasco, F. Navarrón, and E. Moltó, “Multispectral Inspection of Citrus in Real-Time Using Machine Vision and Digital Signal Processors,” Computers and Electronics in Agriculture, vol. 33, no. 2, pp. 121-137, February 2002.
K. H. Dammer, B. Möller, B. Rodemann, and D. Heppner, “Detection of Head Blight (Fusarium Ssp.) in Winter Wheat by Color and Multispectral Image Analyses,” Crop Protection, vol. 30, no. 4, pp. 420-428, April 2011.
R. Oberti, M. Marchi, P. Tirelli, A. Calcante, M. Iriti, and A. N. Borghese, “Automatic Detection of Powdery Mildew on Grapevine Leaves by Image Analysis: Optimal View-Angle Range to Increase the Sensitivity,” Computers and Electronics in Agriculture, vol. 104, pp. 1-8, June 2014.
A. Kamilaris and F. X. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70-90, April 2018.
S. Coulibaly, B. Kamsu-Foguem, D. Kamissoko, and D. Traore, “Deep Neural Networks with Transfer Learning in Millet Crop Images,” Computers in Industry, vol. 108, pp. 115-120, June 2019.
S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers in Plant Science, vol. 7, 1419, September 2016.
P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, “Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks,” IEEE Access, vol. 7, pp. 59069-59080, 2019.
J. G. A. Barbedo, “Plant Disease Identification from Individual Lesions and Spots Using Deep Learning,” Biosystems Engineering, vol. 180, pp. 96-107, April 2019.
G. Geetharamani and A. Pandian, “Identification of Plant Leaf Diseases Using a Nine-Layer Deep Convolutional Neural Network,” Computers and Electrical Engineering, vol. 76, pp. 323-338, June 2019.
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. 161, pp. 272-279, June 2019.
A. Picon, A. Alvarez-Gila, M. Seitz, A. Ortiz-Barredo, J. Echazarra, and A. Johannes, “Deep Convolutional Neural Networks for Mobile Capture Device-Based Crop Disease Classification in the Wild,” Computers and Electronics in Agriculture, vol. 161, pp. 280-290, June 2019.
G. Hu, H. Wu, Y. Zhang, and M. Wan, “A Low Shot Learning Method for Tea Leaf’s Disease Identification,” Computers and Electronics in Agriculture, vol. 163, 104852, August 2019.
K. P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Computers and Electronics in Agriculture, vol. 145, pp. 311-318, February 2018.
M. M. Ghazi, B. Yanikoglu, and E. Aptoula, “Plant Identification Using Deep Neural Networks via Optimization of Transfer Learning Parameters,” Neurocomputing, vol. 235, pp. 228-235, April 2017.
Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, “Identification of Rice Diseases Using Deep Convolutional Neural Networks,” Neurocomputing, vol. 267, pp. 378-384, December 2017.
S. H. Lee, C. S. Chan, S. J. Mayo, and P. Remagnino, “How Deep Learning Extracts and Learns Leaf Features for Plant Classification,” Pattern Recognition, vol. 71, pp. 1-13, November 2017.
A. Signoroni, M. Savardi, A. Baronio, and S. Benini, “Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review,” Journal of Imaging, vol. 5, no. 5, 52, May 2019.
K. Nagasubramanian, S. Jones, A. K. Singh, S. Sarkar, A. Singh, and B. Ganapathysubramanian, “Plant Disease Identification Using Explainable 3D Deep Learning on Hyperspectral Images,” Plant Methods, vol. 15, no. 1, 98, December 2019.
G. Polder, P. M. Blok, H. A. de Villiers, J. M. van der Wolf, and J. Kamp, “Potato Virus Y Detection in Seed Potatoes Using Deep Learning on Hyperspectral Images,” Frontiers in Plant Science, vol. 10, 209, March 2019.
M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza, “A New Deep Convolutional Neural Network for Fast Hyperspectral Image Classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 145, pp. 120-147, November 2018.
Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 10, pp. 6232-6251, October 2016.
D. Wang, R. Vinson, M. Holmes, G. Seibel, A. Bechar, S. Nof, et al., “Early Detection of Tomato Spotted Wilt Virus by Hyperspectral Imaging and Outlier Removal Auxiliary Classifier Generative Adversarial Nets (OR-AC-GAN),” Scientific Reports, vol. 9, 4377, 2019.
X. Zhang, L. Han, Y. Dong, Y. Shi, W. Huang, L. Han, et al., “A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images,” Remote Sensing, vol. 11, no. 13, 1554, July 2019.
X. Jin, L. Jie, S. Wang, H. J. Qi, and S. W. Li, “Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using A Deep Neural Network in the Wild Field,” Remote Sensing, vol. 10, no. 3, 395, March 2018.
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, no. 3, pp. 354-371, September 2018.
R. Sujatha, J. M. Chatterjee, N. Z. Jhanjhi, and S. N. Brohi, “Performance of Deep Learning vs Machine Learning in Plant Leaf Disease Detection,” Microprocessors and Microsystems, vol. 80, 103615, February 2021.
L. C. Ngugi, M. Abelwahab, and M. Abo-Zahhad, “Recent Advances in Image Processing Techniques for Automated Leaf Pest and Disease Recognition—A Review,” Information Processing in Agriculture, vol. 8, no. 1, pp. 27-51, March 2021.
R. Bhagwat and Y. Dandawate, “A Framework for Crop Disease Detection Using Feature Fusion Method,” International Journal of Engineering and Technology Innovation, vol. 11, no. 3, pp. 216-228, June 2021.
J. G. A. Barbedo, L. V. Koenigkan, B. A. Halfeld-Vieira, R. V. Costa, K. L. Nechet, C. V. Godoy, 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, June 2018.
X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,” IEEE Conference on Computer Vision and Pattern Recognition, June 2018, pp. 6848-6856.
Copyright (c) 2021 Radhika Bhagwat, Yogesh Dandawate
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. Also, author can post the final, peer-reviewed manuscript version (postprint) to any repository or website.
Since Jan. 01, 2019, IJETI will publish new articles with Creative Commons Attribution Non-Commercial License, under Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License.
The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.