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Vol. 30 No. 58 (2021)
October-December 2021 (Continuous Publication)

Papers

Interpretability in the Field of Plant Disease Detection: A Review

https://doi.org/10.19053/01211129.v30.n58.2021.13495

Published 2021-11-27

Daniel-David Leal-Lara
Universidad Distrital “Francisco José de Caldas”

Julio Barón-Velandia
Universidad Distrital “Francisco José de Caldas”

Camilo-Enrique Rocha-Calderón
Universidad Distrital “Francisco José de Caldas”

Abstract

The early detection of diseases in plants through artificial intelligence techniques has been a very important technological advance for agriculture since, through machine learning and optimization algorithms, it has been possible to increase the yield of various crops in several countries around the world. Different researchers have focused their efforts on developing models that allow supporting the task of detecting diseases in plants as a solution to the traditional techniques used by farmers. In this systematic literature review, an analysis of the most relevant articles is presented, in which image processing techniques and machine learning were used to detect diseases by means of images of the leaves of different crops. In turn, an analysis of the interpretability and precision of these methods is carried out, considering each phase of the image processing, segmentation, feature extraction and learning processes of each model. In this way, there is evidence of a void in the field of interpretability since the authors have focused mainly on obtaining good results in their models, beyond providing the user with a clear explanation of the characteristics of the model.

Keywords

Machine Learning, Classification, Early detection of diseases, Interpretability, Image processing

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Author Biography

Daniel-David Leal-Lara

Roles: Conceptualization, Research, Content - Curation, Methodology, Writing - Original draft, Writing – Review & editing.

Julio Barón-Velandia

Roles: Conceptualization, Methodology, Writing - Original draft, Writing – Review & editing.

Camilo-Enrique Rocha-Calderón

Roles: Validation, Writing – Review & editing.

References

  1. R. Yadav, Y. Kumar, S. Nagpal, “Plant Leaf Disease Detection and Classification Using Particle Swarm Optimization,” in First International Conference in Machine Learning for Networking, 2019, pp. 294–306. https://doi.org/10.1007/978-3-030-19945-6_21 DOI: https://doi.org/10.1007/978-3-030-19945-6_21
  2. J. Trivedi, Y. Shamnani, R. Gajjar, “Plant Leaf Disease Detection Using Machine Learning,” Communications in Computer and Information Science, vol. 1214, pp. 267–276, 2020. https://doi.org/10.1007/978-981-15-7219-7_23 DOI: https://doi.org/10.1007/978-981-15-7219-7_23
  3. X. Deng, Y. Lan, T. Hong, J. Chen, “Citrus greening detection using visible spectrum imaging and C-SVC,” Computers and Electronics in Agriculture, vol. 130, pp. 177–183, 2016. https://doi.org/10.1016/j.compag.2016.09.005 DOI: https://doi.org/10.1016/j.compag.2016.09.005
  4. E. Omrani, B. Khoshnevisan, S. Shamshirband, H. Saboohi, N. B. Anuar, M. H. N. M. Nasir, “Potential of radial basis function-based support vector regression for apple disease detection,” Measurement, vol. 55, pp. 512–519, 2014. https://doi.org/10.1016/j.measurement.2014.05.033 DOI: https://doi.org/10.1016/j.measurement.2014.05.033
  5. J. Shin, Y. K. Chang, B. Heung, T. Nguyen-Quang, G. W. Price, A. Al-Mallahi, “Effect of directional augmentation using supervised machine learning technologies: A case study of strawberry powdery mildew detection,” Biosystems Engineering, vol. 194, pp. 49–60, 2020. https://doi.org/10.1016/j.biosystemseng.2020.03.016 DOI: https://doi.org/10.1016/j.biosystemseng.2020.03.016
  6. J. F. I. Nturambirwe, U. L. Opara, “Machine learning applications to non-destructive defect detection in horticultural products,” Biosystems Engineering, vol. 189, pp. 60–83, 2019. https://doi.org/10.1016/j.biosystemseng.2019.11.011 DOI: https://doi.org/10.1016/j.biosystemseng.2019.11.011
  7. P. Sharma, Y. P. S. Berwal, W. Ghai, “Performance analysis of deep learning CNN models for disease detection in plants using image segmentation,” Information Processing in Agriculture, vol. 7, no. 4, pp. 566–574, 2020. https://doi.org/10.1016/j.inpa.2019.11.001
  8. A. Cruz, Y. Ampatzidis, R. Pierro, A. Materazzi, A. Panattoni, L. De-Bellis, A. Luvisi, “Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence,” Computers and Electronics in Agriculture, vol. 157, pp. 63–76, 2019. https://doi.org/10.1016/j.compag.2018.12.028 DOI: https://doi.org/10.1016/j.compag.2018.12.028
  9. J. G. A. Barbedo, L. V. Koenigkan, T. T. Santos, “Identifying multiple plant diseases using digital image processing,” Biosystems Engineering, vol. 147, pp. 104–116, 2016. https://doi.org/10.1016/j.biosystemseng.2016.03.012 DOI: https://doi.org/10.1016/j.biosystemseng.2016.03.012
  10. G. Yang, Y. He, Y. Yang, B. Xu, “Fine-Grained Image Classification for Crop Disease Based on Attention Mechanism,” Frontiers in Plant Science, vol. 11, pp. 1–15, 2020. https://doi.org/10.3389/fpls.2020.600854 DOI: https://doi.org/10.3389/fpls.2020.600854
  11. S. Shrivastava, S. K. Singh, D. S. Hooda, “Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation,” Multimedia Tools and Applications, vol. 74, no. 24, pp. 11467–11484, 2015. https://doi.org/10.1007/s11042-014-2239-0 DOI: https://doi.org/10.1007/s11042-014-2239-0
  12. M. Massaro, K. Handley, C. Bagnoli, J. Dumay, “Knowledge management in small and medium enterprises: a structured literature review,” Journal of Knowledge Management, vol. 20, no. 2, pp. 258–291, 2016. https://doi.org/10.1108/JKM-08-2015-0320 DOI: https://doi.org/10.1108/JKM-08-2015-0320
  13. V. Singh, 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, 2017. https://doi.org/10.1016/j.inpa.2016.10.005
  14. J. Siebring, J. Valente, M. H. D. Franceschini, J. Kamp, L. Kooistra, “Object-based image analysis applied to low altitude aerial imagery for potato plant trait retrieval and pathogen detection,” Sensors (Switzerland), vol. 19, no. 24, 2019. https://doi.org/10.3390/s19245477 DOI: https://doi.org/10.3390/s19245477
  15. M. Kerkech, A. Hafiane, R. Canals, “Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach,” Computers and Electronics in Agriculture, vol. 174, p. 105446, 2020. https://doi.org/10.1016/j.compag.2020.105446 DOI: https://doi.org/10.1016/j.compag.2020.105446
  16. H. Ali, M. I. Lali, M. Z. Nawaz, M. Sharif, 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, 2017. https://doi.org/10.1016/j.compag.2017.04.008 DOI: https://doi.org/10.1016/j.compag.2017.04.008
  17. D. Moshou, X.-E. Pantazi, D. Kateris, I. Gravalos, “Water stress detection based on optical multisensor fusion with a least squares support vector machine classifier,” Biosystems Engineering, vol. 117, pp. 15–22, 2014. https://doi.org/10.1016/j.biosystemseng.2013.07.008 DOI: https://doi.org/10.1016/j.biosystemseng.2013.07.008
  18. M. Zhang, Q. Meng, “Automatic citrus canker detection from leaf images captured in field,” Pattern Recognition Letters, vol. 32, no. 15, pp. 2036–2046, 2011. https://doi.org/10.1016/j.patrec.2011.08.003 DOI: https://doi.org/10.1016/j.patrec.2011.08.003
  19. S. S. Chouhan, U. P. Singh, U. Sharma, S. Jain, “Leaf disease segmentation and classification of Jatropha Curcas L. and Pongamia Pinnata L. biofuel plants using computer vision based approaches,” Journal of the International Measurement Confederation, vol. 171, e108796, 2021. https://doi.org/10.1016/j.measurement.2020.108796 DOI: https://doi.org/10.1016/j.measurement.2020.108796
  20. S. Hernández, J. L. López, “Uncertainty quantification for plant disease detection using Bayesian deep learning,” Applied Soft Computing, vol. 96, e106597, 2020. https://doi.org/10.1016/j.asoc.2020.106597 DOI: https://doi.org/10.1016/j.asoc.2020.106597
  21. P. Wspanialy, M. Moussa, “A detection and severity estimation system for generic diseases of tomato greenhouse plants,” Computers and Electronics in Agriculture, vol. 178, p. 105701, 2020. https://doi.org/10.1016/j.compag.2020.105701 DOI: https://doi.org/10.1016/j.compag.2020.105701
  22. M. Sharif, M. A. Khan, Z. Iqbal, M. F. Azam, M. I. U. Lali, 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, 2018. https://doi.org/10.1016/j.compag.2018.04.023 DOI: https://doi.org/10.1016/j.compag.2018.04.023
  23. Y. Xiong, L. Liang, L. Wang, J. She, M. Wu, “Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset,” Computers and Electronics in Agriculture, vol. 177, no. July, p. 105712, 2020. https://doi.org/10.1016/j.compag.2020.105712 DOI: https://doi.org/10.1016/j.compag.2020.105712
  24. S. Khan, M. Narvekar, “Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment,” Journal of King Saud University - Computer and Information Sciences, In-press, 2020. https://doi.org/10.1016/j.jksuci.2020.09.006 DOI: https://doi.org/10.1016/j.jksuci.2020.09.006
  25. G. Sambasivam, G. D. Opiyo, “A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks,” Egyptian Informatics Journal, vol. 22, no. 1, pp. 27–34, 2020. https://doi.org/10.1016/j.eij.2020.02.007 DOI: https://doi.org/10.1016/j.eij.2020.02.007
  26. S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Computational Intelligence and Neuroscience, vol. 2016, e 3289801, 2016. https://doi.org/10.1155/2016/3289801 DOI: https://doi.org/10.1155/2016/3289801
  27. M. Gomez Selvaraj, A. Vergara, F. Montenegro, H. Alonso-Ruiz, N. Safari, D. Raymaekers, W. Ocimati, J. Ntamwira, L. Tits, A. Bonaventure Omondi, G. Blomme, “Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 169, pp. 110–124, 2020. https://doi.org/10.1016/j.isprsjprs.2020.08.025 DOI: https://doi.org/10.1016/j.isprsjprs.2020.08.025
  28. J. Behmann, J. Steinrücken, L. Plümer, “Detection of early plant stress responses in hyperspectral images,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 93, pp. 98–111, 2014. https://doi.org/10.1016/j.isprsjprs.2014.03.016
  29. J. da Rocha Miranda, M. de Carvalho Alves, E. A. Pozza, H. Santos Neto, “Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery,” International Journal of Applied Earth Observation and Geoinformation, vol. 85, e101983, 2020. https://doi.org/10.1016/j.jag.2019.101983 DOI: https://doi.org/10.1016/j.jag.2019.101983
  30. H. Santoso, H. Tani, X. Wang, A. E. Prasetyo, R. Sonobe, “Classifying the severity of basal stem rot disease in oil palm plantations using WorldView-3 imagery and machine learning algorithms,” International Journal of Remote Sensing, vol. 40, no. 19, pp. 7624–7646, 2019. https://doi.org/10.1080/01431161.2018.1541368 DOI: https://doi.org/10.1080/01431161.2018.1541368
  31. Q. Gu, L. Sheng, T. Zhang, Y. Lu, Z. Zhang, K. Zheng, H. Hu, H. Zhou, “Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms,” Computers and Electronics in Agriculture, vol. 167, e105066, 2019. https://doi.org/10.1016/j.compag.2019.105066 DOI: https://doi.org/10.1016/j.compag.2019.105066
  32. T. Poblete, C. Camino, P. S. A. Beck, A. Hornero, T. Kattenborn, M. Saponari, D. Boscia, J. A. Navas-Cortes, P. J. Zarco-Tejada, “Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 162, pp. 27–40, 2020. https://doi.org/10.1016/j.isprsjprs.2020.02.010 DOI: https://doi.org/10.1016/j.isprsjprs.2020.02.010
  33. J. Xia, H. X. Cao, Y. W. Yang, W. X. Zhang, Q. Wan, L. Xu, D. K. Ge, W. Y, Zhang, Y. Q. Ke, B. Huang, “Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.),” Computers and Electronics in Agriculture, vol. 159, pp. 59–68, 2019. https://doi.org/10.1016/j.compag.2019.02.022
  34. M. Kerkech, A. Hafiane, R. Canals, “VddNet: Vine disease detection network based on multispectral images and depth map,” Remote Sensing, vol. 12, no. 20, pp. 1–18, 2020. https://doi.org/10.3390/rs12203305 DOI: https://doi.org/10.3390/rs12203305
  35. M. S. Mustafa, Z. Husin, W. K. Tan, M. F. Mavi, R. S. M. Farook, “Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection,” Neural Computing and Applications, vol. 32, no. 15, pp. 11419–11441, 2020. https://doi.org/10.1007/s00521-019-04634-7 DOI: https://doi.org/10.1007/s00521-019-04634-7
  36. S. Zhang, Z. You, X. Wu, “Plant disease leaf image segmentation based on superpixel clustering and EM algorithm,” Neural Computing and Applications, vol. 31, pp. 1225–1232, 2019. https://doi.org/10.1007/s00521-017-3067-8 DOI: https://doi.org/10.1007/s00521-017-3067-8
  37. F. Wang, R. Wang, C. Xie, P. Yang, L. Liu, “Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition,” Computers and Electronics in Agriculture, vol. 169, e105222, 2020. https://doi.org/10.1016/j.compag.2020.105222 DOI: https://doi.org/10.1016/j.compag.2020.105222
  38. W. S. Kim, D. H. Lee, Y. J. Kim, “Machine vision-based automatic disease symptom detection of onion downy mildew,” Computers and Electronics in Agriculture, vol. 168, e105099, 2020. https://doi.org/10.1016/j.compag.2019.105099 DOI: https://doi.org/10.1016/j.compag.2019.105099
  39. P. Boissard, V. Martin, S. Moisan, “A cognitive vision approach to early pest detection in greenhouse crops,” Computers and Electronics in Agriculture, vol. 62, no. 2, pp. 81–93, 2008. https://doi.org/10.1016/j.compag.2007.11.009 DOI: https://doi.org/10.1016/j.compag.2007.11.009
  40. J. I. Arribas, G. V. Sánchez-Ferrero, G. Ruiz-Ruiz, J. Gómez-Gil, “Leaf classification in sunflower crops by computer vision and neural networks,” Computers and Electronics in Agriculture, vol. 78, no. 1, pp. 9–18, 2011. https://doi.org/10.1016/j.compag.2011.05.007 DOI: https://doi.org/10.1016/j.compag.2011.05.007
  41. M. M. Ozguven, K. Adem, “Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms,” Physica A: Statistical Mechanics and its Applications, vol. 535, e122537, 2019. https://doi.org/10.1016/j.physa.2019.122537 DOI: https://doi.org/10.1016/j.physa.2019.122537
  42. S. E. A. Raza, G. Prince, J. P. Clarkson, N. M. Rajpoot, “Automatic detection of diseased tomato plants using thermal and stereo visible light images,” PLoS One, vol. 10, no. 4, pp. 1–20, 2015. https://doi.org/10.1371/journal.pone.0123262 DOI: https://doi.org/10.1371/journal.pone.0123262
  43. M. S. Mohd Asaari, Mohd Asaari, P. Mishra, S. Mertens, S. Dhondt, D. Inzé, N. Wuyts, P. Scheunders, “Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 138, pp. 121–138, 2018. https://doi.org/10.1016/j.isprsjprs.2018.02.003 DOI: https://doi.org/10.1016/j.isprsjprs.2018.02.003
  44. R. Karthik, M. Hariharan, S. Anand, P. Mathikshara, A. Johnson, R. Menaka, “Attention embedded residual CNN for disease detection in tomato leaves,” Applied Soft Computing Journal, vol. 86, e105933, 2019. https://doi.org/10.1016/j.asoc.2019.105933 DOI: https://doi.org/10.1016/j.asoc.2019.105933
  45. H. Zhu, B. Chu, C. Zhang, F. Liu, L. Jiang, Y. He, “Hyperspectral Imaging for Presymptomatic Detection of Tobacco Disease with Successive Projections Algorithm and Machine-learning Classifiers,” Scientific Reports, vol. 7, no. 1, pp. 1–12, 2017. https://doi.org/10.1038/s41598-017-04501-2 DOI: https://doi.org/10.1038/s41598-017-04501-2
  46. T. Kasinathan, D. Singaraju, S. R. Uyyala, “Insect classification and detection in field crops using modern machine learning techniques,” Information Processing in Agriculture, vol. 8, no. 3, pp. 446-457, 2020. https://doi.org/10.1016/j.inpa.2020.09.006 DOI: https://doi.org/10.1016/j.inpa.2020.09.006
  47. R. Van De Vijver, K. Mertens, K. Heungens, B. Somers, D. Nuyttens, I. Borra-Serrano, P. Lootens, I. Roldán-Ruiz, J. Vangeyte, W. Saeys, “In-field detection of Alternaria solani in potato crops using hyperspectral imaging,” Computers and Electronics in Agriculture, vol. 168, e105106, 2019. https://doi.org/10.1016/j.compag.2019.105106 DOI: https://doi.org/10.1016/j.compag.2019.105106
  48. P. Sharma, Y. P. S. Berwal, W. Ghai, “Performance analysis of deep learning CNN models for disease detection in plants using image segmentation,” Information Processing in Agriculture, vol. 7, no. 4, pp. 566–574, 2020. https://doi.org/10.1016/j.inpa.2019.11.001 DOI: https://doi.org/10.1016/j.inpa.2019.11.001
  49. D. S. Tan, , R. Leong, A. Laguna, C. Ngo, Angelyn Lao, D. Amalin, D. Alvindia, “AuToDiDAC: Automated Tool for Disease Detection and Assessment for Cacao Black Pod Rot,” Crop Protection, vol. 103, pp. 98–102, 2018. https://doi.org/10.1016/j.cropro.2017.09.017 DOI: https://doi.org/10.1016/j.cropro.2017.09.017
  50. J. A. Xia, H. X. Cao, Y. W. Yang, W. X. Zhang, Q. Wan, L- Xu, D. K. Ge, W. Y. Zhang, Y. Q. Ke, B. Huang, “Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.),” Computers and Electronics in Agriculture, vol. 159, pp. 59–68, 2019. https://doi.org/10.1016/j.compag.2019.02.022 DOI: https://doi.org/10.1016/j.compag.2019.02.022
  51. A. Alsuwaidi, B. Grieve, H. Yin, “Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote, vol. 11, no. 4, pp. 1041–1055, 2018. https://doi.org/10.1109/JSTARS.2017.2788426 DOI: https://doi.org/10.1109/JSTARS.2017.2788426
  52. J. Behmann, J. Steinrücken, L. Plümer, “Detection of early plant stress responses in hyperspectral images,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 93, pp. 98–111, 2014. https://doi.org/10.1016/j.isprsjprs.2014.03.016 DOI: https://doi.org/10.1016/j.isprsjprs.2014.03.016
  53. V. Singh, 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, 2017. https://doi.org/10.1016/j.inpa.2016.10.005 DOI: https://doi.org/10.1016/j.inpa.2016.10.005

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