AN EXTENSIVE ANALYSIS OF MACHINE LEARNING METHODS FOR IDENTIFYING PLANT LEAF DISEASES
Main Article Content
Keywords
.
Abstract
India’s economy is dependent on agriculture. Maintaining strong crop yields for food, medicine, and commercial uses is essential as the world’s second-largest population. Applications based on IT are frequently utilised for disease identification. By analysing images of different plant sections, data science-based computer vision systems are incredibly effective at detecting diseases in their early phases. It takes a lot of human skill to diagnose the condition by eye inspection, which is a difficult task in and of itself. The disease diagnosis for supervised machine learning approaches for leaf images is critically reviewed in this work. The application of supervised machine learning as a general concept is described. Based on the symptoms of a disease extracted in the form of features, a disease in plants can be identified. Thus, feature extraction methods are crucial in these systems. There is extensive discussion of the Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) approaches, as well as a brief discussion of relevant recent works are presented. It offers a thorough analysis of various visual characteristics for various illnesses in different atmospheric conditions.
References
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41. WenzhuYangSileWangXiaolanZhaoJingsiZhangJiaqi Feng (2015) Greenness identification based on HSV decision tree https://www.sciencedirect.com/science/article/pii/S2214317315000347
42. Kaur S, Pandey S, Goel S (2018) Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Process 12(6):1038–1048. https:// doi. org/ 10. 1049/ iet- ipr. 2017. 0822 [30]Kaur K, Marwaha C (2017) Analysis of Diseases in Fruits using Image Processing Techniques. In: Precedings of International Conference on Trends in Electronics and Informatics ICEI 201.
43. pp 183–189
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45. Agric 136:157–165. https:// doi. org/ 10. 1016/j. compag. 2017. 03. 004
46. Asfarian A, Herdiyeni Y, Rauf A, Mutaqin KH (2013) Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum. Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: Recent Challenges in Computer, Control and
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48. Lomte, S. S., and A. P. Janwale. "Plant leaves image segmentation techniques
49. Sannakki SS, Rajpurohit VS, Nargund VB, Kulkarni P (2013) Diagnosis and Classification of Grape Leaf Diseases using Neural Networks. Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 2013, pp 1–5
50. Sharma, Sakshi, Vatsala Anand, and Swati Singh. "Classification of Diseased Potato Leaves Using Machine Learning." 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2021.
51. https://support.echoview.com/WebHelp/Windows_and_Dialog_Boxes/Dialog_Boxes/Variable_propert ies_dialog_box/Operator_pages/GLCM_Texture_Features.html
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54. Jaisakthi, S. M., P. Mirunalini, and D. Thenmozhi. "Grape leaf disease identification using machine learning techniques." 2019 International Conference on Computational Intelligence in Data Science (ICCIDS). IEEE, 2019.
55. Panigrahi, Kshyanaprava Panda, et al. "Maize leaf disease detection and classification using machine learning algorithms." Progress in Computing, Analytics and Networking. Springer, Singapore, 2020. 659669.
56. Sharma, Sakshi, Vatsala Anand, and Swati Singh. "Classification of Diseased Potato Leaves Using Machine Learning." 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2021.
57. Sabrol, H., and K. Satish. "Tomato plant disease classification in digital images using classification tree." 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. [46]Ahmed, Kawcher, et al. "Rice leaf disease detection using machine learning techniques." 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI). IEEE, 2019.
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59. Wan, Long, et al. "Hyperspectral Sensing of Plant Diseases: Principle and Methods." Agronomy 12.6 (2022): 1451.
60. D'hondt, Liesbet, et al. "Applications of flow cytometry in plant pathology for genome size determination, detection and physiological status." Molecular Plant Pathology 12.8 (2011): 815-828. [50]Hashim, I. C., et al. "Application of thermal imaging for plant disease detection." IOP Conference Series: Earth and Environmental Science. Vol. 540. No. 1. IOP Publishing, 2020.
61. Amalraj, Augustine, and Sreeraj Gopi. "Medicinal properties of Terminalia arjuna (Roxb.) Wight &Arn.: a review." Journal of traditional and complementary medicine 7.1 (2017): 65-78.
62. Klimek-Szczykutowicz, Marta, Agnieszka Szopa, and Halina Ekiert. "Citrus limon (Lemon) phenomenon—a review of the chemistry, pharmacological properties, applications in the modern pharmaceutical, food, and cosmetics industries, and biotechnological studies." Plants 9.1 (2020): 119.
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Apr 30, 2022
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How to Cite
Deepak Kumar Awasthi, Dr. Arvind Kumar Shukla, & Ashwani Gupta. (2022). AN EXTENSIVE ANALYSIS OF MACHINE LEARNING METHODS FOR IDENTIFYING PLANT LEAF DISEASES. Journal of Population Therapeutics and Clinical Pharmacology, 29(02), 465-482. https://doi.org/10.53555/jptcp.v29i02.7165
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Author Biographies
Deepak Kumar Awasthi
School of Computer Science and Application, IFTM University, Moradabad, Uttar Pradesh, India
Dr. Arvind Kumar Shukla
School of Computer Science and Application, IFTM University, Moradabad, Uttar Pradesh, India
Ashwani Gupta
Assistant Professor, Department of CSIT, MJP Rohilkhand University, Bareilly, Uttar Pradesh -243006, India
How to Cite
Deepak Kumar Awasthi, Dr. Arvind Kumar Shukla, & Ashwani Gupta. (2022). AN EXTENSIVE ANALYSIS OF MACHINE LEARNING METHODS FOR IDENTIFYING PLANT LEAF DISEASES. Journal of Population Therapeutics and Clinical Pharmacology, 29(02), 465-482. https://doi.org/10.53555/jptcp.v29i02.7165