PLANT STEM DISEASE DETECTION USING DEEP LEARNING
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Abstract
In recent years, advancements in machine learning have revolutionized the field of plant biology and agriculture, particularly in the automated classification of plant stems and the identification of associated diseases. Among the various machine learning techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) have emerged as prominent tools for handling complex image-based classification tasks[1].
Support Vector Machines (SVMs) are widely recognized for their ability to construct optimal hyperplanes in high-dimensional feature spaces, making them effective in tasks where the separation of classes is crucial. SVMs have been successfully applied in diverse domains, including image classification, due to their robustness and ability to generalize well with appropriate kernel functions[9].
Convolutional Neural Networks (CNNs), on the other hand, have gained immense popularity in recent years, particularly in computer vision tasks. CNNs are designed to automatically learn hierarchical representations of data, especially in the context of images, by applying convolutions over input images and progressively extracting features at different spatial levels. This characteristic makes CNNs particularly well-suited for tasks such as object recognition and image classification without the need for extensive handcrafted feature engineering[6].
Support Vector Machines (SVMs) are widely recognized for their ability to construct optimal hyperplanes in high-dimensional feature spaces, making them effective in tasks where the separation of classes is crucial. SVMs have been successfully applied in diverse domains, including image classification, due to their robustness and ability to generalize well with appropriate kernel functions[9].
Convolutional Neural Networks (CNNs), on the other hand, have gained immense popularity in recent years, particularly in computer vision tasks. CNNs are designed to automatically learn hierarchical representations of data, especially in the context of images, by applying convolutions over input images and progressively extracting features at different spatial levels. This characteristic makes CNNs particularly well-suited for tasks such as object recognition and image classification without the need for extensive handcrafted feature engineering[6].
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Double-Blind Peer Review
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CC BY-NC 4.0
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Volume Volume 10, Issue 01
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2
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Research Article
Publication Date:
Feb 24 2026
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Shinde, R. (2026). Plant Stem Disease Detection Using Deep Learning. International Journal of Science, Strategic Management and Technology, Volume 10(01). https://doi.org/10.55041/ijsmt.v2i2.144
Shinde, Rohitkumar. "Plant Stem Disease Detection Using Deep Learning." International Journal of Science, Strategic Management and Technology, vol. Volume 10, no. 01, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i2.144.
Shinde, Rohitkumar. "Plant Stem Disease Detection Using Deep Learning." International Journal of Science, Strategic Management and Technology Volume 10, no. 01 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i2.144.
References
1.Jalal Uddin Md Akbar, Syafiq Fauzi Kamarulzaman, Ekramul Haque Tusher,"Plant Stem Disease Detection Using Machine Learning Approaches” in IIT - Delhi, Delhi, India 2023 14th International Conference on Computing Communication and Networking Technologies (ICT) | 979-8-3503-3509-5/23/$31.00 ©2023 IEEE | DOI: 10.1109/ICCCNT56998.2023.10307074
2.Prakanshu Srivastava, Kritika Mishra, Vibhav Awasthi, Vivek Kumar Sahu and Mr. Pawan Kumar Pal, “Plant Disease Detection Using Convolutional Neural Network” in 2021 International Journal of Advanced Research (IJAR), 10.21474/IJAR01/12346 3. Anshika Agarwal, Akash Sanghi , Gaurav Agarwal, Y. D. S. Arya, Shruti Agarwal, “Plant Disease Detection using Deep Learning and Convolutionary Neural Network” in Mathematical Statistician and Engineering Applications ISSN: 2094-0343 2326- 9865, Vol. 71 No. 4 (2022) 4. Geddam Chinni, M Chiranjeevi “Plant Disease Recognizance Using Deep Convolutional-Neural-Network”, Vol 13, Issue 04, APRIL/2022 ISSN NO:0377- 9254 5. Hasin Rehanaa, Muhammad Ibrahim, Md. Haider Alia, “Plant Disease Detection using Region-Based Convolutional Neural Network”, arXiv:2303.09063v2 [cs.CV] 12 Sep 2023, Dept. of Computer Science and Engineering, University of Dhaka, Bangladesh
3.S. Shreya, P. Likitha, G. Saicharan, Dr. Shruti Bhargava Choubey, “Plant Disease Detection Using Deep Learning” in International Journal of Creative Research Thoughts (IJCRT), 2023 IJCRT | Volume 11, Issue 5 May 2023 | ISSN: 2320-2882
4.Sumit Kumar, Veerendra Chaudhary, Ms. Supriya Khaitan Chandra, “Plant Disease Detection Using CNN” in Turkish Journal of Computer and Mathematics Education, Vol.12 No.12 (2021), 2106-2112, 23 May 2021
5.Rinu R, Manjula S H, “Plant Disease Detection and Classification using CNN” in International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277- 3878 (Online), Volume-10 Issue-3, September 2021, 100.1/ijrte.C64580910321 9. P S Ghodekar, V Yermune, A Sable, R Mandhare, “Plant Leaf Disease Detection Using Cnn”, International Research Journal of Modernization in Engineering Technology and Science (Peer-Reviewed, Open Access, Fully Refereed International Journal) Volume:05/Issue:04/April-2023 Impact Factor- 7.868 E- ISSN:2582-5208.
6.Vucha Deepa, P. Arun Kumar, “Plant Disease Detection Using Convolutional Neural Network” in © 2022 IJRTI | Volume 7, Issue 12 | ISSN: 2456-3315, IJRTI2212047 International Journal for Research Trends and Innovation
7.Kowshik B,Savitha V, Nimosh madhav M, Karpagam G, Sangeetha K, “Plant Disease Detection Using Deep Learning” in Special Issue of Second International Conference on Advancements in Research and Development (ICARD 2021), Volume 03 Issue 03S March 202, International Research Journal on Advanced Science Hub (IRJASH)
8.Jun Liu and Xuewei Wang, “Plant diseases and pests detection based on deep learning” in Shandong Provincial University Laboratory for Protected Horticulture, Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Weifang 262700, Shandong, China
9.T Vijaykanth Reddy, K Sashi Rekha, “Plant Disease Detection using Advanced Convolutional Neural Networks with Region of Interest Awareness” in: Reddy TV, Rekha KS (2022) Plant Disease Detection using Advanced Convolutional Neural Networks with Region of Interest Awareness. J Agri Sci Food Res. 13: 506.
10.Ramachandran P, Praveen Kumar P, Sathish, “Plant Diseases Detection Using Convolution Neural Network” in International Journal of Engineering Development and Research ISSN: 2321-9939 | ©IJEDR 2021 Year 2021, Volume 9.
https://iopscience.iop.org/article/10.1088/1742-6596/1820/1/012161/meta
2.Prakanshu Srivastava, Kritika Mishra, Vibhav Awasthi, Vivek Kumar Sahu and Mr. Pawan Kumar Pal, “Plant Disease Detection Using Convolutional Neural Network” in 2021 International Journal of Advanced Research (IJAR), 10.21474/IJAR01/12346 3. Anshika Agarwal, Akash Sanghi , Gaurav Agarwal, Y. D. S. Arya, Shruti Agarwal, “Plant Disease Detection using Deep Learning and Convolutionary Neural Network” in Mathematical Statistician and Engineering Applications ISSN: 2094-0343 2326- 9865, Vol. 71 No. 4 (2022) 4. Geddam Chinni, M Chiranjeevi “Plant Disease Recognizance Using Deep Convolutional-Neural-Network”, Vol 13, Issue 04, APRIL/2022 ISSN NO:0377- 9254 5. Hasin Rehanaa, Muhammad Ibrahim, Md. Haider Alia, “Plant Disease Detection using Region-Based Convolutional Neural Network”, arXiv:2303.09063v2 [cs.CV] 12 Sep 2023, Dept. of Computer Science and Engineering, University of Dhaka, Bangladesh
3.S. Shreya, P. Likitha, G. Saicharan, Dr. Shruti Bhargava Choubey, “Plant Disease Detection Using Deep Learning” in International Journal of Creative Research Thoughts (IJCRT), 2023 IJCRT | Volume 11, Issue 5 May 2023 | ISSN: 2320-2882
4.Sumit Kumar, Veerendra Chaudhary, Ms. Supriya Khaitan Chandra, “Plant Disease Detection Using CNN” in Turkish Journal of Computer and Mathematics Education, Vol.12 No.12 (2021), 2106-2112, 23 May 2021
5.Rinu R, Manjula S H, “Plant Disease Detection and Classification using CNN” in International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277- 3878 (Online), Volume-10 Issue-3, September 2021, 100.1/ijrte.C64580910321 9. P S Ghodekar, V Yermune, A Sable, R Mandhare, “Plant Leaf Disease Detection Using Cnn”, International Research Journal of Modernization in Engineering Technology and Science (Peer-Reviewed, Open Access, Fully Refereed International Journal) Volume:05/Issue:04/April-2023 Impact Factor- 7.868 E- ISSN:2582-5208.
6.Vucha Deepa, P. Arun Kumar, “Plant Disease Detection Using Convolutional Neural Network” in © 2022 IJRTI | Volume 7, Issue 12 | ISSN: 2456-3315, IJRTI2212047 International Journal for Research Trends and Innovation
7.Kowshik B,Savitha V, Nimosh madhav M, Karpagam G, Sangeetha K, “Plant Disease Detection Using Deep Learning” in Special Issue of Second International Conference on Advancements in Research and Development (ICARD 2021), Volume 03 Issue 03S March 202, International Research Journal on Advanced Science Hub (IRJASH)
8.Jun Liu and Xuewei Wang, “Plant diseases and pests detection based on deep learning” in Shandong Provincial University Laboratory for Protected Horticulture, Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Weifang 262700, Shandong, China
9.T Vijaykanth Reddy, K Sashi Rekha, “Plant Disease Detection using Advanced Convolutional Neural Networks with Region of Interest Awareness” in: Reddy TV, Rekha KS (2022) Plant Disease Detection using Advanced Convolutional Neural Networks with Region of Interest Awareness. J Agri Sci Food Res. 13: 506.
10.Ramachandran P, Praveen Kumar P, Sathish, “Plant Diseases Detection Using Convolution Neural Network” in International Journal of Engineering Development and Research ISSN: 2321-9939 | ©IJEDR 2021 Year 2021, Volume 9.
https://iopscience.iop.org/article/10.1088/1742-6596/1820/1/012161/meta
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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