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A survey of detecting leaf diseases using machine learning and deep learning in various crops
IAES International Journal of Artificial Intelligence
ISSN 2252-8938, 2089-4872
Terbit 2024-09-01
Penerbit Institute of Advanced Engineering and Science
Bahasa eng
Abstrak
For agricultural productivity and food security to be guaranteed, early detection and treatment of illnesses are crucial. Machine learning (ML) and deep learning (DL) approaches can be used to precisely and successfully identify plant leaf diseases. A heterogeneous dataset comprising photos of both healthy and diseased leaves such as bacterial blights, fungal infections, and viral manifestations provides the foundation for the model building and training. Accuracy, precision, recall, and F1-score are the measures used to assess the model's performance. ML techniques are helpful in the identification and extraction of pertinent information from plant leaf pictures, whereas DL techniques in general, and convolutional neural networks (CNN), in particular, are remarkable at learning complex hierarchical representations. Therefore, DL architectures like CNN are utilized in conjunction with ML approaches like support vector machines (SVM), decision trees, and random forests to extract complicated patterns and attributes from leaf pictures. This research provides an extensive analysis of the performance and application of DL and ML approaches recently applied to the early identification of leaf diseases in different crops.
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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<dc:title xml:lang="en-US">A survey of detecting leaf diseases using machine learning and deep learning in various crops</dc:title>
<dc:creator>Thangamuthu, Thilagraj</dc:creator>
<dc:creator>Kareem, Abdul</dc:creator>
<dc:creator>Kumara, Varuna</dc:creator>
<dc:creator>Udesh Naik, Utkrishna</dc:creator>
<dc:creator>Poojary, Sanjana</dc:creator>
<dc:creator>R, Bharath</dc:creator>
<dc:subject xml:lang="en-US">machine learning, deep learning</dc:subject>
<dc:subject xml:lang="en-US">Agricultural productivity; Convolutional neural networks; Deep learning; Machine learning; Plant leaf disease</dc:subject>
<dc:description xml:lang="en-US">For agricultural productivity and food security to be guaranteed, early detection and treatment of illnesses are crucial. Machine learning (ML) and deep learning (DL) approaches can be used to precisely and successfully identify plant leaf diseases. A heterogeneous dataset comprising photos of both healthy and diseased leaves such as bacterial blights, fungal infections, and viral manifestations provides the foundation for the model building and training. Accuracy, precision, recall, and F1-score are the measures used to assess the model's performance. ML techniques are helpful in the identification and extraction of pertinent information from plant leaf pictures, whereas DL techniques in general, and convolutional neural networks (CNN), in particular, are remarkable at learning complex hierarchical representations. Therefore, DL architectures like CNN are utilized in conjunction with ML approaches like support vector machines (SVM), decision trees, and random forests to extract complicated patterns and attributes from leaf pictures. This research provides an extensive analysis of the performance and application of DL and ML approaches recently applied to the early identification of leaf diseases in different crops.</dc:description>
<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
<dc:contributor xml:lang="en-US"></dc:contributor>
<dc:date>2024-09-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24961</dc:identifier>
<dc:identifier>10.11591/ijai.v13.i3.pp2498-2505</dc:identifier>
<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024; 2498-2505</dc:source>
<dc:source>2252-8938</dc:source>
<dc:source>2089-4872</dc:source>
<dc:source>10.11591/ijai.v13.i3</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24961/14054</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
Sitasi dan pengindeksan
Jumlah menurut tiap indeks pada 13 Nov 2026; setiap indeks menghitung sitasi dengan cara berbeda.
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Sitasi artikel ini
APA 7
Thangamuthu, T., Kareem, A., Kumara, V., Udesh Naik, U., Poojary, S., & R, B. (2024). A survey of detecting leaf diseases using machine learning and deep learning in various crops. IAES International Journal of Artificial Intelligence, 13(3), 2498–2505. https://doi.org/10.11591/ijai.v13.i3.pp2498-2505
Salin
IEEE
T. Thangamuthu, A. Kareem, V. Kumara, U. Udesh Naik, S. Poojary, and B. R, “A survey of detecting leaf diseases using machine learning and deep learning in various crops,” IAES International Journal of Artificial Intelligence, vol. 13, no. 3, pp. 2498–2505, 2024, doi: 10.11591/ijai.v13.i3.pp2498-2505.
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