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Title: :  Leaf Disease Detection
PaperId: :  23884
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 3 2024
DUI:    16.0415/IJARIIE-23884
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Diya Deepak ShettyD.Y.Patil College of Engineering and Technology
Nikita Vijay AwatiD.Y.Patil College of Engineering and Technology
Abhilasha A. PatilD.Y.Patil College of Engineering and Technology
Shreya K. PatilD.Y.Patil College of Engineering and Technology
Sakshi S. KaragjarD.Y.Patil College of Engineering and Technology
Priyanka V. KhopkarD.Y.Patil College of Engineering and Technology

Abstract

Computer Science and Engineering
Leaf Disease Detection, Machine Learning, Convolutional Neural Network (CNN)
Detecting leaf diseases in plants is imperative for maintaining agricultural productivity and ensuring food security globally. Traditional methods face challenges in early detection, leading to significant yield losses. However, convolutional neural network (CNN) algorithms have emerged as potent tools for image-based disease detection, revolutionizing agricultural practices. This paper presents a comprehensive review of existing research on leaf disease detection techniques employing CNN algorithms. The review begins by discussing the limitations of traditional methods and underscores the advantages of CNNs in early disease detection. It explores various CNN architectures and methodologies utilized in leaf disease detection systems, encompassing data preprocessing, feature extraction, and classification stages. CNNs offer robustness in learning intricate patterns from leaf images, facilitating accurate disease diagnosis. Furthermore, the paper delves into performance metrics commonly employed to assess the effectiveness of CNN-based techniques. Evaluation metrics play a crucial role in benchmarking the performance of CNN models and guiding further research endeavors. Moreover, the review identifies key challenges in CNN-based leaf disease detection, including dataset scarcity, class imbalance, and model interpretability. Addressing these challenges necessitates innovative approaches and collaborations between researchers and agricultural stakeholders. Finally, the paper outlines potential avenues for future research and improvement in CNN-based leaf disease detection. These include the development of transfer learning techniques, domain adaptation strategies, and the integration of multi-modal data sources for enhanced disease diagnosis. In summary, this review offers valuable insights into the current state-of-the-art in leaf disease detection using CNN algorithms. By synthesizing existing research, it provides researchers and practitioners with a roadmap for advancing agricultural research and fostering sustainable crop management practices.

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IJARIIE Diya Deepak Shetty, Nikita Vijay Awati, Abhilasha A. Patil, Shreya K. Patil, Sakshi S. Karagjar, and Priyanka V. Khopkar. "Leaf Disease Detection" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 3 2024 Page 1707-1715
MLA Diya Deepak Shetty, Nikita Vijay Awati, Abhilasha A. Patil, Shreya K. Patil, Sakshi S. Karagjar, and Priyanka V. Khopkar. "Leaf Disease Detection." International Journal Of Advance Research And Innovative Ideas In Education 10.3(2024) : 1707-1715.
APA Diya Deepak Shetty, Nikita Vijay Awati, Abhilasha A. Patil, Shreya K. Patil, Sakshi S. Karagjar, & Priyanka V. Khopkar. (2024). Leaf Disease Detection. International Journal Of Advance Research And Innovative Ideas In Education, 10(3), 1707-1715.
Chicago Diya Deepak Shetty, Nikita Vijay Awati, Abhilasha A. Patil, Shreya K. Patil, Sakshi S. Karagjar, and Priyanka V. Khopkar. "Leaf Disease Detection." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 3 (2024) : 1707-1715.
Oxford Diya Deepak Shetty, Nikita Vijay Awati, Abhilasha A. Patil, Shreya K. Patil, Sakshi S. Karagjar, and Priyanka V. Khopkar. 'Leaf Disease Detection', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, 2024, p. 1707-1715. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Leaf_Disease_Detection_ijariie23884.pdf (Accessed : ).
Harvard Diya Deepak Shetty, Nikita Vijay Awati, Abhilasha A. Patil, Shreya K. Patil, Sakshi S. Karagjar, and Priyanka V. Khopkar. (2024) 'Leaf Disease Detection', International Journal Of Advance Research And Innovative Ideas In Education, 10(3), pp. 1707-1715IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Leaf_Disease_Detection_ijariie23884.pdf (Accessed : )
IEEE Diya Deepak Shetty, Nikita Vijay Awati, Abhilasha A. Patil, Shreya K. Patil, Sakshi S. Karagjar, and Priyanka V. Khopkar, "Leaf Disease Detection," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, pp. 1707-1715, May-Jun 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Leaf_Disease_Detection_ijariie23884.pdf [Accessed : ].
Turabian Diya Deepak Shetty, Nikita Vijay Awati, Abhilasha A. Patil, Shreya K. Patil, Sakshi S. Karagjar, and Priyanka V. Khopkar. "Leaf Disease Detection." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 3 ().
Vancouver Diya Deepak Shetty, Nikita Vijay Awati, Abhilasha A. Patil, Shreya K. Patil, Sakshi S. Karagjar, and Priyanka V. Khopkar. Leaf Disease Detection. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(3) : 1707-1715. Available from: https://ijariie.com/AdminUploadPdf/Leaf_Disease_Detection_ijariie23884.pdf
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