Coconut Leaf Disease detection using deep learning techniques

Author(s)	Md Firoz Kabir, Md Mizanur Rahman, Abdulla Al Mamun, Md Yousuf Ahmad
Country	India
Abstract	These is similar to others studies, these studies explored how various machine leaning and deep learning methods can be employed for real life problems like fruit disease detection. The conclusive evidence is the string of successes that CNNs have gotten in their workplace, which is classification of image-based tasks, including plant disease detection. For example, Deep Learning applied to an apple disease detection problem on convolutional neural networks (CNNs) reach an accuracy level over 90% as Smith et al (2018) confirm, demonstrating the success of deep learning in agribusiness. Consequently, Zhang, and Yang (2020) applied the transfer learning methods for grape disease detection in CNNs explaining the them working well in real-life scenarios. In turn the studies point up the capability of CNN to innovate in pest control and crop studies by providing a quick and precise disease identification.
Keywords	Fruit disease detection, deep learning, image processing, Pest management
Field	Engineering
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-02-07
Cite This	Coconut Leaf Disease detection using deep learning techniques - Md Firoz Kabir, Md Mizanur Rahman, Abdulla Al Mamun, Md Yousuf Ahmad - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.1751
DOI	https://doi.org/10.71097/IJSAT.v16.i1.1751
Short DOI	https://doi.org/g84j94

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