Plant disease recognition using residual convolutional enlightened Swin transformer networks.

Autor: Kalpana P; Department of Computer Science Engineering, Vels Institute of Science Technology and Advanced Studies, Chennai, Tamil Nadu, 600117, India. kalpanaraogonait@gmail.com., Anandan R; Department of Computer Science Engineering, Vels Institute of Science Technology and Advanced Studies, Chennai, Tamil Nadu, 600117, India., Hussien AG; Department of Computer and Information Science, Linköping University, Linköping, Sweden. abdelazim.hussien@liu.se.; Faculty of Science, Fayoum University, Fayoum, Egypt. abdelazim.hussien@liu.se., Migdady H; CSMIS Department, Oman College of Management and Technology, 320, Barka, Oman., Abualigah L; Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491, Tabuk, Saudi Arabia.; Computer Science Department, Al Al-Bayt University, Mafraq, 25113, Jordan.; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan.; MEU Research Unit, Middle East University, Amman, 11831, Jordan.; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon.; School of Computer Sciences, Universiti Sains Malaysia, 11800, George Town, Penang, Malaysia.; School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia.; Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan.; College of Engineering, Yuan Ze University, Taoyuan, Taiwan.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Apr 15; Vol. 14 (1), pp. 8660. Date of Electronic Publication: 2024 Apr 15.
DOI: 10.1038/s41598-024-56393-8
Abstrakt: Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer's life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands. But, precise prediction of plant diseases is a complex job due to the presence of noise, changes in the intensities, similar resemblance between healthy and diseased plants and finally dimension of plant leaves. To tackle this problem, high-accurate and intelligently tuned deep learning algorithms are mandatorily needed. In this research article, novel ensemble of Swin transformers and residual convolutional networks are proposed. Swin transformers (ST) are hierarchical structures with linearly scalable computing complexity that offer performance and flexibility at various scales. In order to extract the best deep key-point features, the Swin transformers and residual networks has been combined, followed by Feed forward networks for better prediction. Extended experimentation is conducted using Plant Village Kaggle datasets, and performance metrics, including accuracy, precision, recall, specificity, and F1-rating, are evaluated and analysed. Existing structure along with FCN-8s, CED-Net, SegNet, DeepLabv3, Dense nets, and Central nets are used to demonstrate the superiority of the suggested version. The experimental results show that in terms of accuracy, precision, recall, and F1-rating, the introduced version shown better performances than the other state-of-art hybrid learning models.
(© 2024. The Author(s).)
Databáze: MEDLINE
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