An enhanced tunicate swarm algorithm with deep-learning based rice seedling classification for sustainable computing based smart agriculture

Manal Abdullah Alohali; Fuad Al-Mutiri; Kamal M. Othman; Ayman Yafoz; Raed Alsini; Ahmed S. Salama; Manal Abdullah Alohali; Fuad Al-Mutiri; Kamal M. Othman; Ayman Yafoz; Raed Alsini; Ahmed S. Salama

doi:10.3934/math.2024498

AIMS Mathematics

2024, Volume 9, Issue 4: 10185-10207. doi: 10.3934/math.2024498

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An enhanced tunicate swarm algorithm with deep-learning based rice seedling classification for sustainable computing based smart agriculture

1.
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
2.
Department of Mathematics, Faculty of Sciences and Arts, King Khalid University, Muhayil Asir, Saudi Arabia
3.
Department of Electrical Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, Saudi Arabia
4.
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
5.
Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt

Received: 21 January 2024 Revised: 29 February 2024 Accepted: 06 March 2024 Published: 14 March 2024
MSC : 11Y40

Smart agricultural techniques employ current information and communication technologies, leveraging artificial intelligence (AI) for effectually managing the crop. Recognizing rice seedlings, which is crucial for harvest estimation, traditionally depends on human supervision but can be expedited and enhanced via computer vision (CV). Unmanned aerial vehicles (UAVs) equipped with high-resolution cameras bestow a swift and precise option for crop condition surveillance, specifically in cloudy states, giving valuable insights into crop management and breeding programs. Therefore, we improved an enhanced tunicate swarm algorithm with deep learning-based rice seedling classification (ETSADL-RSC). The presented ETSADL-RSC technique examined the UAV images to classify them into two classes: Rice seedlings and arable land. Initially, the quality of the pictures could be enhanced by a contrast limited adaptive histogram equalization (CLAHE) approach. Next, the ETSADL-RSC technique used the neural architectural search network (NASNet) method for the feature extraction process and its hyperparameters could be tuned by the ETSA model. For rice seedling classification, the ETSADL-RSC technique used a sparse autoencoder (SAE) model. The experimental outcome study of the ETSADL-RSC system was verified for the UAV Rice Seedling Classification dataset. Wide simulation analysis of the ETSADL-RSC model stated the greater accuracy performance of 97.79% over other DL classifiers.
- smart agriculture,
- crop monitoring,
- rice seedling,
- computer vision,
- image classification,
- deep learning
Citation: Manal Abdullah Alohali, Fuad Al-Mutiri, Kamal M. Othman, Ayman Yafoz, Raed Alsini, Ahmed S. Salama. An enhanced tunicate swarm algorithm with deep-learning based rice seedling classification for sustainable computing based smart agriculture[J]. AIMS Mathematics, 2024, 9(4): 10185-10207. doi: 10.3934/math.2024498

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Abstract

Smart agricultural techniques employ current information and communication technologies, leveraging artificial intelligence (AI) for effectually managing the crop. Recognizing rice seedlings, which is crucial for harvest estimation, traditionally depends on human supervision but can be expedited and enhanced via computer vision (CV). Unmanned aerial vehicles (UAVs) equipped with high-resolution cameras bestow a swift and precise option for crop condition surveillance, specifically in cloudy states, giving valuable insights into crop management and breeding programs. Therefore, we improved an enhanced tunicate swarm algorithm with deep learning-based rice seedling classification (ETSADL-RSC). The presented ETSADL-RSC technique examined the UAV images to classify them into two classes: Rice seedlings and arable land. Initially, the quality of the pictures could be enhanced by a contrast limited adaptive histogram equalization (CLAHE) approach. Next, the ETSADL-RSC technique used the neural architectural search network (NASNet) method for the feature extraction process and its hyperparameters could be tuned by the ETSA model. For rice seedling classification, the ETSADL-RSC technique used a sparse autoencoder (SAE) model. The experimental outcome study of the ETSADL-RSC system was verified for the UAV Rice Seedling Classification dataset. Wide simulation analysis of the ETSADL-RSC model stated the greater accuracy performance of 97.79% over other DL classifiers.

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