Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification
Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a sign...
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| Format: | Article |
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MDPI AG
2023-11-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/7/535 |
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| author | Ahmed S. Almasoud Hanan Abdullah Mengash Muhammad Kashif Saeed Faiz Abdullah Alotaibi Kamal M. Othman Ahmed Mahmud |
| author_facet | Ahmed S. Almasoud Hanan Abdullah Mengash Muhammad Kashif Saeed Faiz Abdullah Alotaibi Kamal M. Othman Ahmed Mahmud |
| author_sort | Ahmed S. Almasoud |
| collection | DOAJ |
| description | Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant application of RS technology in agriculture. It involves the use of satellite or aerial imagery to identify and classify different types of food crops grown in a specific area. This information can be valuable for crop monitoring, yield estimation, and land management. Meeting the criteria for analyzing these data requires increasingly sophisticated methods and artificial intelligence (AI) technologies provide the necessary support. Due to the heterogeneity and fragmentation of crop planting, typical classification approaches have a lower classification performance. However, the DL technique can detect and categorize crop types effectively and has a stronger feature extraction capability. In this aspect, this study designed a new remote sensing imagery data analysis using the marine predators algorithm with deep learning for food crop classification (RSMPA-DLFCC) technique. The RSMPA-DLFCC technique mainly investigates the RS data and determines the variety of food crops. In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process. The MPA is applied for the optimal hyperparameter selection process in order to optimize the accuracy of SimAM-EfficientNet architecture. MPA, inspired by the foraging behaviors of marine predators, perceptively explores hyperparameter configurations to optimize the hyperparameters, thereby improving the classification accuracy and generalization capabilities. For crop type detection and classification, an extreme learning machine (ELM) model can be used. The simulation analysis of the RSMPA-DLFCC technique is performed on two benchmark datasets. The extensive analysis of the results portrayed the higher performance of the RSMPA-DLFCC approach over existing DL techniques. |
| first_indexed | 2024-03-09T16:59:27Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-09T16:59:27Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-68297abfe216432baba23f5447ba71a62023-11-24T14:31:38ZengMDPI AGBiomimetics2313-76732023-11-018753510.3390/biomimetics8070535Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop ClassificationAhmed S. Almasoud0Hanan Abdullah Mengash1Muhammad Kashif Saeed2Faiz Abdullah Alotaibi3Kamal M. Othman4Ahmed Mahmud5Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, Applied College, Muhayil, King Khalid University, Abha 61421, Saudi ArabiaDepartment of Information Science, College of Humanities and Social Sciences, King Saud University, Riyadh 11437, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah 21955, Saudi ArabiaResearch Center, Future University in Egypt, New Cairo 11835, EgyptRecently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant application of RS technology in agriculture. It involves the use of satellite or aerial imagery to identify and classify different types of food crops grown in a specific area. This information can be valuable for crop monitoring, yield estimation, and land management. Meeting the criteria for analyzing these data requires increasingly sophisticated methods and artificial intelligence (AI) technologies provide the necessary support. Due to the heterogeneity and fragmentation of crop planting, typical classification approaches have a lower classification performance. However, the DL technique can detect and categorize crop types effectively and has a stronger feature extraction capability. In this aspect, this study designed a new remote sensing imagery data analysis using the marine predators algorithm with deep learning for food crop classification (RSMPA-DLFCC) technique. The RSMPA-DLFCC technique mainly investigates the RS data and determines the variety of food crops. In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process. The MPA is applied for the optimal hyperparameter selection process in order to optimize the accuracy of SimAM-EfficientNet architecture. MPA, inspired by the foraging behaviors of marine predators, perceptively explores hyperparameter configurations to optimize the hyperparameters, thereby improving the classification accuracy and generalization capabilities. For crop type detection and classification, an extreme learning machine (ELM) model can be used. The simulation analysis of the RSMPA-DLFCC technique is performed on two benchmark datasets. The extensive analysis of the results portrayed the higher performance of the RSMPA-DLFCC approach over existing DL techniques.https://www.mdpi.com/2313-7673/8/7/535remote sensing imagesdeep learningcrop classificationmachine learningcomputer vision |
| spellingShingle | Ahmed S. Almasoud Hanan Abdullah Mengash Muhammad Kashif Saeed Faiz Abdullah Alotaibi Kamal M. Othman Ahmed Mahmud Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification Biomimetics remote sensing images deep learning crop classification machine learning computer vision |
| title | Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification |
| title_full | Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification |
| title_fullStr | Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification |
| title_full_unstemmed | Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification |
| title_short | Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification |
| title_sort | remote sensing imagery data analysis using marine predators algorithm with deep learning for food crop classification |
| topic | remote sensing images deep learning crop classification machine learning computer vision |
| url | https://www.mdpi.com/2313-7673/8/7/535 |
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