Investigation on the use of ensemble learning and big data in crop identification
The agriculture sector in Egypt faces several problems, such as climate change, water storage, and yield variability. The comprehensive capabilities of Big Data (BD) can help in tackling the uncertainty of food supply occurs due to several factors such as soil erosion, water pollution, climate chang...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023005467 |
| _version_ | 1811161761774043136 |
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| author | Sayed Ahmed Amira S. Mahmoud Eslam Farg Amany M. Mohamed Marwa S. Moustafa Khaled Abutaleb Ahmed M. Saleh Mohamed A.E. AbdelRahman Hisham M. AbdelSalam Sayed M. Arafat |
| author_facet | Sayed Ahmed Amira S. Mahmoud Eslam Farg Amany M. Mohamed Marwa S. Moustafa Khaled Abutaleb Ahmed M. Saleh Mohamed A.E. AbdelRahman Hisham M. AbdelSalam Sayed M. Arafat |
| author_sort | Sayed Ahmed |
| collection | DOAJ |
| description | The agriculture sector in Egypt faces several problems, such as climate change, water storage, and yield variability. The comprehensive capabilities of Big Data (BD) can help in tackling the uncertainty of food supply occurs due to several factors such as soil erosion, water pollution, climate change, socio-cultural growth, governmental regulations, and market fluctuations. Crop identification and monitoring plays a vital role in modern agriculture. Although several machine learning models have been utilized in identifying crops, the performance of ensemble learning has not been investigated extensively. The massive volume of satellite imageries has been established as a big data problem forcing to deploy the proposed solution using big data technologies to manage, store, analyze, and visualize satellite data. In this paper, we have developed a weighted voting mechanism for improving crop classification performance in a large scale, based on ensemble learning and big data schema. Built upon Apache Spark, the popular DB Framework, the proposed approach was tested on El Salheya, Ismaili governate. The proposed ensemble approach boosted accuracy by 6.5%, 1.9%, 4.4%, 4.9%, 4.7% in precision, recall, F-score, Overall Accuracy (OA), and Matthews correlation coefficient (MCC) metrics respectively. Our findings confirm the generalization of the proposed crop identification approach at a large-scale setting. |
| first_indexed | 2024-04-10T06:20:26Z |
| format | Article |
| id | doaj.art-41fa3ac0a2b14b7a8821f76c9369306d |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-10T06:20:26Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-41fa3ac0a2b14b7a8821f76c9369306d2023-03-02T05:01:06ZengElsevierHeliyon2405-84402023-02-0192e13339Investigation on the use of ensemble learning and big data in crop identificationSayed Ahmed0Amira S. Mahmoud1Eslam Farg2Amany M. Mohamed3Marwa S. Moustafa4Khaled Abutaleb5Ahmed M. Saleh6Mohamed A.E. AbdelRahman7Hisham M. AbdelSalam8Sayed M. Arafat9National Authority for Remote Sensing and Space Science (NARSS), Cairo, EgyptNational Authority for Remote Sensing and Space Science (NARSS), Cairo, EgyptNational Authority for Remote Sensing and Space Science (NARSS), Cairo, EgyptNational Authority for Remote Sensing and Space Science (NARSS), Cairo, EgyptNational Authority for Remote Sensing and Space Science (NARSS), Cairo, EgyptNational Authority for Remote Sensing and Space Science (NARSS), Cairo, EgyptNational Authority for Remote Sensing and Space Science (NARSS), Cairo, EgyptNational Authority for Remote Sensing and Space Science (NARSS), Cairo, Egypt; Corresponding author.Faculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptNational Authority for Remote Sensing and Space Science (NARSS), Cairo, EgyptThe agriculture sector in Egypt faces several problems, such as climate change, water storage, and yield variability. The comprehensive capabilities of Big Data (BD) can help in tackling the uncertainty of food supply occurs due to several factors such as soil erosion, water pollution, climate change, socio-cultural growth, governmental regulations, and market fluctuations. Crop identification and monitoring plays a vital role in modern agriculture. Although several machine learning models have been utilized in identifying crops, the performance of ensemble learning has not been investigated extensively. The massive volume of satellite imageries has been established as a big data problem forcing to deploy the proposed solution using big data technologies to manage, store, analyze, and visualize satellite data. In this paper, we have developed a weighted voting mechanism for improving crop classification performance in a large scale, based on ensemble learning and big data schema. Built upon Apache Spark, the popular DB Framework, the proposed approach was tested on El Salheya, Ismaili governate. The proposed ensemble approach boosted accuracy by 6.5%, 1.9%, 4.4%, 4.9%, 4.7% in precision, recall, F-score, Overall Accuracy (OA), and Matthews correlation coefficient (MCC) metrics respectively. Our findings confirm the generalization of the proposed crop identification approach at a large-scale setting.http://www.sciencedirect.com/science/article/pii/S2405844023005467Big dataCrop identificationEnsemble learningDB FrameworkApache spark |
| spellingShingle | Sayed Ahmed Amira S. Mahmoud Eslam Farg Amany M. Mohamed Marwa S. Moustafa Khaled Abutaleb Ahmed M. Saleh Mohamed A.E. AbdelRahman Hisham M. AbdelSalam Sayed M. Arafat Investigation on the use of ensemble learning and big data in crop identification Heliyon Big data Crop identification Ensemble learning DB Framework Apache spark |
| title | Investigation on the use of ensemble learning and big data in crop identification |
| title_full | Investigation on the use of ensemble learning and big data in crop identification |
| title_fullStr | Investigation on the use of ensemble learning and big data in crop identification |
| title_full_unstemmed | Investigation on the use of ensemble learning and big data in crop identification |
| title_short | Investigation on the use of ensemble learning and big data in crop identification |
| title_sort | investigation on the use of ensemble learning and big data in crop identification |
| topic | Big data Crop identification Ensemble learning DB Framework Apache spark |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023005467 |
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