Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery
Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essenti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/1779 |
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| author | Haseeb Rehman Khan Zeeshan Gillani Muhammad Hasan Jamal Atifa Athar Muhammad Tayyab Chaudhry Haoyu Chao Yong He Ming Chen |
| author_facet | Haseeb Rehman Khan Zeeshan Gillani Muhammad Hasan Jamal Atifa Athar Muhammad Tayyab Chaudhry Haoyu Chao Yong He Ming Chen |
| author_sort | Haseeb Rehman Khan |
| collection | DOAJ |
| description | Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food. |
| first_indexed | 2024-03-11T08:11:27Z |
| format | Article |
| id | doaj.art-0506349cfb0b4d429c75b8ca9580f156 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:11:27Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0506349cfb0b4d429c75b8ca9580f1562023-11-16T23:06:03ZengMDPI AGSensors1424-82202023-02-01234177910.3390/s23041779Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 ImageryHaseeb Rehman Khan0Zeeshan Gillani1Muhammad Hasan Jamal2Atifa Athar3Muhammad Tayyab Chaudhry4Haoyu Chao5Yong He6Ming Chen7Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, PakistanDepartment of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, PakistanDepartment of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, PakistanDepartment of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, PakistanDepartment of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, PakistanDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, ChinaClimate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.https://www.mdpi.com/1424-8220/23/4/1779crop-type mappingSentinel-2deep learningcrop classification |
| spellingShingle | Haseeb Rehman Khan Zeeshan Gillani Muhammad Hasan Jamal Atifa Athar Muhammad Tayyab Chaudhry Haoyu Chao Yong He Ming Chen Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery Sensors crop-type mapping Sentinel-2 deep learning crop classification |
| title | Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery |
| title_full | Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery |
| title_fullStr | Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery |
| title_full_unstemmed | Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery |
| title_short | Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery |
| title_sort | early identification of crop type for smallholder farming systems using deep learning on time series sentinel 2 imagery |
| topic | crop-type mapping Sentinel-2 deep learning crop classification |
| url | https://www.mdpi.com/1424-8220/23/4/1779 |
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