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Khan HR, Gillani Z, Jamal MH, Athar A, Chaudhry MT, Chao H, He Y, Chen M. Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery. SENSORS (BASEL, SWITZERLAND) 2023; 23:1779. [PMID: 36850377 PMCID: PMC9967001 DOI: 10.3390/s23041779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/27/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Haseeb Rehman Khan
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
| | - Zeeshan Gillani
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Muhammad Hasan Jamal
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
| | - Atifa Athar
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
| | - Muhammad Tayyab Chaudhry
- Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
| | - Haoyu Chao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
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Son NT, Chen CF, Cheng YS, Toscano P, Chen CR, Chen SL, Tseng KH, Syu CH, Guo HY, Zhang YT. Field-scale rice yield prediction from Sentinel-2 monthly image composites using machine learning algorithms. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101618] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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