MSCPUnet: A multi-task neural network for plot-level crop classification in complex agricultural areas
High-precision mapping of agricultural crops in complex planting areas is a prerequisite for precision agricultural management. This paper first proposes a novel multi-task neural network, Multi-task Multi-Scale Convolutional Pooling Unet (MSCPUnet), for extracting vector plots. MSCPUnet is based on...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277237552400265X |
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| author | Kedi Fang Shengwei Zhang Yongting Han Lin Yang Meng Luo Lu Liu Qian Zhang Bo Wang |
| author_facet | Kedi Fang Shengwei Zhang Yongting Han Lin Yang Meng Luo Lu Liu Qian Zhang Bo Wang |
| author_sort | Kedi Fang |
| collection | DOAJ |
| description | High-precision mapping of agricultural crops in complex planting areas is a prerequisite for precision agricultural management. This paper first proposes a novel multi-task neural network, Multi-task Multi-Scale Convolutional Pooling Unet (MSCPUnet), for extracting vector plots. MSCPUnet is based on the Unet model and enhances performance through the incorporation of attention mechanisms, multi-scale pooling layers, and a multi-task learning approach for parallel processing. A ZiYuan-1 02D (ZY1E) satellite image collected from the He-Tao irrigation district in China is selected for plot extraction experiments, where MSCPUnet is compared with three other deep learning network variants. The Inter section over Union (IoU) and Accuracy metrics for the MSCPUnet model achieve the highest values, converging at 0.837 and 0.928, respectively. Leveraging this capability, a crop classification framework is proposed, which first extracts crop attributes from Sentinel-2 (S2) time series data. The plot information obtained from the MSCPUnet model is then combined with the area dominance method to assign crop attributes to vector plots, facilitating crop structure identification at the plot scale. Results indicate that this method significantly improves classification accuracy for fragmented farmlands, with overall accuracy (OA) rising to 91 % and Kappa coefficient increasing to 0.84 compared to a random forest classifier. This integrated crop classification approach has been validated in this study for high-precision crop mapping in complex planting areas. |
| format | Article |
| id | doaj-art-17a7669795ab4ab08dc5df5494df28f9 |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-17a7669795ab4ab08dc5df5494df28f92025-08-20T01:59:35ZengElsevierSmart Agricultural Technology2772-37552024-12-01910066010.1016/j.atech.2024.100660MSCPUnet: A multi-task neural network for plot-level crop classification in complex agricultural areasKedi Fang0Shengwei Zhang1Yongting Han2Lin Yang3Meng Luo4Lu Liu5Qian Zhang6Bo Wang7College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Inner Mongolia Key Laboratory of Ecohydrology and High Efficient Utilization of Water Resources, Hohhot 010018, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China; Corresponding author.College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaHigh-precision mapping of agricultural crops in complex planting areas is a prerequisite for precision agricultural management. This paper first proposes a novel multi-task neural network, Multi-task Multi-Scale Convolutional Pooling Unet (MSCPUnet), for extracting vector plots. MSCPUnet is based on the Unet model and enhances performance through the incorporation of attention mechanisms, multi-scale pooling layers, and a multi-task learning approach for parallel processing. A ZiYuan-1 02D (ZY1E) satellite image collected from the He-Tao irrigation district in China is selected for plot extraction experiments, where MSCPUnet is compared with three other deep learning network variants. The Inter section over Union (IoU) and Accuracy metrics for the MSCPUnet model achieve the highest values, converging at 0.837 and 0.928, respectively. Leveraging this capability, a crop classification framework is proposed, which first extracts crop attributes from Sentinel-2 (S2) time series data. The plot information obtained from the MSCPUnet model is then combined with the area dominance method to assign crop attributes to vector plots, facilitating crop structure identification at the plot scale. Results indicate that this method significantly improves classification accuracy for fragmented farmlands, with overall accuracy (OA) rising to 91 % and Kappa coefficient increasing to 0.84 compared to a random forest classifier. This integrated crop classification approach has been validated in this study for high-precision crop mapping in complex planting areas.http://www.sciencedirect.com/science/article/pii/S277237552400265XMulti-task neural networkPlot segmentationHigh-precision crop mappingZY1E satellite |
| spellingShingle | Kedi Fang Shengwei Zhang Yongting Han Lin Yang Meng Luo Lu Liu Qian Zhang Bo Wang MSCPUnet: A multi-task neural network for plot-level crop classification in complex agricultural areas Smart Agricultural Technology Multi-task neural network Plot segmentation High-precision crop mapping ZY1E satellite |
| title | MSCPUnet: A multi-task neural network for plot-level crop classification in complex agricultural areas |
| title_full | MSCPUnet: A multi-task neural network for plot-level crop classification in complex agricultural areas |
| title_fullStr | MSCPUnet: A multi-task neural network for plot-level crop classification in complex agricultural areas |
| title_full_unstemmed | MSCPUnet: A multi-task neural network for plot-level crop classification in complex agricultural areas |
| title_short | MSCPUnet: A multi-task neural network for plot-level crop classification in complex agricultural areas |
| title_sort | mscpunet a multi task neural network for plot level crop classification in complex agricultural areas |
| topic | Multi-task neural network Plot segmentation High-precision crop mapping ZY1E satellite |
| url | http://www.sciencedirect.com/science/article/pii/S277237552400265X |
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