Cross-Platform Wheat Ear Counting Model Using Deep Learning for UAV and Ground Systems
Wheat is one of the widely cultivated crops. Accurate and efficient high-throughput ear counting is important for wheat production, yield evaluation, and seed breeding. The traditional wheat ear counting method is inefficient due to the small scope of investigation. Especially in the wheat field sce...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/7/1792 |
| _version_ | 1827734291515179008 |
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| author | Baohua Yang Ming Pan Zhiwei Gao Hongbo Zhi Xiangxuan Zhang |
| author_facet | Baohua Yang Ming Pan Zhiwei Gao Hongbo Zhi Xiangxuan Zhang |
| author_sort | Baohua Yang |
| collection | DOAJ |
| description | Wheat is one of the widely cultivated crops. Accurate and efficient high-throughput ear counting is important for wheat production, yield evaluation, and seed breeding. The traditional wheat ear counting method is inefficient due to the small scope of investigation. Especially in the wheat field scene, the images obtained from different platforms, including ground systems and unmanned aerial vehicles (UAVs), have differences in density, scale, and wheat ear distribution, which makes the wheat ear counting task still face some challenges. To this end, a density map counting network (LWDNet) model was constructed for cross-platform wheat ear statistics. Firstly, CA-MobileNetV3 was constructed by introducing a collaborative attention mechanism (CA) to optimize the lightweight neural network MobileNetV3, which was used as the front end of the feature extraction network, aiming to solve the problem of occlusion and adhesion of wheat ears in the field. Secondly, to enhance the model’s ability to learn the detailed features of wheat ears, the CARAFE upsampling module was introduced in the feature fusion layer to better restore the characteristics of wheat ears and improve the counting accuracy of the model for wheat ears. Finally, density map regression was used to achieve high-density, small-target ear counting, and the model was tested on datasets from different platforms. The results showed that our method can efficiently count wheat ears of different spatial scales, achieving good accuracy while maintaining a competitive number of parameters (2.38 million with a size of 9.24 MB), which will benefit wheat breeding and screening analysis to provide technical support. |
| first_indexed | 2024-03-11T01:23:15Z |
| format | Article |
| id | doaj.art-1d81645bc90a455696221095635a30a0 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T01:23:15Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-1d81645bc90a455696221095635a30a02023-11-18T17:56:14ZengMDPI AGAgronomy2073-43952023-07-01137179210.3390/agronomy13071792Cross-Platform Wheat Ear Counting Model Using Deep Learning for UAV and Ground SystemsBaohua Yang0Ming Pan1Zhiwei Gao2Hongbo Zhi3Xiangxuan Zhang4School of Information and Computer, Anhui Agricultural University, Hefei 230036, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei 230036, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei 230036, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei 230036, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei 230036, ChinaWheat is one of the widely cultivated crops. Accurate and efficient high-throughput ear counting is important for wheat production, yield evaluation, and seed breeding. The traditional wheat ear counting method is inefficient due to the small scope of investigation. Especially in the wheat field scene, the images obtained from different platforms, including ground systems and unmanned aerial vehicles (UAVs), have differences in density, scale, and wheat ear distribution, which makes the wheat ear counting task still face some challenges. To this end, a density map counting network (LWDNet) model was constructed for cross-platform wheat ear statistics. Firstly, CA-MobileNetV3 was constructed by introducing a collaborative attention mechanism (CA) to optimize the lightweight neural network MobileNetV3, which was used as the front end of the feature extraction network, aiming to solve the problem of occlusion and adhesion of wheat ears in the field. Secondly, to enhance the model’s ability to learn the detailed features of wheat ears, the CARAFE upsampling module was introduced in the feature fusion layer to better restore the characteristics of wheat ears and improve the counting accuracy of the model for wheat ears. Finally, density map regression was used to achieve high-density, small-target ear counting, and the model was tested on datasets from different platforms. The results showed that our method can efficiently count wheat ears of different spatial scales, achieving good accuracy while maintaining a competitive number of parameters (2.38 million with a size of 9.24 MB), which will benefit wheat breeding and screening analysis to provide technical support.https://www.mdpi.com/2073-4395/13/7/1792UAVswheat ear countingdensity maplightweight |
| spellingShingle | Baohua Yang Ming Pan Zhiwei Gao Hongbo Zhi Xiangxuan Zhang Cross-Platform Wheat Ear Counting Model Using Deep Learning for UAV and Ground Systems Agronomy UAVs wheat ear counting density map lightweight |
| title | Cross-Platform Wheat Ear Counting Model Using Deep Learning for UAV and Ground Systems |
| title_full | Cross-Platform Wheat Ear Counting Model Using Deep Learning for UAV and Ground Systems |
| title_fullStr | Cross-Platform Wheat Ear Counting Model Using Deep Learning for UAV and Ground Systems |
| title_full_unstemmed | Cross-Platform Wheat Ear Counting Model Using Deep Learning for UAV and Ground Systems |
| title_short | Cross-Platform Wheat Ear Counting Model Using Deep Learning for UAV and Ground Systems |
| title_sort | cross platform wheat ear counting model using deep learning for uav and ground systems |
| topic | UAVs wheat ear counting density map lightweight |
| url | https://www.mdpi.com/2073-4395/13/7/1792 |
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