Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture
This study aims to improve the precision of wheat spike counting and disease detection, exploring the application of deep learning in the agricultural sector. Addressing the shortcomings of traditional detection methods, we propose an advanced feature extraction strategy and a model based on the pro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/13/24/3462 |
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| author | Ruiheng Li Wenjie Hong Ruiming Wu Yan Wang Xiaohan Wu Zhongtian Shi Yifei Xu Zixu Han Chunli Lv |
| author_facet | Ruiheng Li Wenjie Hong Ruiming Wu Yan Wang Xiaohan Wu Zhongtian Shi Yifei Xu Zixu Han Chunli Lv |
| author_sort | Ruiheng Li |
| collection | DOAJ |
| description | This study aims to improve the precision of wheat spike counting and disease detection, exploring the application of deep learning in the agricultural sector. Addressing the shortcomings of traditional detection methods, we propose an advanced feature extraction strategy and a model based on the probability density attention mechanism, designed to more effectively handle feature extraction in complex backgrounds and dense areas. Through comparative experiments with various advanced models, we comprehensively evaluate the performance of our model. In the disease detection task, our model performs excellently, achieving a precision of 0.93, a recall of 0.89, an accuracy of 0.91, and an mAP of 0.90. By introducing the density loss function, we are able to effectively improve the detection accuracy when dealing with high-density regions. In the wheat spike counting task, the model similarly demonstrates a strong performance, with a precision of 0.91, a recall of 0.88, an accuracy of 0.90, and an mAP of 0.90, further validating its effectiveness. Furthermore, this paper also conducts ablation experiments on different loss functions. The results of this research provide a new method for wheat spike counting and disease detection, fully reflecting the application value of deep learning in precision agriculture. By combining the probability density attention mechanism and the density loss function, the proposed model significantly improves the detection accuracy and efficiency, offering important references for future related research. |
| format | Article |
| id | doaj-art-6066c1a944414584969d83c61f89bf4c |
| institution | Kabale University |
| issn | 2223-7747 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-6066c1a944414584969d83c61f89bf4c2024-12-27T14:47:38ZengMDPI AGPlants2223-77472024-12-011324346210.3390/plants13243462Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision AgricultureRuiheng Li0Wenjie Hong1Ruiming Wu2Yan Wang3Xiaohan Wu4Zhongtian Shi5Yifei Xu6Zixu Han7Chunli Lv8China Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaThis study aims to improve the precision of wheat spike counting and disease detection, exploring the application of deep learning in the agricultural sector. Addressing the shortcomings of traditional detection methods, we propose an advanced feature extraction strategy and a model based on the probability density attention mechanism, designed to more effectively handle feature extraction in complex backgrounds and dense areas. Through comparative experiments with various advanced models, we comprehensively evaluate the performance of our model. In the disease detection task, our model performs excellently, achieving a precision of 0.93, a recall of 0.89, an accuracy of 0.91, and an mAP of 0.90. By introducing the density loss function, we are able to effectively improve the detection accuracy when dealing with high-density regions. In the wheat spike counting task, the model similarly demonstrates a strong performance, with a precision of 0.91, a recall of 0.88, an accuracy of 0.90, and an mAP of 0.90, further validating its effectiveness. Furthermore, this paper also conducts ablation experiments on different loss functions. The results of this research provide a new method for wheat spike counting and disease detection, fully reflecting the application value of deep learning in precision agriculture. By combining the probability density attention mechanism and the density loss function, the proposed model significantly improves the detection accuracy and efficiency, offering important references for future related research.https://www.mdpi.com/2223-7747/13/24/3462wheatspikedisease detectionsmart agriculturedeep learning |
| spellingShingle | Ruiheng Li Wenjie Hong Ruiming Wu Yan Wang Xiaohan Wu Zhongtian Shi Yifei Xu Zixu Han Chunli Lv Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture Plants wheat spike disease detection smart agriculture deep learning |
| title | Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture |
| title_full | Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture |
| title_fullStr | Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture |
| title_full_unstemmed | Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture |
| title_short | Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture |
| title_sort | enhancing wheat spike counting and disease detection using a probability density attention mechanism in deep learning models for precision agriculture |
| topic | wheat spike disease detection smart agriculture deep learning |
| url | https://www.mdpi.com/2223-7747/13/24/3462 |
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