EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment
The precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/9/2099 |
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| author | Jihong Sun Zhaowen Li Fusheng Li Yingming Shen Ye Qian Tong Li |
| author_facet | Jihong Sun Zhaowen Li Fusheng Li Yingming Shen Ye Qian Tong Li |
| author_sort | Jihong Sun |
| collection | DOAJ |
| description | The precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation of relevant nutrients to ensure sugarcane quality and yield. This paper proposes a human–machine collaborative sugarcane disease detection method in complex environments. Initially, data on five common sugarcane diseases—brown stripe, rust, ring spot, brown spot, and red rot—as well as two nutrient deficiency conditions—sulfur deficiency and phosphorus deficiency—were collected, totaling 11,364 images and 10 high-definition videos captured by a 4K drone. The data sets were augmented threefold using techniques such as flipping and gamma adjustment to construct a disease data set. Building upon the YOLOv8 framework, the EMA attention mechanism and Focal loss function were added to optimize the model, addressing the complex backgrounds and imbalanced positive and negative samples present in the sugarcane data set. Disease detection models EF-yolov8s, EF-yolov8m, EF-yolov8n, EF-yolov7, and EF-yolov5n were constructed and compared. Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. The experimental results demonstrate that our improved EF-yolov8s model outperforms other models, achieving mAP_0.5, precision, recall, and F1 scores of 89.70%, 88.70%, 86.00%, and 88.00%, respectively, highlighting the effectiveness of EF-yolov8s for sugarcane disease detection. Additionally, yolov8s-seg achieves an average precision of 80.30% with a smaller number of parameters, outperforming other models by 5.2%, 1.9%, 2.02%, and 0.92% in terms of mAP_0.5, respectively, effectively detecting nutrient deficiency symptoms and addressing the challenges of sugarcane growth monitoring and disease detection in complex environments using computer vision technology. |
| format | Article |
| id | doaj-art-548668377457468cb6907a829a408fd1 |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-548668377457468cb6907a829a408fd12025-08-20T01:56:02ZengMDPI AGAgronomy2073-43952024-09-01149209910.3390/agronomy14092099EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex EnvironmentJihong Sun0Zhaowen Li1Fusheng Li2Yingming Shen3Ye Qian4Tong Li5College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, ChinaThe Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming 650201, ChinaCollege of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, ChinaInternational Cooperation Office, Yunnan Provincial Academy of Science and Technology, Kunming 650201, ChinaThe Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming 650201, ChinaThe Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming 650201, ChinaThe precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation of relevant nutrients to ensure sugarcane quality and yield. This paper proposes a human–machine collaborative sugarcane disease detection method in complex environments. Initially, data on five common sugarcane diseases—brown stripe, rust, ring spot, brown spot, and red rot—as well as two nutrient deficiency conditions—sulfur deficiency and phosphorus deficiency—were collected, totaling 11,364 images and 10 high-definition videos captured by a 4K drone. The data sets were augmented threefold using techniques such as flipping and gamma adjustment to construct a disease data set. Building upon the YOLOv8 framework, the EMA attention mechanism and Focal loss function were added to optimize the model, addressing the complex backgrounds and imbalanced positive and negative samples present in the sugarcane data set. Disease detection models EF-yolov8s, EF-yolov8m, EF-yolov8n, EF-yolov7, and EF-yolov5n were constructed and compared. Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. The experimental results demonstrate that our improved EF-yolov8s model outperforms other models, achieving mAP_0.5, precision, recall, and F1 scores of 89.70%, 88.70%, 86.00%, and 88.00%, respectively, highlighting the effectiveness of EF-yolov8s for sugarcane disease detection. Additionally, yolov8s-seg achieves an average precision of 80.30% with a smaller number of parameters, outperforming other models by 5.2%, 1.9%, 2.02%, and 0.92% in terms of mAP_0.5, respectively, effectively detecting nutrient deficiency symptoms and addressing the challenges of sugarcane growth monitoring and disease detection in complex environments using computer vision technology.https://www.mdpi.com/2073-4395/14/9/2099disease detectiongrowth monitoringdeficiency syndromecomplex environmentattention mechanism |
| spellingShingle | Jihong Sun Zhaowen Li Fusheng Li Yingming Shen Ye Qian Tong Li EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment Agronomy disease detection growth monitoring deficiency syndrome complex environment attention mechanism |
| title | EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment |
| title_full | EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment |
| title_fullStr | EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment |
| title_full_unstemmed | EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment |
| title_short | EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment |
| title_sort | ef yolov8s a human computer collaborative sugarcane disease detection model in complex environment |
| topic | disease detection growth monitoring deficiency syndrome complex environment attention mechanism |
| url | https://www.mdpi.com/2073-4395/14/9/2099 |
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