Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging
To achieve an efficient, non-destructive, and intelligent identification of tea plant seedlings under high-temperature stress, this study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification. Using tea plant seedlings under varying de...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/14/13/1965 |
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| author | Chun Wang Zejun Wang Lijiao Chen Weihao Liu Xinghua Wang Zhiyong Cao Jinyan Zhao Man Zou Hongxu Li Wenxia Yuan Baijuan Wang |
| author_facet | Chun Wang Zejun Wang Lijiao Chen Weihao Liu Xinghua Wang Zhiyong Cao Jinyan Zhao Man Zou Hongxu Li Wenxia Yuan Baijuan Wang |
| author_sort | Chun Wang |
| collection | DOAJ |
| description | To achieve an efficient, non-destructive, and intelligent identification of tea plant seedlings under high-temperature stress, this study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification. Using tea plant seedlings under varying degrees of high temperature as the research objects, raw fluorescence images were acquired through a chlorophyll fluorescence image acquisition device. The fluorescence parameters obtained by Spearman correlation analysis were found to be the maximum photochemical efficiency (Fv/Fm), and the fluorescence image of this parameter is used to construct the dataset. The YOLOv11 model was improved in the following ways. First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. The research results show that the improved YOLOv11-MEIP model has the best performance, with precision, recall, and mAP50 reaching 99.25%, 99.19%, and 99.46%, respectively. Compared with the YOLOv11 model, the improved YOLOv11-MEIP model achieved increases of 4.05%, 7.86%, and 3.42% in precision, recall, and mAP50, respectively. Additionally, the number of model parameters was reduced by 29.45%. This study provides a new intelligent method for the classification of high-temperature stress levels of tea seedlings, as well as state detection and identification, and provides new theoretical support and technical reference for the monitoring and prevention of tea plants and other crops in tea gardens under high temperatures. |
| format | Article |
| id | doaj-art-8d1371ca49464ae38213f3412fbb7861 |
| institution | Kabale University |
| issn | 2223-7747 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-8d1371ca49464ae38213f3412fbb78612025-08-20T03:50:17ZengMDPI AGPlants2223-77472025-06-011413196510.3390/plants14131965Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence ImagingChun Wang0Zejun Wang1Lijiao Chen2Weihao Liu3Xinghua Wang4Zhiyong Cao5Jinyan Zhao6Man Zou7Hongxu Li8Wenxia Yuan9Baijuan Wang10College of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming 650201, ChinaYunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, ChinaYunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, ChinaYunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, ChinaYunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, ChinaYunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, ChinaYunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, ChinaYunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, ChinaYunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, ChinaYunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, ChinaYunnan Organic Tea Industry Intelligent Engineering Research Center, Kunming 650201, ChinaTo achieve an efficient, non-destructive, and intelligent identification of tea plant seedlings under high-temperature stress, this study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification. Using tea plant seedlings under varying degrees of high temperature as the research objects, raw fluorescence images were acquired through a chlorophyll fluorescence image acquisition device. The fluorescence parameters obtained by Spearman correlation analysis were found to be the maximum photochemical efficiency (Fv/Fm), and the fluorescence image of this parameter is used to construct the dataset. The YOLOv11 model was improved in the following ways. First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. The research results show that the improved YOLOv11-MEIP model has the best performance, with precision, recall, and mAP50 reaching 99.25%, 99.19%, and 99.46%, respectively. Compared with the YOLOv11 model, the improved YOLOv11-MEIP model achieved increases of 4.05%, 7.86%, and 3.42% in precision, recall, and mAP50, respectively. Additionally, the number of model parameters was reduced by 29.45%. This study provides a new intelligent method for the classification of high-temperature stress levels of tea seedlings, as well as state detection and identification, and provides new theoretical support and technical reference for the monitoring and prevention of tea plants and other crops in tea gardens under high temperatures.https://www.mdpi.com/2223-7747/14/13/1965chlorophyll fluorescence imaginghigh-temperature stresstea plant seedlingsimproved YOLOv11model lightweightimage classification recognition |
| spellingShingle | Chun Wang Zejun Wang Lijiao Chen Weihao Liu Xinghua Wang Zhiyong Cao Jinyan Zhao Man Zou Hongxu Li Wenxia Yuan Baijuan Wang Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging Plants chlorophyll fluorescence imaging high-temperature stress tea plant seedlings improved YOLOv11 model lightweight image classification recognition |
| title | Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging |
| title_full | Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging |
| title_fullStr | Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging |
| title_full_unstemmed | Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging |
| title_short | Intelligent Identification of Tea Plant Seedlings Under High-Temperature Conditions via YOLOv11-MEIP Model Based on Chlorophyll Fluorescence Imaging |
| title_sort | intelligent identification of tea plant seedlings under high temperature conditions via yolov11 meip model based on chlorophyll fluorescence imaging |
| topic | chlorophyll fluorescence imaging high-temperature stress tea plant seedlings improved YOLOv11 model lightweight image classification recognition |
| url | https://www.mdpi.com/2223-7747/14/13/1965 |
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