Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses
Environmental control based on growth stage is critical for enhancing the yield and quality of industrially cultivated <i>Pleurotus pulmonarius</i>. Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmenta...
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MDPI AG
2025-05-01
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| Series: | Agriculture |
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| author | Can Wang Xinhui Wu Zhaoquan Wang Han Shao Dapeng Ye Xiangzeng Kong |
| author_facet | Can Wang Xinhui Wu Zhaoquan Wang Han Shao Dapeng Ye Xiangzeng Kong |
| author_sort | Can Wang |
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| description | Environmental control based on growth stage is critical for enhancing the yield and quality of industrially cultivated <i>Pleurotus pulmonarius</i>. Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmentation. This study introduces a lightweight method called the real-time detection model for the growth stages of <i>P. pulmonarius</i> (GSP-RTMDet). A spatial pyramid pooling fast network with simple parameter-free attention (SPPF-SAM) was proposed, which enhances the backbone’s capability to extract key feature information. Additionally, it features an interactive attention mechanism between spatial and channel dimensions to build a cross-stage partial spatial group-wise enhance network (CSP-SGE), improving the feature fusion capability of the neck. The class-aware adaptive feature enhancement (CARAFE) upsampling module is utilized to enhance instance segmentation performance. This study innovatively fusions the improved methods, enhancing the feature representation and the accuracy of masks. By lightweight model design, it achieves real-time growth stage detection of <i>P. pulmonarius</i> and accurate instance segmentation, forming the foundation of an environmental control strategy. Model evaluations reveal that GSP-RTMDet-S achieves an optimal balance between accuracy and speed, with a bounding box mean average precision (bbox mAP) and a segmentation mAP (segm mAP) of 96.40% and 93.70% on the test set, marking improvements of 2.20% and 1.70% over the baseline. Moreover, it boosts inference speed to 39.58 images per second. This method enhances detection and segmentation outcomes in real-world environments of <i>P. pulmonarius</i> houses, offering a more accurate and efficient growth stage perception solution for environmental control. |
| format | Article |
| id | doaj-art-de1af4bceffd48a9ba82a9859b466d37 |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-de1af4bceffd48a9ba82a9859b466d372025-08-20T01:57:04ZengMDPI AGAgriculture2077-04722025-05-011510103310.3390/agriculture15101033Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom HousesCan Wang0Xinhui Wu1Zhaoquan Wang2Han Shao3Dapeng Ye4Xiangzeng Kong5College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaSchool of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaEnvironmental control based on growth stage is critical for enhancing the yield and quality of industrially cultivated <i>Pleurotus pulmonarius</i>. Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmentation. This study introduces a lightweight method called the real-time detection model for the growth stages of <i>P. pulmonarius</i> (GSP-RTMDet). A spatial pyramid pooling fast network with simple parameter-free attention (SPPF-SAM) was proposed, which enhances the backbone’s capability to extract key feature information. Additionally, it features an interactive attention mechanism between spatial and channel dimensions to build a cross-stage partial spatial group-wise enhance network (CSP-SGE), improving the feature fusion capability of the neck. The class-aware adaptive feature enhancement (CARAFE) upsampling module is utilized to enhance instance segmentation performance. This study innovatively fusions the improved methods, enhancing the feature representation and the accuracy of masks. By lightweight model design, it achieves real-time growth stage detection of <i>P. pulmonarius</i> and accurate instance segmentation, forming the foundation of an environmental control strategy. Model evaluations reveal that GSP-RTMDet-S achieves an optimal balance between accuracy and speed, with a bounding box mean average precision (bbox mAP) and a segmentation mAP (segm mAP) of 96.40% and 93.70% on the test set, marking improvements of 2.20% and 1.70% over the baseline. Moreover, it boosts inference speed to 39.58 images per second. This method enhances detection and segmentation outcomes in real-world environments of <i>P. pulmonarius</i> houses, offering a more accurate and efficient growth stage perception solution for environmental control.https://www.mdpi.com/2077-0472/15/10/1033environmental parameter controlgrowth stage detectioncomputer visioninstance segmentationlightweight |
| spellingShingle | Can Wang Xinhui Wu Zhaoquan Wang Han Shao Dapeng Ye Xiangzeng Kong Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses Agriculture environmental parameter control growth stage detection computer vision instance segmentation lightweight |
| title | Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses |
| title_full | Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses |
| title_fullStr | Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses |
| title_full_unstemmed | Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses |
| title_short | Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses |
| title_sort | real time detection and instance segmentation models for the growth stages of i pleurotus pulmonarius i for environmental control in mushroom houses |
| topic | environmental parameter control growth stage detection computer vision instance segmentation lightweight |
| url | https://www.mdpi.com/2077-0472/15/10/1033 |
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