Quantitative and Correlation Analysis of Pear Leaf Dynamics Under Wind Field Disturbances
In wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and t...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/15/1597 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849407835779104768 |
|---|---|
| author | Yunfei Wang Xiang Dong Weidong Jia Mingxiong Ou Shiqun Dai Zhenlei Zhang Ruohan Shi |
| author_facet | Yunfei Wang Xiang Dong Weidong Jia Mingxiong Ou Shiqun Dai Zhenlei Zhang Ruohan Shi |
| author_sort | Yunfei Wang |
| collection | DOAJ |
| description | In wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and the spatial distribution of pesticide efficacy. However, current research lacks comprehensive quantification and correlation analysis of the temporal response characteristics of leaves under wind disturbances. To address this gap, a systematic analytical framework was proposed, integrating real-time leaf segmentation and tracking, geometric feature quantification, and statistical correlation modeling. High-frame-rate videos of fluttering leaves were acquired under controlled wind conditions, and background segmentation was performed using principal component analysis (PCA) followed by clustering in the reduced feature space. A fine-tuned Segment Anything Model 2 (SAM2-FT) was employed to extract dynamic leaf masks and enable frame-by-frame tracking. Based on the extracted masks, time series of leaf area and inclination angle were constructed. Subsequently, regression analysis, cross-correlation functions, and Granger causality tests were applied to investigate cooperative responses and potential driving relationships among leaves. Results showed that the SAM2-FT model significantly outperformed the YOLO series in segmentation accuracy, achieving a precision of 98.7% and recall of 97.48%. Leaf area exhibited strong linear coupling and directional causality, while angular responses showed weaker correlations but demonstrated localized synchronization. This study offers a methodological foundation for quantifying temporal dynamics in wind–leaf systems and provides theoretical insights for the adaptive control and optimization of intelligent spraying strategies. |
| format | Article |
| id | doaj-art-05d3df7638c54c1283ec0390a930ce7b |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-05d3df7638c54c1283ec0390a930ce7b2025-08-20T03:35:57ZengMDPI AGAgriculture2077-04722025-07-011515159710.3390/agriculture15151597Quantitative and Correlation Analysis of Pear Leaf Dynamics Under Wind Field DisturbancesYunfei Wang0Xiang Dong1Weidong Jia2Mingxiong Ou3Shiqun Dai4Zhenlei Zhang5Ruohan Shi6School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaIn wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and the spatial distribution of pesticide efficacy. However, current research lacks comprehensive quantification and correlation analysis of the temporal response characteristics of leaves under wind disturbances. To address this gap, a systematic analytical framework was proposed, integrating real-time leaf segmentation and tracking, geometric feature quantification, and statistical correlation modeling. High-frame-rate videos of fluttering leaves were acquired under controlled wind conditions, and background segmentation was performed using principal component analysis (PCA) followed by clustering in the reduced feature space. A fine-tuned Segment Anything Model 2 (SAM2-FT) was employed to extract dynamic leaf masks and enable frame-by-frame tracking. Based on the extracted masks, time series of leaf area and inclination angle were constructed. Subsequently, regression analysis, cross-correlation functions, and Granger causality tests were applied to investigate cooperative responses and potential driving relationships among leaves. Results showed that the SAM2-FT model significantly outperformed the YOLO series in segmentation accuracy, achieving a precision of 98.7% and recall of 97.48%. Leaf area exhibited strong linear coupling and directional causality, while angular responses showed weaker correlations but demonstrated localized synchronization. This study offers a methodological foundation for quantifying temporal dynamics in wind–leaf systems and provides theoretical insights for the adaptive control and optimization of intelligent spraying strategies.https://www.mdpi.com/2077-0472/15/15/1597precision sprayingdynamic leaf trackingtemporal quantificationsynergistic response analysis |
| spellingShingle | Yunfei Wang Xiang Dong Weidong Jia Mingxiong Ou Shiqun Dai Zhenlei Zhang Ruohan Shi Quantitative and Correlation Analysis of Pear Leaf Dynamics Under Wind Field Disturbances Agriculture precision spraying dynamic leaf tracking temporal quantification synergistic response analysis |
| title | Quantitative and Correlation Analysis of Pear Leaf Dynamics Under Wind Field Disturbances |
| title_full | Quantitative and Correlation Analysis of Pear Leaf Dynamics Under Wind Field Disturbances |
| title_fullStr | Quantitative and Correlation Analysis of Pear Leaf Dynamics Under Wind Field Disturbances |
| title_full_unstemmed | Quantitative and Correlation Analysis of Pear Leaf Dynamics Under Wind Field Disturbances |
| title_short | Quantitative and Correlation Analysis of Pear Leaf Dynamics Under Wind Field Disturbances |
| title_sort | quantitative and correlation analysis of pear leaf dynamics under wind field disturbances |
| topic | precision spraying dynamic leaf tracking temporal quantification synergistic response analysis |
| url | https://www.mdpi.com/2077-0472/15/15/1597 |
| work_keys_str_mv | AT yunfeiwang quantitativeandcorrelationanalysisofpearleafdynamicsunderwindfielddisturbances AT xiangdong quantitativeandcorrelationanalysisofpearleafdynamicsunderwindfielddisturbances AT weidongjia quantitativeandcorrelationanalysisofpearleafdynamicsunderwindfielddisturbances AT mingxiongou quantitativeandcorrelationanalysisofpearleafdynamicsunderwindfielddisturbances AT shiqundai quantitativeandcorrelationanalysisofpearleafdynamicsunderwindfielddisturbances AT zhenleizhang quantitativeandcorrelationanalysisofpearleafdynamicsunderwindfielddisturbances AT ruohanshi quantitativeandcorrelationanalysisofpearleafdynamicsunderwindfielddisturbances |