Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing
A disease detection network based on a sparse parallel attention mechanism is proposed and experimentally validated in the passion fruit (<i>Passiflora edulis</i> [Sims]) disease detection task. <i>Passiflora edulis</i>, as a tropical and subtropical fruit tree, is loved worl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/7/733 |
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| author | Yajie He Ningyi Zhang Xinjin Ge Siqi Li Linfeng Yang Minghao Kong Yiping Guo Chunli Lv |
| author_facet | Yajie He Ningyi Zhang Xinjin Ge Siqi Li Linfeng Yang Minghao Kong Yiping Guo Chunli Lv |
| author_sort | Yajie He |
| collection | DOAJ |
| description | A disease detection network based on a sparse parallel attention mechanism is proposed and experimentally validated in the passion fruit (<i>Passiflora edulis</i> [Sims]) disease detection task. <i>Passiflora edulis</i>, as a tropical and subtropical fruit tree, is loved worldwide for its unique flavor and rich nutritional value. The experimental results demonstrate that the proposed model performs excellently across various metrics, achieving a precision of 0.93, a recall of 0.88, an accuracy of 0.91, an mAP@50 (average precision at the IoU threshold of 0.50) of 0.90, an mAP@50–95 (average precision at IoU thresholds from 0.50 to 0.95) of 0.60, and an F1-score of 0.90, significantly outperforming traditional object detection models such as Faster R-CNN, SSD, and YOLO. The experiments show that the sparse parallel attention mechanism offers significant advantages in disease detection with multi-scale and complex backgrounds. This study proposes a lightweight deep learning model incorporating a sparse parallel attention mechanism (SPAM) for passion fruit disease detection. Built upon a Convolutional Neural Network (CNN) backbone, the model integrates a dynamically selective attention mechanism to enhance detection performance in cases with complex backgrounds and multi-scale objects. Experimental results demonstrate that the model has superior precision, recall, and mean average precision (mAP) compared with state-of-the-art detection models while maintaining computational efficiency. |
| format | Article |
| id | doaj-art-e5ee2dc868324270a89ce86cb631e0f7 |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-e5ee2dc868324270a89ce86cb631e0f72025-08-20T02:15:54ZengMDPI AGAgriculture2077-04722025-03-0115773310.3390/agriculture15070733Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical SensingYajie He0Ningyi Zhang1Xinjin Ge2Siqi Li3Linfeng Yang4Minghao Kong5Yiping Guo6Chunli Lv7China 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, ChinaA disease detection network based on a sparse parallel attention mechanism is proposed and experimentally validated in the passion fruit (<i>Passiflora edulis</i> [Sims]) disease detection task. <i>Passiflora edulis</i>, as a tropical and subtropical fruit tree, is loved worldwide for its unique flavor and rich nutritional value. The experimental results demonstrate that the proposed model performs excellently across various metrics, achieving a precision of 0.93, a recall of 0.88, an accuracy of 0.91, an mAP@50 (average precision at the IoU threshold of 0.50) of 0.90, an mAP@50–95 (average precision at IoU thresholds from 0.50 to 0.95) of 0.60, and an F1-score of 0.90, significantly outperforming traditional object detection models such as Faster R-CNN, SSD, and YOLO. The experiments show that the sparse parallel attention mechanism offers significant advantages in disease detection with multi-scale and complex backgrounds. This study proposes a lightweight deep learning model incorporating a sparse parallel attention mechanism (SPAM) for passion fruit disease detection. Built upon a Convolutional Neural Network (CNN) backbone, the model integrates a dynamically selective attention mechanism to enhance detection performance in cases with complex backgrounds and multi-scale objects. Experimental results demonstrate that the model has superior precision, recall, and mean average precision (mAP) compared with state-of-the-art detection models while maintaining computational efficiency.https://www.mdpi.com/2077-0472/15/7/733passion fruit disease detectionmulti-scale detectionsmart agricultureimage processingdeep learning |
| spellingShingle | Yajie He Ningyi Zhang Xinjin Ge Siqi Li Linfeng Yang Minghao Kong Yiping Guo Chunli Lv Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing Agriculture passion fruit disease detection multi-scale detection smart agriculture image processing deep learning |
| title | Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing |
| title_full | Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing |
| title_fullStr | Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing |
| title_full_unstemmed | Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing |
| title_short | Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing |
| title_sort | passion fruit disease detection using sparse parallel attention mechanism and optical sensing |
| topic | passion fruit disease detection multi-scale detection smart agriculture image processing deep learning |
| url | https://www.mdpi.com/2077-0472/15/7/733 |
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