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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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/7/733 |
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| Summary: | 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. |
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| ISSN: | 2077-0472 |