Passion Fruit Disease Detection Using Sparse Parallel Attention Mechanism and Optical Sensing

Yajie He, Ningyi Zhang, Xinjin Ge, Siqi Li, Linfeng Yang, Minghao Kong, Yiping Guo and Chunli Lv ()
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Yajie He: China Agricultural University, Beijing 100083, China
Ningyi Zhang: China Agricultural University, Beijing 100083, China
Xinjin Ge: China Agricultural University, Beijing 100083, China
Siqi Li: China Agricultural University, Beijing 100083, China
Linfeng Yang: China Agricultural University, Beijing 100083, China
Minghao Kong: China Agricultural University, Beijing 100083, China
Yiping Guo: China Agricultural University, Beijing 100083, China
Chunli Lv: China Agricultural University, Beijing 100083, China

Agriculture, 2025, vol. 15, issue 7, 1-24

Abstract: A disease detection network based on a sparse parallel attention mechanism is proposed and experimentally validated in the passion fruit ( Passiflora edulis [Sims]) disease detection task. Passiflora edulis , 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.

Keywords: passion fruit disease detection; multi-scale detection; smart agriculture; image processing; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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