Real-time object detection method of melon leaf diseases under complex background in greenhouse

Xu, Yanlei; Chen, Qingyuan; Kong, Shuolin; Lü, Xing; Wang, Qi; Xue, Cong; Zhou, Yang

doi:10.1007/s11554-022-01239-7

Cited by 20 publications

(16 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This particular configuration achieved an impressive average precision (AP) of 85.98% and effectively recognized nine different diseases. In their study, Xu et al. (2022) presented a real-time technique for detecting diseases on cucumber leaves.…”

Section: Related Workmentioning

confidence: 99%

Tomato disease object detection method combining prior knowledge attention mechanism and multiscale features

Liu,

Wang

2023

Front. Plant Sci.

View full text Add to dashboard Cite

To address the challenges of insufficient accuracy in detecting tomato disease object detection caused by dense target distributions, large-scale variations, and poor feature information of small objects in complex backgrounds, this study proposes the tomato disease object detection method that integrates prior knowledge attention mechanism and multi-scale features (PKAMMF). Firstly, the visual features of tomato disease images are fused with prior knowledge through the prior knowledge attention mechanism to obtain enhanced visual features corresponding to tomato diseases. Secondly, a new feature fusion layer is constructed in the Neck section to reduce feature loss. Furthermore, a specialized prediction layer specifically designed to improve the model’s ability to detect small targets is incorporated. Finally, a new loss function known as A-SIOU (Adaptive Structured IoU) is employed to optimize the performance of the model in terms of bounding box regression. The experimental results on the self-built tomato disease dataset demonstrate the effectiveness of the proposed approach, and it achieves a mean average precision (mAP) of 91.96%, which is a 3.86% improvement compared to baseline methods. The results show significant improvements in the detection performance of multi-scale tomato disease objects.

show abstract

Section: Related Workmentioning

confidence: 99%

Tomato disease object detection method combining prior knowledge attention mechanism and multiscale features

Liu,

Wang

2023

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Wu et al [23] complete the detection and counting of banana bunches by integrating deep learning and classical image processing algorithms. Xu et al [24] put the ShuffleNet lightweight network in the YOLOv5 backbone to detect melon leaf diseases. On the Jetson nano device, the reasoning time was 13.8 ms. Jia et al [25] used the YOLOv7 structure and lightweight network MobileNet for feature extraction of rice disease and insect detection.…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al. [24] put the ShuffleNet lightweight network in the YOLOv5 backbone to detect melon leaf diseases. On the Jetson nano device, the reasoning time was 13.8 ms. Jia et al.…”

Section: Introductionmentioning

confidence: 99%

A lightweight waxberry fruit detection model based on YOLOv5

Yang,

Liu,

2024

IET Image Processing

View full text Add to dashboard Cite

In order to solve the safety and efficiency problems in the picking process of Waxberry, the slow speed and low precision of high‐density Waxberry target detection under a complex background were studied. A lightweight Waxberry target detection algorithm based on YOLOv5 is studied. In this study, C3‐Faster1 and C3‐Faster2 modules are proposed, which are located in the backbone and neck of the network: C3‐Faster1 can improve the model speed with a simple structure; C3‐Faster2 integrates the context attention mechanism and transform module based on C3‐Faster1 to make the network pay attention to the information of Waxberry image context and expand the channel receptive field. A new pyramid module, SPPFCSPC, is proposed to expand the sensing field and improve the accuracy of boundary detection. It also combines the Coordinate Attention (CA) and Dyhead dynamic detection head to suppress useless information and enhance the detection ability of small targets. Compared to YOLOv4, YOLOv7, and YOLOv8, mean accuracy percentage (mAP) improved by 5.7%, 9.4%, 8.3%. Compared to the base YOLOv5 model, mAP has improved from 86.5% to 91.9%, running on 2 GB Jeston nano, and the improved model is 5.03 frames per second (FPS) faster than YOLOv5. Experiments show that the designed module is more effective in Waxberry detection tasks.

show abstract

“…The two-stage algorithm has high accuracy but poor real-time performance, and the effect of detecting small targets is poor. The one-stage algorithm has the advantage of high real-time performance, but the accuracy of group targets and small targets is low, and the suitable scene is real-time target detection [ 13 ]. However, in real life, when performing time-sensitive operations such as fertilizer application and fruit picking, the detection of plants is often real-time target detection, and its significance is greater.…”

Section: Introductionmentioning

confidence: 99%

YOLO-plum: A high precision and real-time improved algorithm for plum recognition

Niu,

Lu,

Liang

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Real-time, rapid, accurate, and non-destructive batch testing of fruit growth state is crucial for improving economic benefits. However, for plums, environmental variability, multi-scale, occlusion, overlapping of leaves or fruits pose significant challenges to accurate and complete labeling using mainstream algorithms like YOLOv5. In this study, we established the first artificial dataset of plums and used deep learning to improve target detection. Our improved YOLOv5 algorithm achieved more accurate and rapid batch identification of immature plums, resulting in improved quality and economic benefits. The YOLOv5-plum algorithm showed 91.65% recognition accuracy for immature plums after our algorithmic improvements. Currently, the YOLOv5-plum algorithm has demonstrated significant advantages in detecting unripe plums and can potentially be applied to other unripe fruits in the future.

show abstract

Real-time object detection method of melon leaf diseases under complex background in greenhouse

Cited by 20 publications

References 25 publications

Tomato disease object detection method combining prior knowledge attention mechanism and multiscale features

Tomato disease object detection method combining prior knowledge attention mechanism and multiscale features

A lightweight waxberry fruit detection model based on YOLOv5

YOLO-plum: A high precision and real-time improved algorithm for plum recognition

Contact Info

Product

Resources

About