Grading Method of Fresh Cut Rose Flowers Based on Improved YOLOv8s

doi:10.12133/j.smartag.SA202401005

Abstract

Abstract:

[Objective] The fresh cut rose industry has shown a positive growth trend in recent years, demonstrating sustained development. Considering the current fresh cut roses grading process relies on simple manual grading, which results in low efficiency and accuracy, a new model named Flower-YOLOv8s was proposed for grading detection of fresh cut roses. [Methods] The flower head of a single rose against a uniform background was selected as the primary detection target. Subsequently, fresh cut roses were categorized into four distinct grades: A, B, C, and D. These grades were determined based on factors such as color, size, and freshness, ensuring a comprehensive and objective grading system. A novel dataset contenting 778 images was specifically tailored for rose fresh-cut flower grading and detection was constructed. This dataset served as the foundation for our subsequent experiments and analysis. To further enhance the performance of the YOLOv8s model, two cutting-edge attention convolutional block attention module (CBAM) and spatial attention module (SAM) were introduced separately for comparison experiments. These modules were seamlessly integrated into the backbone network of the YOLOv8s model to enhance its ability to focus on salient features and suppressing irrelevant information. Moreover, selecting and optimizing the SAM module by reducing the number of convolution kernels, incorporating a depth-separable convolution module and reducing the number of input channels to improve the module's efficiency and contribute to reducing the overall computational complexity of the model. The convolution layer (Conv) in the C2f module was replaced by the depth separable convolution (DWConv), and then combined with Optimized-SAM was introduced into the C2f structure, giving birth to the Flower-YOLOv8s model. Precision, recall and F₁ score were used as evaluation indicators. [Results and Discussions] Ablation results showed that the Flower-YOLOv8s model proposed in this study, namely YOLOv8s+DWConv+Optimized-SAM, the recall rate was 95.4%, which was 3.8% higher and the average accuracy, 0.2% higher than that of YOLOv8s with DWConv alone. When compared to the baseline model YOLOv8s, the Flower-YOLOv8s model exhibited a remarkable 2.1% increase in accuracy, reaching a peak of 97.4%. Furthermore, mAP was augmented by 0.7%, demonstrating the model's superior performance across various evaluation metrics. The effectiveness of adding Optimized-SAM was proved. From the overall experimental results, the number of parameters of Flower-YOLOv8s was reduced by 2.26 M compared with the baseline model YOLOv8s, and the reasoning time was also reduced from 15.6 to 5.7 ms. Therefore, the Flower-YOLOv8s model was superior to the baseline model in terms of accuracy rate, average accuracy, number of parameters, detection time and model size. The performances of Flower-YOLOv8s network were compared with other target detection algorithms of Fast-RCNN, Faster-RCNN and first-stage target detection models of SSD, YOLOv3, YOLOv5s and YOLOv8s to verify the superiority under the same condition and the same data set. The average precision values of the Flower-YOLOv8s model proposed in this study were 2.6%, 19.4%, 6.5%, 1.7%, 1.9% and 0.7% higher than those of Fast-RCNN, Faster-RCNN, SSD, YOLOv3, YOLOv5s and YOLOv8s, respectively. Compared with YOLOv8s with higher recall rate, Flower-YOLOv8s reduced model size, inference time and parameter number by 4.5 MB, 9.9 ms and 2.26 M, respectively. Notably, the Flower-YOLOv8s model achieved these improvements while simultaneously reducing model parameters and computational complexity. [Conclusions] The Flower-YOLOv8s model not only demonstrated superior detection accuracy but also exhibited a reduction in model parameters and computational complexity. This lightweight yet powerful model is highly suitable for real-time applications, making it a promising candidate for flower grading and detection tasks in the agricultural and horticultural industries.

Key words: YOLOv8s, fresh cut roses, hierarchical detection, deep learning, SAM, attention mechanism

ZHANG Yuyu, BING Shuying, JI Yuanhao, YAN Beibei, XU Jinpu. Grading Method of Fresh Cut Rose Flowers Based on Improved YOLOv8s[J]. Smart Agriculture, 2024, 6(2): 118-127.

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等级	分级标准
A级	花头大，花色花型正常；无弯头，开放度基本一致，基本无色差；叶片无损伤，无虫害，成熟度达到季节采收要求；茎秆粗细均匀，叶面清秀无污染
B级	花头大，花色花型正常，有轻微损伤；无弯头，开放度大部分一致，基本无色差；叶片基本无损伤，无虫害，成熟度达到季节采收要求；茎秆粗细均匀，叶面清秀无污染
C级	花色、花型有偏差，可能有双心花，花头、花径、叶有轻微损伤；轻微病虫害、弯头、缺陷，开放度大部分一致；部分茎秆弯曲，粗细不均匀，叶面有轻微药斑、病斑
D级	花色、花型有畸形或擦痕，花头、花径、叶片有损伤；大部分为侧枝切花，有病虫害、弯头、缺陷，开放度一般；茎秆多数细短弯曲，叶面有药斑病斑

过程	A级/张	B级/张	C级/张	D级/张	合计/张
增强前	46	95	107	76	324
增强后	114	231	244	189	778

参数	数值
学习率（Learning Rate）	0.000 1
图像大小（Image Size）/像素	640×640
梯度动量（Momentum）	0.937
Batch Size	16
迭代轮数（Epochs）	200
优化器（Optimizer）	Adam

模型名称	精确率/%	召回率/%	mAP@0.5/%	参数量/M	推理时间/ms	模型大小/MB
YOLOv8s	95.3	96.5	82.4	11.13	15.6	22.5
YOLOv8s+DWConv	97.2	91.6	82.9	11.13	9.2	22.6
YOLOv8s+DWConv+Optimized-SAM（Flower-YOLOv8s）	97.4	95.4	83.1	8.87	5.7	18.0

模型名称	准确率/%	召回率/%	mAP@0.5/%	mAP@0.5∶0.95/%	F ₁值/%	参数量/10⁶	推理时间/ms	模型大小/MB
Fast-RCNN	86.8	81.4	80.5	76.1	0.55	193.9	36.5	102.35
Faster-RCNN	93.3	87.2	63.7	58.9	0.49	254.6	48.8	142.08
SSD	95.8	80.6	76.3	68.8	0.66	106.4	25.3	97.42
YOLOv3	93.6	76.9	81.4	71.8	0.74	207.8	22.6	103.67
YOLOv5s	86.1	74.4	81.2	71.0	0.75	22.2	5.9	10.92
YOLOv8s	95.3	96.5	82.4	72.2	0.75	22.5	15.6	11.13
Flower-YOLOv8s	97.4	95.4	83.1	72.5	0.77	18.0	5.7	8.87