Real-Time Monitoring Method of Strawberry Fruit Growth State Based on YOLO Improved Model

An, Qilin; Wang, Kai; Li, Zhongyang; Song, Chengyuan; Tang, Xiuying; Jian, Song

doi:10.1109/access.2022.3220234

Cited by 36 publications

(10 citation statements)

References 28 publications

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“…Similarly, the transient maturity class exhibits the least mAP value due to the wide feature space between immature and mature classes, compounded by the limited number of data samples in the dataset. A similar observation was observed in the studies of SDNet [25] and DSE-YOLO model [10].…”

Section: Analysis Of Class-wise Strawberry Fruit Detection Algorithmssupporting

confidence: 88%

“…Table 6 shows the performance metric mAP@0.5, model size, and FPS of three strawberry detection models with our model YOLOv5s-CGhostnet. Among the compared object detection models, SDNet [25] stands out with the highest mAP@0.5 of 94.26, showcasing commendable accuracy. However, its larger model size at 54.6 MB and a moderate FPS of 30.5 are of concern.…”

Section: Comparison With Relevant Studiesmentioning

confidence: 97%

“…In this study, the improved YOLOv5s-CGhostnet was analyzed on performance met rics and compared with the standard detection model and recent relevant studies, ablation experiments, and feature visualizations. However, the quantification information is also essential to determine yield and helps to count the number of ripe fruits in the image Therefore, to identify and quantify different classes, real-time detection and counting o the fruits is visualized using an autonomous program developed by modifying inheren capability of detect.py source code in YOLOv5, as recommended in a prior study [25]. Th result of fruit detection and counting is shown in Figure 8.…”

Section: Real-time Detection and Countingmentioning

confidence: 99%

See 2 more Smart Citations

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Tamrakar,

Karki,

Kang

et al. 2024

AgriEngineering

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A lightweight strawberry detection and localization algorithm plays a crucial role in enabling the harvesting robot to effectively harvest strawberries. The YOLO model has often been used in strawberry fruit detection for its high accuracy, speed, and robustness. However, some challenges exist, such as the requirement for large model sizes, high computation operation, and undesirable detection. Therefore, the lightweight improved YOLOv5s-CGhostnet was proposed to enhance strawberry detection. In this study, YOLOv5s underwent comprehensive model compression with Ghost modules GCBS and GC3, replacing modules CBS and C3 in the backbone and neck. Furthermore, the default GIOU bounding box regressor loss function was replaced by SIOU for improved localization. Similarly, CBAM attention modules were added before SPPF and between the up-sampling and down-sampling feature fusion FPN–PAN network in the neck section. The improved model exhibited higher mAP@0.5 of 91.7% with a significant decrement in model size by 85.09% and a reduction in GFLOPS by 88.5% compared to the baseline model of YOLOv5. The model demonstrated an increment in mean average precision, a decrement in model size, and reduced computation overhead compared to the standard lightweight YOLO models.

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Section: Analysis Of Class-wise Strawberry Fruit Detection Algorithmssupporting

confidence: 88%

Section: Comparison With Relevant Studiesmentioning

confidence: 97%

Section: Real-time Detection and Countingmentioning

confidence: 99%

See 1 more Smart Citation

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Tamrakar,

Karki,

Kang

et al. 2024

AgriEngineering

View full text Add to dashboard Cite

show abstract

“…Finally, the sigmoid func tion is used to calculate the feature weights s, as expressed in Equation ( 2 The SE module primarily consists of three steps. In the first step, a feature map X is processed through the F tr convolution to obtain a feature map U of size H × W × C. Feature map U is then compressed through F sq to yield a 1 × 1 × C output, which is achieved by global average pooling, compressing the two-dimensional features of each channel into a single real number [18]. Its expression is given in Equation (1):…”

Section: Se Attention Mechanismmentioning

confidence: 99%

Evaluation Model of Rice Seedling Production Line Seeding Quality Based on Deep Learning

Liu,

He,

Cao

et al. 2024

Applied Sciences

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A critical precondition for realizing mechanized transplantation in rice cultivation is the implementation of seedling tray techniques. To augment the efficacy of seeding, a precise evaluation of the quality of rice seedling cultivation in these trays is imperative. This research centers on the analysis of rice seedling tray images, employing deep learning as the foundational technology. The aim is to construct a computational model capable of autonomously evaluating seeding quality within the ambit of intelligent seedling cultivation processes. This study proposes a virtual grid-based image segmentation preprocessing method. It involves dividing the complete image of a rice seedling tray into several grid images. These grid images are then classified and marked using an improved ResNet50 model that integrates the SE attention mechanism with the Adam optimizer. Finally, the objective of detecting missing seeding areas is achieved by reassembling the marked grid images. The experimental results demonstrate that the improved ResNet50 model, integrating the SE attention mechanism and employing an initial learning rate of 0.01 over 50 iterations, attains a test set accuracy of 95.82%. This accuracy surpasses that of the AlexNet, DenseNet, and VGG16 models by respective margins of 4.55%, 2.07%, and 2.62%. This study introduces an innovative model for the automatic assessment of rice seeding quality. This model is capable of rapidly evaluating the seeding quality during the seedling phase; precisely identifying the locations of missing seeds in individual seedling trays; and effectively calculating the missing seed rate for each tray. Such precision in assessment is instrumental for optimizing seedling processes

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“…However, although algorithms such as deep learning convolutional neural networks and YOLO models can detect different targets quickly and accurately, complex and changing natural environments still pose a challenge for fruit detection, such as leaf occlusion, fruit overlap, light changes, brightness changes, target size, and shooting distant views, all of which affect fruit ripeness detection precision and accuracy [17]. In addition, the existing fruit ripeness detection studies mainly focus on crops such as apple [18], tomato [19], jujube [20], mango [21], and oil palm [22]; as an economic fruit crop of China's National Geographic indication products, there are few related studies on Gannan navel orange.…”

Section: Introductionmentioning

confidence: 99%

Detection of Gannan Navel Orange Ripeness in Natural Environment Based on YOLOv5-NMM

Zhou,

Wu,

Chen

2024

Agronomy

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In order to achieve fast and accurate detection of Gannan navel orange fruits with different ripeness levels in a natural environment under all-weather scenarios and then to realise automated harvesting of Gannan navel oranges, this paper proposes a YOLOv5-NMM (YOLOv5 with Navel orange Measure Model) object detection model based on the improvement in the original YOLOv5 model. Based on the changes in the phenotypic characteristics of navel oranges and the Chinese national standard GB/T 21488-2008, the maturity of Gannan navel oranges is tested. And it addresses and improves the problems of occlusion, dense distribution, small target size, rainy days, and light changes in the detection of navel orange fruits. Firstly, a new detection head of 160 × 160 feature maps is constructed in the detection layer to improve the multi-scale target detection layer of YOLOv5 and to increase the detection accuracy of the different maturity levels of Gannan navel oranges of small sizes. Secondly, a convolutional block attention module is incorporated in its backbone layer to capture the correlations between features in different dimensions to improve the perceptual ability of the model. Then, the weighted bidirectional feature pyramid network structure is integrated into the Neck layer to improve the fusion efficiency of the network on the feature maps and reduce the amount of computation. Lastly, in order to reduce the loss of the target of the Gannan Navel Orange due to occlusion and overlapping, the detection frame is used to remove redundancy using the Soft-NMS algorithm to remove redundant candidate frames. The results show that the accuracy rate, recall rate, and average accuracy of the improved YOLOv5-NMM model are 93.2%, 89.6%, and 94.2%, respectively, and the number of parameters is only 7.2 M. Compared with the mainstream network models, such as Faster R-CNN, YOLOv3, the original model of YOLOv5, and YOLOv7-tiny, it is superior in terms of the accuracy rate, recall rate, and average accuracy mean, and also performs well in terms of the detection rate and memory occupation. This study shows that the YOLOv5-NMM model can effectively identify and detect the ripeness of Gannan navel oranges in natural environments, which provides an effective exploration of the automated harvesting of Gannan navel orange fruits.

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Real-Time Monitoring Method of Strawberry Fruit Growth State Based on YOLO Improved Model

Cited by 36 publications

References 28 publications

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Evaluation Model of Rice Seedling Production Line Seeding Quality Based on Deep Learning

Detection of Gannan Navel Orange Ripeness in Natural Environment Based on YOLOv5-NMM

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