Appearance quality classification method of Huangguan pear under complex background based on instance segmentation and semantic segmentation

Zhang, Yuhang; Shi, Nan; Zhang, Hao; Zhang, Jun; Fan, Xiaofei; Suo, Xuesong

doi:10.3389/fpls.2022.914829

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…In the segmentation evaluation method, the commonly used segmentation networks include DeepLabV3+ , U-Net, PSPNet and Mask R-CNN. For example, Zhang et al 74 used the three-stage method to classify "Huangguan" pears. In the first stage, Mask R-CNN was used to segment "Huangguan" pears from complex backgrounds, and in the second stage, DeepLabV3+ , U-Net and PSPNet were used to segment the "Huangguan" pear spot, and the ratio of the spot area to the pixel area of the "Huangguan" pear was calculated, which was divided into three levels.…”

Section: Application Of Cnn In Plant Disease Severity Assessmentmentioning

confidence: 99%

Recent advances in plant disease severity assessment using convolutional neural networks

Shi

Liu

Zheng

et al. 2023

Sci Rep

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In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be clarified. In recent years, deep learning for plant disease species identification has been widely used. In particular, the application of convolutional neural network (CNN) to plant disease images has made breakthrough progress. However, there are relatively few studies on disease severity assessment. The group first traced the prevailing views of existing disease researchers to provide criteria for grading the severity of plant diseases. Then, depending on the network architecture, this study outlined 16 studies on CNN-based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures and CNN-based segmentation networks, and provided a detailed comparative analysis of the advantages and disadvantages of each. Common methods for acquiring datasets and performance evaluation metrics for CNN models were investigated. Finally, this study discussed the major challenges faced by CNN-based plant disease severity assessment methods in practical applications, and provided feasible research ideas and possible solutions to address these challenges.

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Section: Application Of Cnn In Plant Disease Severity Assessmentmentioning

confidence: 99%

Recent advances in plant disease severity assessment using convolutional neural networks

Shi

Liu

Zheng

et al. 2023

Sci Rep

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“…Appearance quality classification method for Huangguan pear under complex backgrounds based on instance segmentation and semantic segmentation [24] Accurate identification of diseased Crown pears…”

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confidence: 99%

ECLPOD: An Extremely Compressed Lightweight Model for Pear Object Detection in Smart Agriculture

et al. 2023

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Accurate pear sorting plays a crucial role in ensuring the quality of pears and increasing the sales of them. In the domain of intelligent pear sorting, precise target detection of pears is imperative. However, practical implementation faces challenges in achieving adequate accuracy in pear target detection due to the limitations of computational resources in embedded devices and the occurrence of occlusion among pears. To solve this problem, we built an image acquisition system based on pear sorting equipment and created a pear dataset containing 34,598 pear images under laboratory conditions. The dataset was meticulously annotated using the LabelImg software, resulting in a total of 154,688 precise annotations for pears, pear stems, pear calyxes, and pear defects. Furthermore, we propose an Extremely Compressed Lightweight Model for Pear Object Detection (ECLPOD) based on YOLOv7’s pipeline to assist in the pear sorting task. Firstly, the Hierarchical Interactive Shrinking Network (HISNet) was proposed, which contributed to efficient feature extraction with a limited amount of computation and parameters. The Bulk Feature Pyramid (BFP) module was then proposed to enhance pear contour information extraction during feature fusion. Finally, the Accuracy Compensation Strategy (ACS) was proposed to improve the detection capability of the model, especially for identification of the calyces and stalks of pears. The experimental results indicate that the ECLPOD achieves 90.1% precision (P) and 85.52% mAP50 with only 0.58 million parameters and 1.3 GFLOPs of computation in the homemade pear dataset in this paper. Compared with YOLOv7, the number of parameters and the amount of computation for the ECLPOD are compressed to 1.5% and 1.3%, respectively. Compared with other mainstream methods, the ECLPOD achieves an optimal trade-off between accuracy and complexity. This suggests that the ECLPOD is superior to these existing approaches in the field of object detection for assisting pear sorting tasks with good potential for embedded device deployment.

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YOLOv9s-Pear: A Lightweight YOLOv9s-Based Improved Model for Young Red Pear Small-Target Recognition

Shi,

Duan,

Qing

et al. 2024

Agronomy

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With the advancement of computer vision technology, the demand for fruit recognition in agricultural automation is increasing. To improve the accuracy and efficiency of recognizing young red pears, this study proposes an improved model based on the lightweight YOLOv9s, termed YOLOv9s-Pear. By constructing a feature-rich and diverse image dataset of young red pears and introducing spatial-channel decoupled downsampling (SCDown), C2FUIBELAN, and the YOLOv10 detection head (v10detect) modules, the YOLOv9s model was enhanced to achieve efficient recognition of small targets in resource-constrained agricultural environments. Images of young red pears were captured at different times and locations and underwent preprocessing to establish a high-quality dataset. For model improvements, this study integrated the general inverted bottleneck blocks from C2f and MobileNetV4 with the RepNCSPELAN4 module from the YOLOv9s model to form the new C2FUIBELAN module, enhancing the model’s accuracy and training speed for small-scale object detection. Additionally, the SCDown and v10detect modules replaced the original AConv and detection head structures of the YOLOv9s model, further improving performance. The experimental results demonstrated that the YOLOv9s-Pear model achieved high detection accuracy in recognizing young red pears, while reducing computational costs and parameters. The detection accuracy, recall, mean precision, and extended mean precision were 0.971, 0.970, 0.991, and 0.848, respectively. These results confirm the efficiency of the SCDown, C2FUIBELAN, and v10detect modules in young red pear recognition tasks. The findings of this study not only provide a fast and accurate technique for recognizing young red pears but also offer a reference for detecting young fruits of other fruit trees, significantly contributing to the advancement of agricultural automation technology.

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Appearance quality classification method of Huangguan pear under complex background based on instance segmentation and semantic segmentation

Cited by 3 publications

References 46 publications

Recent advances in plant disease severity assessment using convolutional neural networks

Recent advances in plant disease severity assessment using convolutional neural networks

ECLPOD: An Extremely Compressed Lightweight Model for Pear Object Detection in Smart Agriculture

YOLOv9s-Pear: A Lightweight YOLOv9s-Based Improved Model for Young Red Pear Small-Target Recognition

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