High-throughput measurement method for rice seedling based on improved UNet model

Liu, Sicheng; Huang, Ze; Xu, Zhihui; Zhao, Fujun; Xiong, Dongliang; Peng, Shaobing; Huang, Jianliang

doi:10.1016/j.compag.2024.108770

Computers and Electronics in Agriculture

2024

DOI: 10.1016/j.compag.2024.108770

|View full text |Cite

High-throughput measurement method for rice seedling based on improved UNet model

Sicheng Liu,

Ze Huang,

Zhihui Xu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A deep learning-based method for estimating the main stem length of sweet potato seedlings

Mao,

Liu,

Luo

2024

Measurement

View full text Add to dashboard Cite

A deep learning-based method for estimating the main stem length of sweet potato seedlings

Mao,

Liu,

Luo

2024

Measurement

View full text Add to dashboard Cite

An efficient deep learning model for paddy growth stage classification using neural network pruning on UAV images

Ramachandran,

Kumar K S

2024

Eng. Res. Express

View full text Add to dashboard Cite

Crop phenology has a vital role in sustainable agriculture, facilitating farmers to make informed decisions throughout the crop-growing season. The traditional method of phenological detection relies on vegetation index calculations and time-series data, which can be extremely costly and difficult to obtain. In contrast, deep learning algorithms can estimate phenological stages directly from images, overcoming Vegetative Index (VI)-based limitations. Unmanned Aerial Vehicles (UAVs) offer high spatial and temporal resolution images at low cost, making them suitable for frequent field monitoring. This study focuses on the classification of rice seedling growth stages using deep learning techniques from images captured by UAVs. The proposed PaddyTrimNet model incorporates neural network pruning to classify paddy growth stages efficiently based on the BBCH (Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie) scale. It focuses on the BBCH11, BBCH12, and BBCH13 using UAV images. PaddyTrimNet is an architecture based on ResNet50 modified specifically to classify rice development stages, incorporating separable convolutional layers to reduce parameters. The model is pruned using the Layer-wise Relevance Propagation method to enhance efficiency without compromising performance. It has demonstrated superior performance in paddy growth stage classification, achieving an accuracy of 96.97% while utilizing only 48.18 MFLOPS. It surpasses the existing pretrained deep learning classification models in terms of both accuracy and computational efficiency. This study contributes to precision agriculture and sustainable farming practices by leveraging deep learning and UAV imagery.

show abstract

U-Net with Coordinate Attention and VGGNet: A Grape Image Segmentation Algorithm Based on Fusion Pyramid Pooling and the Dual-Attention Mechanism

Yi,

Zhou,

et al. 2024

Agronomy

View full text Add to dashboard Cite

Currently, the classification of grapevine black rot disease relies on assessing the percentage of affected spots in the total area, with a primary focus on accurately segmenting these spots in images. Particularly challenging are cases in which lesion areas are small and boundaries are ill-defined, hampering precise segmentation. In our study, we introduce an enhanced U-Net network tailored for segmenting black rot spots on grape leaves. Leveraging VGG as the U-Net’s backbone, we strategically position the atrous spatial pyramid pooling (ASPP) module at the base of the U-Net to serve as a link between the encoder and decoder. Additionally, channel and spatial dual-attention modules are integrated into the decoder, alongside a feature pyramid network aimed at fusing diverse levels of feature maps to enhance the segmentation of diseased regions. Our model outperforms traditional plant disease semantic segmentation approaches like DeeplabV3+, U-Net, and PSPNet, achieving impressive pixel accuracy (PA) and mean intersection over union (MIoU) scores of 94.33% and 91.09%, respectively. Demonstrating strong performance across various levels of spot segmentation, our method showcases its efficacy in enhancing the segmentation accuracy of black rot spots on grapevines.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

High-throughput measurement method for rice seedling based on improved UNet model

Cited by 5 publications

References 34 publications

A deep learning-based method for estimating the main stem length of sweet potato seedlings

A deep learning-based method for estimating the main stem length of sweet potato seedlings

An efficient deep learning model for paddy growth stage classification using neural network pruning on UAV images

U-Net with Coordinate Attention and VGGNet: A Grape Image Segmentation Algorithm Based on Fusion Pyramid Pooling and the Dual-Attention Mechanism

Contact Info

Product

Resources

About