Improving U-net network for semantic segmentation of corns and weeds during corn seedling stage in field

Cui, Jiapeng; Tan, Feng; Bai, Nan; Fu, Yaping

doi:10.3389/fpls.2024.1344958

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…In comparison to traditional methods that utilize deep learning models to directly segment crops from weeds [20,24,25], the main advantages of the approach proposed in this paper are as follows:…”

Section: Image Segmentation Experiments and Analysismentioning

confidence: 99%

“…Sahin et al [24] used multispectral imaging and a CRF-enhanced U-Net model to segment weeds and crops, achieving a mIoU of 88.3% on a sunflower dataset, providing a feasible method for early weed detection. Cui et al [25] proposed a semantic segmentation network, RDS Unet, based on corn seedling fields built upon an improved U-net network. This network accurately recognizes weeds even under complex environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

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An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields

Wang,

Yao,

et al. 2024

Agriculture

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In response to the limitations of existing methods in differentiating between vegetables and all types of weeds in farmlands, a new image segmentation method is proposed based on the improved YOLOv7-tiny. Building on the original YOLOv7-tiny framework, we replace the CIoU loss function with the WIoU loss function, substitute the Leaky ReLU loss function with the SiLU activation function, introduce the SimAM attention mechanism in the neck network, and integrate the PConv convolution module into the backbone network. The improved YOLOv7-tiny is used for vegetable target detection, while the ExG index, in combination with the OTSU method, is utilized to obtain a foreground image that includes both vegetables and weeds. By integrating the vegetable detection results with the foreground image, a vegetable distribution map is generated. Subsequently, by excluding the vegetable targets from the foreground image using the vegetable distribution map, a single weed target is obtained, thereby achieving accurate segmentation between vegetables and weeds. The experimental results show that the improved YOLOv7-tiny achieves an average precision of 96.5% for vegetable detection, with a frame rate of 89.3 fps, Params of 8.2 M, and FLOPs of 10.9 G, surpassing the original YOLOv7-tiny in both detection accuracy and speed. The image segmentation algorithm achieves a mIoU of 84.8% and an mPA of 97.8%. This method can effectively segment vegetables and a variety of weeds, reduce the complexity of segmentation with good feasibility, and provide a reference for the development of intelligent plant protection robots.

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Section: Image Segmentation Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields

Wang,

Yao,

et al. 2024

Agriculture

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Study on Tobacco Plant Cross-Level Recognition in Complex Habitats in Karst Mountainous Areas Based on the U-Net Model

Li,

Yan,

Zhou

et al. 2024

J Indian Soc Remote Sens

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Weed Instance Segmentation from UAV ortho-mosaic Images based on Deep Learning

Lu,

2024

Preprint

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Weeds significantly impact agricultural production, and traditional weed control methods often harm soil health and the environment. This study aims to develop deep learning based segmentation models in identifying weeds in potato fields captured by Unmanned Aerial Vehicle (UAV) orthophotos and to explore the effects of weeds on potato yield. UAVs were used to collect RGB data from potato fields, flying at an altitude of 10m, with Real ESRGAN Super-Resolution (SR) enhancing image resolution. We applied the Segment Anything Model (SAM) to do semi-automatic annotation, followed by training the YOLOv8 and MASK-RCNN models for segmentation. Also we used ANOVA and linear regression to analyze the effects of weeds and nitrogen fertilizer on yield. Results showed that the detection accuracy mAP50 scores for YOLOv8 and Mask R-CNN were 0.902 and 0.920, respectively, with the Real-ESRGAN-enhanced model achieving 0.909. When multiple weed types were present, accuracy decreased to 0.86. The linear regression model, incorporating plant and weed coverage areas, explained 41.2% of yield variation (R2 = 0.412, p-value = 0.01). Both YOLOv8 and Mask R-CNN achieved high accuracy, with YOLOv8 converging faster. Real-ESRGAN reconstruction slightly improved accuracy. Different nitrogen fertilizer treatments had no significant effect on yield, while weed control treatments significantly impacted yield, showing the importance of precise weed mapping. This study provides insights into weed segmentation and contributes to environmentally friendly precision weed control.

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Improving U-net network for semantic segmentation of corns and weeds during corn seedling stage in field

Cited by 3 publications

References 44 publications

An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields

An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields

Study on Tobacco Plant Cross-Level Recognition in Complex Habitats in Karst Mountainous Areas Based on the U-Net Model

Weed Instance Segmentation from UAV ortho-mosaic Images based on Deep Learning

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