Maize tassel area dynamic monitoring based on near-ground and UAV RGB images by U-Net model

Yu, Xun; Yin, Dameng; Nie, Chaoqun; Ming, Bo; Xu, Huilin; Liu, Yuan; Bai, Yi; Shao, Mingchao; Cheng, Minghan; Liu, Yadong; Liu, Shuaibing; Wang, Zixu; Wang, Siyu; Shi, Lei; Jin, Xiuliang

doi:10.1016/j.compag.2022.107477

Cited by 21 publications

(11 citation statements)

References 37 publications

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“…In contrast, refs. [39,40,43] directly use the VGG16 network as the encoding network for U-Net without simplifying VGG16, which also achieves better segmentation results but increases the width and depth of the network and requires a more optimal environment configuration to run the model. Chen et al [53] achieved the accurate segmentation of grains, branches, and straws in hybrid rice grain images by improving the U-Net model, but the improvement they made was still to make the model extract richer semantic information by increasing the depth of the model.…”

Section: Comparison Of the Overall Accuracy Of The Modelmentioning

confidence: 99%

“…VGG16 is a typical structure in the VGG network and is frequently used as a feature extraction network for U-Net since it is well-suited for classification and localization tasks. Yu et al [39] investigated the potential of the U-Net model to segment maize tassel, and the results showed that the segmentation accuracy of the U-Net model with VGG16 as the feature extraction network for tassels at the all the tasseling stages was better than that of U-Net model with MobileNet; Sugirtha et al [40] also confirmed that U-Net with the VGG16 encoder shows better performance than the ResNet-50 encoder when segmenting urban streets. In order to accomplish the reliable detection of navigation lines in different growth periods of potato, Yang et al [41] presented a fitting approach of feature midpoint modification and replaced the original U-Net's feature extraction structure with VGG16; Zou et al [42] proposed an image-enhancement method based on the random synthesis of "foreground" and "background", and reduced the number of convolutional layers in the U-Net model network, achieving the semantic segmentation of field weed images.…”

Section: Introductionmentioning

confidence: 99%

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An Improved U-Net Model Based on Multi-Scale Input and Attention Mechanism: Application for Recognition of Chinese Cabbage and Weed

Wang

Yao

et al. 2023

Sustainability

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The accurate spraying of herbicides and intelligent mechanical weeding operations are the main ways to reduce the use of chemical pesticides in fields and achieve sustainable agricultural development, and an important prerequisite for achieving these is to identify field crops and weeds accurately and quickly. To this end, a semantic segmentation model based on an improved U-Net is proposed in this paper to address the issue of efficient and accurate identification of vegetable crops and weeds. First, the simplified visual group geometry 16 (VGG16) network is used as the coding network of the improved model, and then, the input images are continuously and naturally down-sampled using the average pooling layer to create feature maps of various sizes, and these feature maps are laterally integrated from the network into the coding network of the improved model. Then, the number of convolutional layers of the decoding network of the model is cut and the efficient channel attention (ECA) is introduced before the feature fusion of the decoding network, so that the feature maps from the jump connection in the encoding network and the up-sampled feature maps in the decoding network pass through the ECA module together before feature fusion. Finally, the study uses the obtained Chinese cabbage and weed images as a dataset to compare the improved model with the original U-Net model and the current commonly used semantic segmentation models PSPNet and DeepLab V3+. The results show that the mean intersection over union and mean pixel accuracy of the improved model increased in comparison to the original U-Net model by 1.41 and 0.72 percentage points, respectively, to 88.96% and 93.05%, and the processing time of a single image increased by 9.36 percentage points to 64.85 ms. In addition, the improved model in this paper has a more accurate segmentation effect on weeds that are close to and overlap with crops compared to the other three comparison models, which is a necessary condition for accurate spraying and accurate weeding. As a result, the improved model in this paper can offer strong technical support for the development of intelligent spraying robots and intelligent weeding robots.

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Section: Comparison Of the Overall Accuracy Of The Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved U-Net Model Based on Multi-Scale Input and Attention Mechanism: Application for Recognition of Chinese Cabbage and Weed

Wang

Yao

et al. 2023

Sustainability

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“…The experiment conducted by Deb et al Utilized a dataset of over 2010 images from the plant phenotyping (CVPPP) and KOMATSUNA datasets. In another study, (6) examined the accuracy differences of the U-Net model when applied to images of various maize varieties, different stages of tasseling, and different spatial resolutions. In addition to the aforementioned techniques, object detection represents another crucial computer vision approach that enables the identification of objects in images or videos by drawing a bounding box around them.…”

Section: Introductionmentioning

confidence: 99%

Recognition and localization of maize leaves in RGB images based on Point-Line Net

Liu,

Chang,

Hou

et al. 2024

Preprint

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Plant phenotype detection plays a crucial role in understanding and studying plant biology, agriculture, and ecology. It involves the quantification and analysis of various physical traits and characteristics of plants, such as plant height, leaf shape, angle, number, and growth trajectory. By accurately detecting and measuring these phenotypic traits, researchers can gain insights into plant growth, development, stress tolerance, and the influence of environmental factors. Among these phenotypic information, the number of leaves and growth trajectory of the plant are more accessible. Nonetheless, obtaining these information is labor-intensive and financially demanding. With the rapid development of computer vision technology and artificial intelligence, using maize field images to fully analyze plant-related information such as growth trajectory and number of leaves can greatly eliminate repetitive labor work and enhance the efficiency of plant breeding. However, the application of deep learning methods still faces challenges due to the serious occlusion problem and complex background of field plant images. In this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN framework, to automatically recognize maize field images and determine the number and growth trajectory of leaves and roots. The experimental results demonstrate that the object detection accuracy (mAP) of our Point-Line Net can reach 81.5%.Moreover, to describe the position and growth of leaves and roots, we introduced a new lightweight "keypoint" detection branch that achieved 33.5 using our custom distance verification index. Overall, these findings provide valuable insights for future field plant phenotype detection, particularly for the datasets with dot and line annotations.

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“…The model aims to improve the accuracy and efficiency of winter wheat ear segmentation and achieves an F1 measurement of 87.25%. Yu et al (2022) proposed an Unmanned Aerial Vehicle (UAV) tassel image recognition algorithm based on the U-Net model. The algorithm combines lightweight and heavy extraction networks, striking a balance between accuracy and speed with a relative mean squared error RMSE of 4.4.…”

Section: Introductionmentioning

confidence: 99%

TasselLFANet: a novel lightweight multi-branch feature aggregation neural network for high-throughput image-based maize tassels detection and counting

et al. 2023

Front. Plant Sci.

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Accurately and rapidly counting the number of maize tassels is critical for maize breeding, management, and monitoring the growth stage of maize plants. With the advent of high-throughput phenotyping platforms and the availability of large-scale datasets, there is a pressing need to automate this task for genotype and phenotype analysis. Computer vision technology has been increasingly applied in plant science, offering a promising solution for automated monitoring of a large number of plants. However, the current state-of-the-art image algorithms are hindered by hardware limitations, which compromise the balance between algorithmic capacity, running speed, and overall performance, making it difficult to apply them in real-time sensing field environments. Thus, we propose a novel lightweight neural network, named TasselLFANet, with an efficient and powerful structure for accurately and efficiently detecting and counting maize tassels in high spatiotemporal image sequences. Our proposed approach improves the feature-learning ability of TasselLFANet by adopting a cross-stage fusion strategy that balances the variability of different layers. Additionally, TasselLFANet utilizes multiple receptive fields to capture diverse feature representations, and incorporates an innovative visual channel attention module to detect and capture features more flexibly and precisely. We conducted a series of comparative experiments on a new, highly informative dataset called MrMT, which demonstrate that TasselLFANet outperforms the latest batch of lightweight networks in terms of performance, flexibility, and adaptability, achieving an F1 measure value of 94.4%, a mAP.@5 value of 96.8%, and having only 6.0M parameters. Moreover, compared with the regression-based TasselNetV3-Seg† model, our proposed model achieves superior counting performance, with a mean absolute error (MAE) of 1.80, a root mean square error (RMSE) of 2.68, and a R2 of 0.99. The proposed model meets the accuracy and speed requirements of the vision system in maize tassel detection. Furthermore, our proposed method is reliable and unaffected by geographical changes, providing essential technical support for computerized counting in the field.

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Maize tassel area dynamic monitoring based on near-ground and UAV RGB images by U-Net model

Cited by 21 publications

References 37 publications

An Improved U-Net Model Based on Multi-Scale Input and Attention Mechanism: Application for Recognition of Chinese Cabbage and Weed

An Improved U-Net Model Based on Multi-Scale Input and Attention Mechanism: Application for Recognition of Chinese Cabbage and Weed

Recognition and localization of maize leaves in RGB images based on Point-Line Net

TasselLFANet: a novel lightweight multi-branch feature aggregation neural network for high-throughput image-based maize tassels detection and counting

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