Fusing vegetation index and ridge segmentation for robust vision based autonomous navigation of agricultural robots in vegetable farms

Wang, Shuo; Su, Daobilige; Jiang, Yiyu; Tan, Yu; Qiao, Yongliang; Yang, Shenghui; Feng, Yuliang; Hu, Nan

doi:10.1016/j.compag.2023.108235

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…One of the most fundamental uses of remote sensing data in the field of agriculture management is mapping and monitoring farmland information. Field ridges are used in farming information to divide farmland into several crop zones and assist farmers in planning and managing their crops logically ( Li et al., 2020 ; S. Wang et al., 2023 ). In wheat breeding, the division of ridges can help control the spread of pests and diseases and cross-contamination ( Jiaguo et al., 2023 ), and the reasonable distribution of ridges can help provide crops with appropriate moisture and temperature to improve crop yield and quality ( Zhang et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

A segmentation network for farmland ridge based on encoder-decoder architecture in combined with strip pooling module and ASPP

Hong,

Zhu,

Liu

et al. 2024

Front. Plant Sci.

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In order to effectively support wheat breeding, farmland ridge segmentation can be used to visualize the size and spacing of a wheat field. At the same time, accurate ridge information collecting can deliver useful data support for farmland management. However, in the farming ridge segmentation scenarios based on remote sensing photos, the commonly used semantic segmentation methods tend to overlook the ridge edges and ridge strip features, which impair the segmentation effect. In order to efficiently collect ridge information, this paper proposes a segmentation method based on encoder-decoder of network with strip pooling module and ASPP module. First, in order to extract context information for multi-scale features, ASPP module are integrated in the deepest feature map. Second, the remote dependence of the ridge features is improved in both horizontal and vertical directions by using the strip pooling module. The final segmentation map is generated by fusing the boundary features and semantic features using an encoder and decoder architecture. As a result, the accuracy of the proposed method in the validation set is 98.0% and mIoU is 94.6%. The results of the experiments demonstrate that the method suggested in this paper can precisely segment the ridge information, as well as its value in obtaining data on the distribution of farmland and its potential for practical application.

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Section: Introductionmentioning

confidence: 99%

A segmentation network for farmland ridge based on encoder-decoder architecture in combined with strip pooling module and ASPP

Hong,

Zhu,

Liu

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Due to the dynamic characteristics of paddy fields, such as uneven terrain, changing weed conditions, varying weather conditions, and fluctuating lighting, traditional image processing methods lack the robustness to cope with the complex and ever-changing environment of paddy fields and advanced machine learning algorithms need to be considered [13]. Convolutional neural networks, with strong feature-learning ability, can be effectively used for the extraction of features using image spatial information [14,15], and good results have been achieved in terms of the segmentation of farmland areas [16,17]. In recent years, image segmentation methods based on deep neural networks have been widely used for farmland image segmentation and boundary extraction from UAV remote sensing images [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

AM-UNet: Field Ridge Segmentation of Paddy Field Images Based on an Improved MultiResUNet Network

Wu,

Fang,

Liu

et al. 2024

Agriculture

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In order to solve the problem of image boundary segmentation caused by the irregularity of paddy fields in southern China, a high-precision segmentation method based on the improved MultiResUNet model for paddy field mapping is proposed, combining the characteristics of paddy field scenes. We introduce the attention gate (AG) mechanism at the end of the encoder–decoder skip connections in the MultiResUNet model to generate the weights and highlight the response of the field ridge area, add an atrous spatial pyramid pooling (ASPP) module after the end of the encoder down-sampling, use an appropriate combination of expansion rates to improve the identification of small-scale edge details, use 1 × 1 convolution to improve the range of the sensory field after bilinear interpolation to increase the segmentation accuracy, and, thus, construct the AM-UNet paddy field ridge segmentation model. The experimental results show that the IoU, precision, and F1 value of the AM-UNet model are 88.74%, 93.45%, and 93.95%, respectively, and that inference time for a single image is 168ms, enabling accurate and real-time segmentation of field ridges in a complex paddy field environment. Thus, the AM-UNet model can provide technical support for the development of vision-based automatic navigation systems for agricultural machines.

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Efficient extraction of corn rows in diverse scenarios: A grid-based selection method for intelligent classification

Quan,

Guo,

Huang

et al. 2024

Computers and Electronics in Agriculture

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Fusing vegetation index and ridge segmentation for robust vision based autonomous navigation of agricultural robots in vegetable farms

Cited by 5 publications

References 34 publications

A segmentation network for farmland ridge based on encoder-decoder architecture in combined with strip pooling module and ASPP

A segmentation network for farmland ridge based on encoder-decoder architecture in combined with strip pooling module and ASPP

AM-UNet: Field Ridge Segmentation of Paddy Field Images Based on an Improved MultiResUNet Network

Efficient extraction of corn rows in diverse scenarios: A grid-based selection method for intelligent classification

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