Improved 3D point cloud segmentation for accurate phenotypic analysis of cabbage plants using deep learning and clustering algorithms

Guo, R.T.; Xie, Jilong; Zhu, Jianguo; Cheng, Rongjun; Zhang, Yi; Zhang, Xihai; Gong, Xinjing; Zhang, Ruwen; Wang, Hao; Meng, Fanfeng

doi:10.1016/j.compag.2023.108014

Cited by 14 publications

(2 citation statements)

References 58 publications

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“…The collected data contain some three-dimensional point cloud noise, attributed to occluders, equipment limitations, and external conditions [ 21 ]. This noise can compromise the accuracy of expressing useful information, which results in a large error in pig weight estimation.…”

Section: Methodsmentioning

confidence: 99%

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Liu,

Hua,

Xue

et al. 2023

Sensors

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The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig’s back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.

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Section: Methodsmentioning

confidence: 99%

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Liu,

Hua,

Xue

et al. 2023

Sensors

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“…Experimental results indicated an overall semantic segmentation accuracy of 97.07%, demonstrating the significant potential of deep learning-based point cloud segmentation methods in handling dense plant point clouds with complex morphological features. Guo et al [68] proposed a segmentation method for cabbage point cloud data by combining deep learning and clustering algorithms. This approach optimized the workflow of the DBSCAN algorithm and exhibited strong performance in organ-level plant point cloud segmentation experiments, achieving an accuracy of 95% and an IoU of 0.86.…”

Section: Neural Network-based 3d Point Cloud Segmentationmentioning

confidence: 99%

Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review

Ma,

Qi,

Sun

et al. 2024

Agriculture

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Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement.

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A fast high throughput plant phenotyping system using YOLO and Chan-Vese segmentation

Jain,

Ramesh,

Damodar Reddy

et al. 2024

Soft Comput

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Improved 3D point cloud segmentation for accurate phenotypic analysis of cabbage plants using deep learning and clustering algorithms

Cited by 14 publications

References 58 publications

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network

Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review

A fast high throughput plant phenotyping system using YOLO and Chan-Vese segmentation

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