Remote estimation of grafted apple tree trunk diameter in modern orchard with RGB and point cloud based on SOLOv2

Sun, Xiaoming; Fang, Wentai; Gao, Changqing; Fu, Longsheng; Majeed, Yaqoob; Liu, Xiaojuan; Gao, Feng; Yang, Ruizhe; Li, Rui

doi:10.1016/j.compag.2022.107209

Cited by 22 publications

(9 citation statements)

References 38 publications

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“…This unique characteristic makes it a powerful tool with various applications in CV [ 32 , 33 ]. The choice of the SOLOv2 algorithm for pumpkin recognition in this study was driven by its distinct capabilities in precise object segmentation with fast inference speed [ 34 ]. This characteristic makes it particularly suitable for our goal of accurately segmenting the different components of pumpkins, namely the vines and fruits.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

VPBR: An Automatic and Low-Cost Vision-Based Biophysical Properties Recognition Pipeline for Pumpkin

Dang

Nadeem

Nguyen

et al. 2023

Plants

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Pumpkins are a nutritious and globally enjoyed fruit for their rich and earthy flavor. The biophysical properties of pumpkins play an important role in determining their yield. However, manual in-field techniques for monitoring these properties can be time-consuming and labor-intensive. To address this, this research introduces a novel approach that feeds high-resolution pumpkin images to train a mathematical model to automate the measurement of each pumpkin’s biophysical properties. Color correction was performed on the dataset using a color-checker panel to minimize the impact of varying light conditions on the RGB images. A segmentation model was then trained to effectively recognize two fundamental components of each pumpkin: the fruit and vine. Real-life measurements of various biophysical properties, including fruit length, fruit width, stem length, stem width and fruit peel color, were computed and compared with manual measurements. The experimental results on 10 different pumpkin samples revealed that the framework obtained a small average mean absolute percentage error (MAPE) of 2.5% compared to the manual method, highlighting the potential of this approach as a faster and more efficient alternative to conventional techniques for monitoring the biophysical properties of pumpkins.

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Section: Methodsmentioning

confidence: 99%

“…In comparison to other instance segmentation models like Mask R-CNN, SOLOv2 consistently achieves superior results across a range of evaluation metrics. Moreover, the model exhibits high efficiency, and it is characterized by a relatively low parameter count and fast inference speed, making it well-suited for real-world applications [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

VPBR: An Automatic and Low-Cost Vision-Based Biophysical Properties Recognition Pipeline for Pumpkin

Dang

Nadeem

Nguyen

et al. 2023

Plants

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“…Compared to traditional object detection and segmentation algorithms, a convolutional neural network (CNN) achieves a more robust and accurate performance due to its strong feature extraction ability and autonomous learning mechanism [20,21]. In recent years, there are some researchers applying various networks to detect or segment various objects from images in agricultural scenarios [21][22][23][24][25][26][27]. However, semantic segmentation can only segment images into different classes while lacking the capability of segmenting each object within the class [28].…”

Section: Introductionmentioning

confidence: 99%

A Refined Apple Binocular Positioning Method with Segmentation-Based Deep Learning for Robotic Picking

et al. 2023

Self Cite

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An apple-picking robot is now the most widely accepted method in the substitution of low-efficiency and high-cost labor-intensive apple harvesting. Although most current research on apple-picking robots works well in the laboratory, most of them are unworkable in an orchard environment due to unsatisfied apple positioning performance. In general, an accurate, fast, and widely used apple positioning method for an apple-picking robot remains lacking. Some positioning methods with detection-based deep learning reached an acceptable performance in some orchards. However, apples occluded by apples, leaves, and branches are ignored in these methods with detection-based deep learning. Therefore, an apple binocular positioning method based on a Mask Region Convolutional Neural Network (Mask R-CNN, an instance segmentation network) was developed to achieve better apple positioning. A binocular camera (Bumblebee XB3) was adapted to capture binocular images of apples. After that, a Mask R-CNN was applied to implement instance segmentation of apple binocular images. Then, template matching with a parallel polar line constraint was applied for the stereo matching of apples. Finally, four feature point pairs of apples from binocular images were selected to calculate disparity and depth. The trained Mask R-CNN reached a detection and segmentation intersection over union (IoU) of 80.11% and 84.39%, respectively. The coefficient of variation (CoV) and positioning accuracy (PA) of binocular positioning were 5.28 mm and 99.49%, respectively. The research developed a new method to fulfill binocular positioning with a segmentation-based neural network.

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“…Accurate and robust plant segmentation based on DL is a key technique in machine vision for measuring morphological indicators [18,19]. Compared to traditional plant segmentation methods, DL approaches exhibit significant advantages in various aspects, including speed and accuracy [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Research on Morphological Indicator Extraction Method of Pinus massoniana Lamb. Based on 3D Reconstruction

Li,

Xia,

Liu

et al. 2023

Forests

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Pinus massoniana (Lamb.) is an important plantation species in southern China. Accurate measurement of P. massoniana seedling morphological indicators is crucial for accelerating seedling quality assessment. Machine vision, with its objectivity and stability, can replace human eyes in performing these measurements. In this paper, a measurement method for seedling morphological indicators based on Euclidean distance, Laplacian contraction, PointNet++, and 3D reconstruction is proposed. Firstly, multi-angle sequence images of 30 one-year-old P. massoniana seedlings were collected, distorted, and corrected to generate a sparse point cloud through the Structure-from-Motion (SFM) and dense point cloud through the Patch-Based Multiple View Stereo (PMVS). Secondly, a Dense Weighted Semantic Segmentation Model based on PointNet++ was designed, achieving effective segmentation of the P. massoniana seedling point clouds. Finally, a multi-iteration plane method based on Laplacian contraction was proposed. The new skeleton points were refined by minimizing the Euclidean distance, iteratively generating the optimal morphological skeleton, thus facilitating the extraction of morphological indicators. The experimental results demonstrated a good correlation between the machine vision-extracted morphological indicators (including plant height, ground diameter, and height-to-diameter ratio) and manually measured data. The improved PointNet++ model achieved an accuracy of 0.9448 on the training set. The accuracy and Mean Intersection over Union (MIoU) of the test set reached 0.9430 and 0.7872, respectively. These findings can provide reliable technical references for the accurate assessment of P. massoniana seedling quality and the promotion of digital forestry construction.

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Remote estimation of grafted apple tree trunk diameter in modern orchard with RGB and point cloud based on SOLOv2

Cited by 22 publications

References 38 publications

VPBR: An Automatic and Low-Cost Vision-Based Biophysical Properties Recognition Pipeline for Pumpkin

VPBR: An Automatic and Low-Cost Vision-Based Biophysical Properties Recognition Pipeline for Pumpkin

A Refined Apple Binocular Positioning Method with Segmentation-Based Deep Learning for Robotic Picking

Research on Morphological Indicator Extraction Method of Pinus massoniana Lamb. Based on 3D Reconstruction

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