An automatic splitting method for the adhesive piglets’ gray scale image based on the ellipse shape feature

Lu, Mingzhou; Yi, Xiaolan; Li, Kunquan; Liu, Longshen; Li, Yan; Ding, Yongqian; Lin, Xiangze; Yang, Xiu; Shen, Mingxia

doi:10.1016/j.compag.2015.11.008

Cited by 24 publications

(17 citation statements)

References 21 publications

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“…After the background removal, the target regions were identified to obtain interlacing canopies. Methods such as contour features, bump detection, and contour stripping [35,36] could not be used to identify interlacing tree canopies because the trees do not have regular shape. Figure 4 shows that intermingled trees would occupy more area on their respective rows than single trees.…”

Section: Interlacing Canopy Region Extractionmentioning

confidence: 99%

Interlacing Orchard Canopy Separation and Assessment using UAV Images

Cheng

Cheng³

et al. 2020

Remote Sensing

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To minimize pesticide dosage and its adverse environmental impact, Unmanned Aerial Vehicle (UAV) spraying requires precise individual canopy information. Branches from neighboring trees may overlap, preventing image-based artificial intelligence analysis from correctly identifying individual trees. To solve this problem, this paper proposes a segmentation and evaluation method for mingled fruit tree canopies with irregular shapes. To extract the individual trees from mingled canopies, the study fitted the projection curve distribution of the interlacing tree with Gaussian Mixture Model (GMM) and solved the matter of segmentation by estimating the GMM parameters. For the intermingling degree assessment, the Gaussian parameters were used to quantify the characteristics of the mingled fruit trees and then as the input for Extreme Gradient Boosting (XGBoost) model training. The proposed method was tested on the aerial images of cherry and apple trees. Results of the experiments show that the proposed method can not only accurately identify individual trees, but also estimate the intermingledness of the interlacing canopies. The root mean squares (R) of the over-segmentation rate (Ro) and under-segmentation rate (Ru) for individual trees counting were less than 10%. Moreover, the Intersection over Union (IoU), used to evaluate the integrity of a single canopy area, was greater than 88%. An 84.3% Accuracy (ACC) with a standard deviation of 1.2% was achieved by the assessment model. This method will supply more accurate data of individual canopy for spray volume assessments or other precision-based applications in orchards.

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Section: Interlacing Canopy Region Extractionmentioning

confidence: 99%

Interlacing Orchard Canopy Separation and Assessment using UAV Images

Cheng

Cheng³

et al. 2020

Remote Sensing

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“…Other methods can separate animals that are close together, but cannot be used for accurate extraction of animal foreground as the contour of the animal is either rough and incomplete or of a specific shape (e.g. the Otsu-based background subtraction proposed by Nasirahmadi et al (2015) and the method of merging fitted ellipses proposed by Lu et al (2016)). Thus, more work is required on this challenge.…”

Section: Image Analysismentioning

confidence: 99%

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

Norton

Chen

Larsen

et al. 2019

Animal

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Economic pressures continue to mount on modern-day livestock farmers, forcing them to increase herds sizes in order to be commercially viable. The natural consequence of this is to drive the farmer and the animal further apart. However, closer attention to the animal not only positively impacts animal welfare and health but can also increase the capacity of the farmer to achieve a more sustainable production. State-of-the-art precision livestock farming (PLF) technology is one such means of bringing the animals closer to the farmer in the facing of expanding systems. Contrary to some current opinions, it can offer an alternative philosophy to 'farming by numbers'. This review addresses the key technology-oriented approaches to monitor animals and demonstrates how image and sound analyses can be used to build 'digital representations' of animals by giving an overview of some of the core concepts of PLF tool development and value discovery during PLF implementation. The key to developing such a representation is by measuring important behaviours and events in the livestock buildings. The application of image and sound can realise more advanced applications and has enormous potential in the industry. In the end, the importance lies in the accuracy of the developed PLF applications in the commercial farming system as this will also make the farmer embrace the technological development and ensure progress within the PLF field in favour of the livestock animals and their well-being. ImplicationsThis work address the main approaches utilised in developing precision livestock farming tools. Precision livestock farming is an approach that enables the farmer with more objective information about the animal to make better choices about the sustainability of their production system. This paper demonstrates some of the key solutions and the approaches taken to develop technologies with sound and image analyses. †

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“…presented by commercial farms. Table 1 summarizes the topview-based pig monitoring results introduced recently [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. Two-dimensional gray-scale or color information has been used to detect a single pig in a pen or a specially built facility (i.e., in “constrained” environments) [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Two-dimensional gray-scale or color information has been used to detect a single pig in a pen or a specially built facility (i.e., in “constrained” environments) [ 9 , 10 , 11 ]. However, even with advanced techniques applied to 2D gray-scale or color information, it remains challenging to detect multiple pigs accurately in a “commercial” farm environment [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. For example, images from a gray-scale or RGB camera are affected by various illuminations in a pig pen.…”

Section: Introductionmentioning

confidence: 99%

Depth-Based Detection of Standing-Pigs in Moving Noise Environments

Kim

Chung

Choi

et al. 2017

Sensors

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In a surveillance camera environment, the detection of standing-pigs in real-time is an important issue towards the final goal of 24-h tracking of individual pigs. In this study, we focus on depth-based detection of standing-pigs with “moving noises”, which appear every night in a commercial pig farm, but have not been reported yet. We first apply a spatiotemporal interpolation technique to remove the moving noises occurring in the depth images. Then, we detect the standing-pigs by utilizing the undefined depth values around them. Our experimental results show that this method is effective for detecting standing-pigs at night, in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (i.e., 94.47%), even with severe moving noises occluding up to half of an input depth image. Furthermore, without any time-consuming technique, the proposed method can be executed in real-time.

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An automatic splitting method for the adhesive piglets’ gray scale image based on the ellipse shape feature

Cited by 24 publications

References 21 publications

Interlacing Orchard Canopy Separation and Assessment using UAV Images

Interlacing Orchard Canopy Separation and Assessment using UAV Images

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

Depth-Based Detection of Standing-Pigs in Moving Noise Environments

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