ANN approach for estimation of cow weight depending on photogrammetric body dimensions

Taşdemir, Şakir; Özkan, İlker Ali

doi:10.26833/ijeg.427531

Cited by 17 publications

(8 citation statements)

References 28 publications

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“…Eight years later, Tasdemir and Ozkan (2019) used an artificial neural network (ANN) to estimate Holstein cows’ BW using the same type of measurements obtained from digital images as in their previous work. Different ANN model architectures were generated, and the best performing model improved on their previous results leading to a correlation coefficient R equal to 0.99.…”

Section: And CV Methods For Bw Predictionmentioning

confidence: 99%

“…While animal biometrics is an emerging field focused on quantification and detection of the phenotypic appearance of species, individuals, behaviors, and morphological traits ( Kühl and Burghardt, 2013 ), animal morphometrics ( Rohlf, 1990 ; Adams et al, 2004 ; Doyle et al, 2018 ) is almost exclusively focused on landmark-based methods (and less on outline-based methods) using quantitative analysis of form relying on measuring the size and shape of animals, and the relation between size and shape (allometry). Estimation of livestock BW using biometric and morphometric measurements has been studied in detail for various species, such as cattle ( Taşdemir et al, 2011a , b ; Miller et al, 2019 ; Tasdemir and Ozkan, 2019 ; Gjergji et al, 2020 ; de Moraes Weber et al, 2020 ; Rudenko, 2020 ), pigs ( Brandl and Jørgensen, 1996 ; O’Connell et al, 2007 ; Mutua et al, 2011 ; Sungirai et al, 2014 ; Al Ard Khanji et al, 2018 ), sheep ( Eyduran et al, 2015 ; Huma and Iqbal, 2019 ), goats ( Sebolai et al, 2012 ; Eyduran et al, 2017 ; Temoso et al, 2017 ), camels ( Fadlelmoula et al, 2020 ; de Moraes Weber et al, 2020 ), yaks ( Yan et al, 2019 ), poultry ( Mendeş and Akkartal, 2009 ), and fish ( Fernandes et al, 2020b ). This process is typically applied to avoid drawbacks associated with manually performed individual animal weighing such as: 1) the animal and manual laborer stress associated with animal relocation, 2) the costs associated with this labor-intensive process, and 3) the significant cost associated with acquiring and maintaining industrial scales.…”

Section: Biometric and Morphometric Measurements For Bw Predictionmentioning

confidence: 99%

“…Since feature selection can be a tedious task particularly when the number of morphometric measurements is large, it is preferable to automate the process. Therefore, the third approach (CV+ML approach) includes systems that use CV techniques as described in the CV approach and machine learning (ML) methods for feature selection automation ( Tasdemir and Ozkan, 2019 ; de Moraes Weber et al, 2020 ; Rudenko, 2020 ). Both, the CV approach and the CV+ML approach, enlist some level of manual operations, such as image and feature selection, image segmentation, and morphometric measurement extractions.…”

Section: Introductionmentioning

confidence: 99%

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ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Wang

Shadpour

Chan

et al. 2021

Journal of Animal Science

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Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.

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Section: And CV Methods For Bw Predictionmentioning

confidence: 99%

Section: Biometric and Morphometric Measurements For Bw Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Wang

Shadpour

Chan

et al. 2021

Journal of Animal Science

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“…An approach in which ANN was used to estimate the weight of animals depending on the photogrammetric size of their body has turned out to be more effective [19]. Photogrammetry is a technology for obtaining quantitative and qualitative information on the natural and industrial characteristics of an object under study using photographic or non-photographic images [20].…”

Section: The Study Materials and Methodsmentioning

confidence: 99%

Breed recognition and estimation of live weight of cattle based on methods of machine learning and computer vision

Bezsonov

Lebediev

et al. 2021

EEJET

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A method of measuring cattle parameters using neural network methods of image processing was proposed. To this end, several neural network models were used: a convolutional artificial neural network and a multilayer perceptron. The first is used to recognize a cow in a photograph and identify its breed followed by determining its body dimensions using the stereopsis method. The perceptron was used to estimate the cow's weight based on its breed and size information. Mask RCNN (Mask Regions with CNNs) convolutional network was chosen as an artificial neural network. To clarify information on the physical parameters of animals, a 3D camera (Intel RealSense D435i) was used. Images of cows taken from different angles were used to determine the parameters of their bodies using the photogrammetric method. The cow body dimensions were determined by analyzing animal images taken with synchronized cameras from different angles. First, a cow was identified in the photograph and its breed was determined using the Mask RCNN convolutional neural network. Next, the animal parameters were determined using the stereopsis method. The resulting breed and size data were fed to a predictive model to determine the estimated weight of the animal. When modeling, Ayrshire, Holstein, Jersey, Krasnaya Stepnaya breeds were considered as cow breeds to be recognized. The use of a pre-trained network with its subsequent training applying the SGD algorithm and Nvidia GeForce 2080 video card has made it possible to significantly speed up the learning process compared to training in a CPU. The results obtained confirm the effectiveness of the proposed method in solving practical problems.

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“…Recent progress in the field of computer vision (CV) indicates that, with the help of sufficient computing power and large training datasets (Cordts et al, 2016;Deng et al, 2009;Everingham et al, 2010;Lin et al, 2014), deep learning methods such as Convolutional Neural Networks (CNNs) (LeCun et al, 1989) can considerably improve the performance of object detection and segmentation tasks from high-resolution imagery (He et al, 2016;Krizhevsky et al, 2012). Neural networks can deal with complex problems to reach accurate solutions (Tasdemir & Ozkan, 2019). This situation strongly indicates that deep learning will play a critical role in promoting the accuracy of building segmentation toward practical applications of automatic mapping of features (Chen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Feature extraction from satellite images using segnet and fully convolutional networks (FCN)

Sarıtürk¹,

Bayram²,

Duran³

et al. 2020

International Journal of Engineering and Geosciences

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Object detection and classification are among the most popular topics in Photogrammetry and Remote Sensing studies. With technological developments, a large number of high-resolution satellite images have been obtained and it has become possible to distinguish many different objects. Despite all these developments, the need for human intervention in object detection and classification is seen as one of the major problems. Machine learning has been used as a priority option to this day to reduce this need. Although success has been achieved with this method, human intervention is still needed. Deep learning provides a great convenience by eliminating this problem. Deep learning methods carry out the learning process on raw data unlike traditional machine learning methods. Although deep learning has a long history, the main reasons for its increased popularity in recent years are; the availability of sufficient data for the training proce ss and the availability of hardware to process the data. In this study, a performance comparison was made between two different convolutional neural network architectures (SegNet and Fully Convolutional Networks (FCN)) which are used for object segmentation and classification on images. These two different models were trained using the same training dataset and their performances have been evaluated using the same test dataset. The results show that, for building segmentation, there is not much significant difference between these two architectures in terms of accuracy, but FCN architecture is more successful than SegNet by 1%. However, this situation may vary according to the dataset used during the training of the system.

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ANN approach for estimation of cow weight depending on photogrammetric body dimensions

Cited by 17 publications

References 28 publications

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Breed recognition and estimation of live weight of cattle based on methods of machine learning and computer vision

Feature extraction from satellite images using segnet and fully convolutional networks (FCN)

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