Cattle Race Classification Using Gray Level Co-occurrence Matrix Convolutional Neural Networks

Santoni, Mayanda Mega; Sensuse, Dana Indra; Arymurthy, Aniati Murni; Fanany, Mohamad Ivan

doi:10.1016/j.procs.2015.07.525

Cited by 49 publications

(30 citation statements)

References 11 publications

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“…Other operations involved the creation of bounding boxes (Chen, et al, 2017), (Sa, et al, 2016), (McCool, Perez, & Upcroft, 2017), (Milioto, Lottes, & Stachniss, 2017) to facilitate detection of weeds or counting of fruits. Some datasets were converted to grayscale (Santoni, Sensuse, Arymurthy, & Fanany, 2015), (Amara, Bouaziz, & Algergawy, 2017) or to the HSV color model (Luus, Salmon, van den Bergh, & Maharaj, 2015), (Lee, Chan, Wilkin, & Remagnino, 2015).…”

Section: Data Pre-processingmentioning

confidence: 99%

“…Furthermore, some papers used features extracted from the images as input to their models, such as shape and statistical features (Hall, McCool, Dayoub, Sunderhauf, & Upcroft, 2015), histograms (Hall, McCool, Dayoub, Sunderhauf, & Upcroft, 2015), (Xinshao & Cheng, 2015), (Rebetez, J., et al, 2016), Principal Component Analysis (PCA) filters (Xinshao & Cheng, 2015), Wavelet transformations (Kuwata & Shibasaki, 2015) and Gray Level Co-occurrence Matrix (GLCM) features (Santoni, Sensuse, Arymurthy, & Fanany, 2015). Satellite or aerial images involved a combination of pre-processing steps such as orthorectification (Lu, et al, 2017), (Minh, et al, 2017) calibration and terrain correction (Kussul, Lavreniuk, Skakun, & Shelestov, 2017), (Minh, et al, 2017) and atmospheric correction (Rußwurm & Körner, 2017).…”

Section: Data Pre-processingmentioning

confidence: 99%

“…CNN also seem to be superior than unsupervised feature learning with 3-11% higher CA (Luus, Salmon, van den Bergh, & Maharaj, 2015), 2-44% improved CA in relation to local shape and color features (Dyrmann, Karstoft, & Midtiby, 2016 ), (Sørensen, Rasmussen, Nielsen, & Jørgensen, 2017), and 2% better CA (Kussul, Lavreniuk, Skakun, & Shelestov, 2017) or 18% less RMSE (Song, et al, 2016) compared to multilayer perceptrons. CNN had also superior performance than Penalized Discriminant Analysis (Grinblat, Uzal, Larese, & Granitto, 2016), SVM Regression (Kuwata & Shibasaki, 2015), area-based techniques (Rahnemoonfar & Sheppard, 2017), texturebased regression models (Chen, et al, 2017), LMC classifiers (Xinshao & Cheng, 2015), Gaussian Mixture Models (Santoni, Sensuse, Arymurthy, & Fanany, 2015) and Naïve-Bayes classifiers (Yalcin, 2017 ).…”

Section: Performance Comparison With Other Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning in agriculture: A survey

Kamilaris

Prenafeta-Boldú

2018

Computers and Electronics in Agriculture

2,860

1,147

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Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study.Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

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Section: Data Pre-processingmentioning

confidence: 99%

Section: Data Pre-processingmentioning

confidence: 99%

Section: Performance Comparison With Other Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning in agriculture: A survey

Kamilaris

Prenafeta-Boldú

2018

Computers and Electronics in Agriculture

2,860

1,147

View full text Add to dashboard Cite

show abstract

“…Furthermore, the majority of related work included some image pre-processing steps, where each image in the data set was reduced to a smaller size, before being used as input to the model, such as 256 × 256, 128 × 128, 96 × 96, 60 × 60 pixels, or converted to greyscale (Santoni et al ., 2015). Most of the studies divided their data randomly between training and testing/verification sets, using a ratio of 80 : 20 or 90 : 10, respectively.…”

Section: Convolutional Neural Network Applications In Agriculturementioning

confidence: 99%

A review of the use of convolutional neural networks in agriculture

2018

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Deep learning (DL) constitutes a modern technique for image processing, with large potential. Having been successfully applied in various areas, it has recently also entered the domain of agriculture. In the current paper, a survey was conducted of research efforts that employ convolutional neural networks (CNN), which constitute a specific class of DL, applied to various agricultural and food production challenges. The paper examines agricultural problems under study, models employed, sources of data used and the overall precision achieved according to the performance metrics used by the authors. Convolutional neural networks are compared with other existing techniques, and the advantages and disadvantages of using CNN in agriculture are listed. Moreover, the future potential of this technique is discussed, together with the authors’ personal experiences after employing CNN to approximate a problem of identifying missing vegetation from a sugar cane plantation in Costa Rica. The overall findings indicate that CNN constitutes a promising technique with high performance in terms of precision and classification accuracy, outperforming existing commonly used image-processing techniques. However, the success of each CNN model is highly dependent on the quality of the data set used.

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“…Within these, Demmers et al [17,18] have developed first order DRNN (Differential Recurrent Neural Networks) models to control and predict growth of pigs and broiler chickens (by estimating their weight) using field sensory data and a combination of static and dynamic environmental variables. Santoni et al [19] have built a CNN model to classify cattles into 5 different races using grayscale images.…”

Section: Introductionmentioning

confidence: 99%

Estimating Body Condition Score in Dairy Cows From Depth Images Using Convolutional Neural Networks, Transfer Learning and Model Ensembling Techniques

et al. 2019

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BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are associated with milk production, reproduction, and health of cows. However, in practice, obtaining BCS values is a time-consuming and subjective task performed visually by expert scorers. There have been several efforts to automate BCS of dairy cows by using image analysis and machine learning techniques. In a previous work, an automatic system to estimate BCS values was proposed, which is based on Convolutional Neural Networks (CNNs). In this paper we significantly extend the techniques exploited by that system via using transfer learning and ensemble modeling techniques to further improve BCS estimation accuracy. The improved system has achieved good estimations results in comparison with the base system. Overall accuracy of BCS estimations within 0.25 units of difference from true values has increased 4% (up to 82%), while overall accuracy within 0.50 units has increased 3% (up to 97%).

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Cattle Race Classification Using Gray Level Co-occurrence Matrix Convolutional Neural Networks

Cited by 49 publications

References 11 publications

Deep learning in agriculture: A survey

Deep learning in agriculture: A survey

A review of the use of convolutional neural networks in agriculture

Estimating Body Condition Score in Dairy Cows From Depth Images Using Convolutional Neural Networks, Transfer Learning and Model Ensembling Techniques

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