Detection of sick broilers by digital image processing and deep learning

Zhuang, Xiaolin; Zhang, Tiemin

doi:10.1016/j.biosystemseng.2019.01.003

Cited by 118 publications

(70 citation statements)

References 19 publications

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“…The drive towards reduced FCR motivates farmers to monitor the performance better and understand the development of their animals. Over the past decades, a variety of classification and detection methods have been developed in poultry farming including acoustic resonance [12][13][14][15][16], robotics [17], remote sensing [18], Wireless Sensor Networks (WSNs) [19][20][21][22][23][24][25][26], and computer vision . It should be noted that this review highlights on the computer vision component in poultry farming.…”

Section: Introductionmentioning

confidence: 99%

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

et al. 2021

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Section: Introductionmentioning

confidence: 99%

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

et al. 2021

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“…Shen et al [4] have applied a faster R-CNN [5] framework with improved Inception-V3 [6] to detect stored-grain insects under field condition with impurities. The same feature extractor network and SSD [7] model have been utilized by Zhuang et al [8] to evaluate the health status of farm broilers.…”

Section: Introductionmentioning

confidence: 99%

Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network

Xing

Lee

2019

Sensors

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Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision technology, using convolutional neural network (CNN) models to identify pests and diseases has become a recent trend in the field of agriculture. However, many researchers refer to pre-trained models of ImageNet to execute different recognition tasks without considering their own dataset scale, resulting in a waste of computational resources. In this paper, a simple but effective CNN model was developed based on our image dataset. The proposed network was designed from the aspect of parameter efficiency. To achieve this goal, the complexity of cross-channel operation was increased and the frequency of feature reuse was adapted to network depth. Experiment results showed that Weakly DenseNet-16 got the highest classification accuracy with fewer parameters. Because this network is lightweight, it can be used in mobile devices.

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“…The most common technique used to detect the occurrences of these poultry diseases is visual observation and sound distinction [7]. However, this technique is timeconsuming, subjective, labor-intensive, and often fails to provide early detection [8]. With the development of computer vision systems, computerized disease diagnosis and detection of sick birds have been reported in several studies.…”

Section: Introductionmentioning

confidence: 99%

“…Zhuang et al [7] performed a skeleton analysis for early detection of sick broilers by image processing. Additionally, Zhuang and Zhang [8] reported on a sick broiler detector based on deep learning techniques. However, the analysis of chicken droppings by image processing and deep learning for sick bird detection has not been reported in any literature.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, CNN has also been applied in several areas in smart agriculture. Zhuang and Zhang [8] detected the sick broiler by CNN based on the Improved Feature Fusion Single Shot Multibox Detector (IFFSSD), and Zheng et al [15] applied SBDA-DL for the real-time detection of sow behaviors. Tian et al [16] used YOLO-V3 to detect apples in orchards during different growth cycles.…”

Section: Introductionmentioning

confidence: 99%

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Recognition and Classification of Broiler Droppings Based on Deep Convolutional Neural Network

et al. 2019

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Digestive diseases are one of the common broiler diseases that significantly affect production and animal welfare in broiler breeding. Droppings examination and observation are the most precise techniques to detect the occurrence of digestive disease infections in birds. This study proposes an automated broiler digestive disease detector based on a deep Convolutional Neural Network model to classify fine-grained abnormal broiler droppings images as normal and abnormal (shape, color, water content, and shape&water). Droppings images were collected from 10,000 25-35-day-old Ross broiler birds reared in multilayer cages with automatic droppings conveyor belts. For comparative purposes, Faster R-CNN and YOLO-V3 deep Convolutional Neural Networks were developed. The performance of YOLO-V3 was improved by optimizing the anchor box. Faster R-CNN achieved 99.1% recall and 93.3% mean average precision, while YOLO-V3 achieved 88.7% recall and 84.3% mean average precision on the testing data set. The proposed detector can provide technical support for the detection of digestive diseases in broiler production by automatically and nonintrusively recognizing and classifying chicken droppings.

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Detection of sick broilers by digital image processing and deep learning

Cited by 118 publications

References 19 publications

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network

Recognition and Classification of Broiler Droppings Based on Deep Convolutional Neural Network

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