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

Wang, Jintao; Shen, Mingxia; Liu, Longshen; Xu, Yi; Okinda, Cedric

doi:10.1155/2019/3823515

Cited by 29 publications

(12 citation statements)

References 20 publications

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“…Recently, several studies have proposed machine learning methods as effective methods for detecting poultry diseases (Zhuang et al, 2018 ; Okinda et al, 2019 ; Wang et al, 2019 ). Wang et al ( 2019 ) proposed an automated broiler digestive disease detector based on deep Convolutional Neural Network models, Faster R-CNN and YOLO-V3, to classify fine-grained abnormal broiler droppings images as normal and abnormal. 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.…”

Section: Related Workmentioning

confidence: 99%

“…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. Wang et al ( 2019 )'s study contributes to the development of an automatic and non-contact model for identifying and classifying abnormal droppings in broilers suffering from digestive disease; however, an effective solution is required for early detection of poultry diseases.…”

Section: Related Workmentioning

confidence: 99%

“…Deep learning methods have been demonstrated to automate the disease diagnostics procedures for both human and livestock (Quinn et al, 2016 ; Zhuang et al, 2018 ; Okinda et al, 2019 ; Wang et al, 2019 ; Yadav and Jadhav, 2019 ). They have outperformed traditional imaging techniques in diagnostics of malaria, tuberculosis and intestinal parasite (Quinn et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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Poultry diseases diagnostics models using deep learning

Machuve

Nwankwo

Mduma

et al. 2022

Front. Artif. Intell.

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Coccidiosis, Salmonella, and Newcastle are the common poultry diseases that curtail poultry production if they are not detected early. In Tanzania, these diseases are not detected early due to limited access to agricultural support services by poultry farmers. Deep learning techniques have the potential for early diagnosis of these poultry diseases. In this study, a deep Convolutional Neural Network (CNN) model was developed to diagnose poultry diseases by classifying healthy and unhealthy fecal images. Unhealthy fecal images may be symptomatic of Coccidiosis, Salmonella, and Newcastle diseases. We collected 1,255 laboratory-labeled fecal images and fecal samples used in Polymerase Chain Reaction diagnostics to annotate the laboratory-labeled fecal images. We took 6,812 poultry fecal photos using an Open Data Kit. Agricultural support experts annotated the farm-labeled fecal images. Then we used a baseline CNN model, VGG16, InceptionV3, MobileNetV2, and Xception models. We trained models using farm and laboratory-labeled fecal images and then fine-tuned them. The test set used farm-labeled images. The test accuracies results without fine-tuning were 83.06% for the baseline CNN, 85.85% for VGG16, 94.79% for InceptionV3, 87.46% for MobileNetV2, and 88.27% for Xception. Finetuning while freezing the batch normalization layer improved model accuracies, resulting in 95.01% for VGG16, 95.45% for InceptionV3, 98.02% for MobileNetV2, and 98.24% for Xception, with F1 scores for all classifiers above 75% in all four classes. Given the lighter weight of the trained MobileNetV2 and its better ability to generalize, we recommend deploying this model for the early detection of poultry diseases at the farm level.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Poultry diseases diagnostics models using deep learning

Machuve

Nwankwo

Mduma

et al. 2022

Front. Artif. Intell.

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“…In addition, transfer learning was also implemented to expand the DNN model in realizing the behaviour recognition of egg breeders with low stock density. The authors in [38] also followed the same approach by proposing an automated broiler digestive disease detector based on DNN model to classify fine-grained abnormal broiler droppings images as normal and abnormal.…”

Section: Related Workmentioning

confidence: 99%

Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

Adami

Ojo²,

Giordano

2021

IEEE Access

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In recent years, edge computing has become an essential technology for real-time application development by moving processing and storage capabilities close to end devices, thereby reducing latency, improving response time and ensuring secure data exchange. In this work, we focus on a Smart Agriculture application that aims to protect crops from ungulate attacks, and therefore to significantly reduce production losses, through the creation of virtual fences that take advantage of computer vision and ultrasound emission. Starting with an innovative device capable of generating ultrasound to drive away ungulates and thus protect crops from their attack, this work provides a comprehensive description of the design, development and assessment of an intelligent animal repulsion system that allows to detect and recognize the ungulates as well as generate ultrasonic signals tailored to each species of the ungulate. Taking into account the constraints coming from the rural environment in terms of energy supply and network connectivity, the proposed system is based on IoT platforms that provide a satisfactory compromise between performance, cost and energy consumption. More specifically, in this work, we deployed and evaluated various edge computing devices (Raspberry Pi, with or without a neural compute stick, and NVIDIA Jetson Nano) running real-time object detector (YOLO and Tiny-YOLO) with custom-trained models to identify the most suitable animal recognition HW/SW platform to be integrated with the ultrasound generator. Experimental results show the feasibility of the intelligent animal repelling system through the deployment of the animal detectors on power efficient edge computing devices without compromising the mean average precision and also satisfying real-time requirements. In addition, for each HW/SW platform, the experimental study provides a cost/performance analysis, as well as measurements of the average and peak CPU temperature. Best practices are also discussed and lastly, this article discusses how the combined technology used can help farmers and agronomists in their decision making and management process.

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“…Okinda et al [11] used the feature variables which were extracted based on 2D posture shape descriptors (circle variance, elongation, convexity, complexity, and eccentricity) and mobility feature (walk speed) achieved the early diagnosis of Newcastle disease virus infection in broiler chickens. Wang et al [12] realized the recognition and classification of abnormal feces by using deep learning and machine vision, so as to achieve the purpose of monitoring digestive diseases of broilers. However, presently, no research has reported on the use of computer vision technology to detect the RR of broilers.…”

Section: Introductionmentioning

confidence: 99%

The estimation of broiler respiration rate based on the semantic segmentation and video amplification

Wang

Liu

et al. 2022

Front. Phys.

Self Cite

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Respiratory rate is an indicator of a broilers’ stress and health status, thus, it is essential to detect respiratory rate contactless and stress-freely. This study proposed an estimation method of broiler respiratory rate by deep learning and machine vision. Experiments were performed at New Hope (Shandong Province, P. R. China) and Wen’s group (Guangdong Province, P. R. China), and a total of 300 min of video data were collected. By separating video frames, a data set of 3,000 images was made, and two semantic segmentation models were trained. The single-channel Euler video magnification algorithm was used to amplify the belly fluctuation of the broiler, which saved 55% operation time compared with the traditional Eulerian video magnification algorithm. The contour features significantly related to respiration were used to obtain the signals that could estimate broilers’ respiratory rate. Detrending and band-pass filtering eliminated the influence of broiler posture conversion and motion on the signal. The mean absolute error, root mean square error, average accuracy of the proposed respiratory rate estimation technique for broilers were 3.72%, 16.92%, and 92.19%, respectively.

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

Cited by 29 publications

References 20 publications

Poultry diseases diagnostics models using deep learning

Poultry diseases diagnostics models using deep learning

Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

The estimation of broiler respiration rate based on the semantic segmentation and video amplification

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