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Special Issue "Artificial Intelligence Tools to Optimize Livestock Production"

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A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 2587

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Prof. Dr. Danilo Florentino Pereira

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Department of Management, Development and Technology, School of Science and Engineering, São Paulo State University, 780 Domingos da Costa Lopes Avenue, Tupã 17602-496, SP, Brazil
Interests: computer vision; poultry farming; animal welfare assessment
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Prof. Dr. Irenilza de Alencar Nääs

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Graduate Program in Production Engineering, Universidade Paulista, 1212 Dr. Bacelar Street, São Paulo 04026-002, Brazil
Interests: precision livestock farming; image analysis; broiler supply chain; pig farming
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Dr. Saman Abdanan Mehdizadeh

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Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
Interests: machine learning; mechatronics; smart livestock farming

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) in agriculture aims to bring the revolution of robotics and automation to animal protein production and supply chains. The idea is to relieve human labor under tedious and monotouse conditions and to solve the problem of finding cost-effective methods to provide the world a sustainable solution for future food security.

There is high pressure on livestock production to mechanize further actions on-farm, and there is resistance to performing the physically challenging labor. In stark contrast, yields (meat, milk, and eggs) must keep increasing to feed the world population. We believe AI is the decisive answer to this food production and supply chain matter.

In this special issue, we hope to bring together articles exploring the frontline of AI application in animal production systems and supply chains, both in field and laboratory conditions, also expanding the current knowledge about animal welfare.

Prof. Dr. Danilo Florentino Pereira
Prof. Dr. Irenilza de Alencar Nääs
Dr. Saman Abdanan Mehdizadeh
Guest Editors

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Keywords

robotics
soft computing
digital image processing
deep learning
computer vision
non-invasion method

Published Papers (3 papers)

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Research

Open AccessArticle

LAD-RCNN: A Powerful Tool for Livestock Face Detection and Normalization

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Animals 2023, 13(9), 1446; https://doi.org/10.3390/ani13091446 - 24 Apr 2023

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Abstract

With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in the area of animal face detection and face identification was conducted. However, there are no specialized studies on livestock face normalization, which may significantly reduce the performance of face identification. The keypoint detection technology, which has been widely applied in human face normalization, is not suitable for animal face normalization due to the arbitrary directions of animal face images captured from uncooperative animals. It is necessary to develop a livestock face normalization method that can handle arbitrary face directions. In this study, a lightweight angle detection and region-based convolutional network (LAD-RCNN) was developed, which contains a new rotation angle coding method that can detect the rotation angle and the location of the animal’s face in one stage. LAD-RCNN also includes a series of image enhancement methods to improve its performance. LAD-RCNN has been evaluated on multiple datasets, including a goat dataset and infrared images of goats. Evaluation results show that the average precision of face detection was more than 97%, and the deviations between the detected rotation angle and the ground-truth rotation angle were less than 6.42° on all the test datasets. LAD-RCNN runs very fast and only takes 13.7 ms to process a picture on a single RTX 2080Ti GPU. This shows that LAD-RCNN has an excellent performance in livestock face recognition and direction detection, and therefore it is very suitable for livestock face detection and normalization. Full article

(This article belongs to the Special Issue Artificial Intelligence Tools to Optimize Livestock Production)

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Open AccessArticle

DISubNet: Depthwise Separable Inception Subnetwork for Pig Treatment Classification Using Thermal Data

Savina Jassica Colaco

and

Animals 2023, 13(7), 1184; https://doi.org/10.3390/ani13071184 - 28 Mar 2023

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Abstract

Thermal imaging is increasingly used in poultry, swine, and dairy animal husbandry to detect disease and distress. In intensive pig production systems, early detection of health and welfare issues is crucial for timely intervention. Using thermal imaging for pig treatment classification can improve animal welfare and promote sustainable pig production. In this paper, we present a depthwise separable inception subnetwork (DISubNet), a lightweight model for classifying four pig treatments. Based on the modified model architecture, we propose two DISubNet versions: DISubNetV1 and DISubNetV2. Our proposed models are compared to other deep learning models commonly employed for image classification. The thermal dataset captured by a forward-looking infrared (FLIR) camera is used to train these models. The experimental results demonstrate that the proposed models for thermal images of various pig treatments outperform other models. In addition, both proposed models achieve approximately 99.96–99.98% classification accuracy with fewer parameters. Full article

(This article belongs to the Special Issue Artificial Intelligence Tools to Optimize Livestock Production)

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Open AccessArticle

On the Development of a Wearable Animal Monitor

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Animals 2023, 13(1), 120; https://doi.org/10.3390/ani13010120 - 28 Dec 2022

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Abstract

Animal monitoring is a task traditionally performed by pastoralists, as a way of ensuring the safety and well-being of animals; a tremendously arduous and lonely task, it requires long walks and extended periods of contact with the animals. The Internet of Things and the possibility of applying sensors to different kinds of devices, in particular the use of wearable sensors, has proven not only to be less invasive to the animals, but also to have a low cost and to be quite efficient. The present work analyses the most impactful monitored features in the behavior learning process and their learning results. It especially addresses the impact of a gyroscope, which heavily influences the cost of the collar. Based on the chosen set of sensors, a learning model is subsequently established, and the learning outcomes are analyzed. Finally, the animal behavior prediction capability of the learning model (which was based on the sensed data of adult animals) is additionally subjected and evaluated in a scenario featuring younger animals. Results suggest that not only is it possible to accurately classify these behaviors (with a balanced accuracy around 91%), but that removing the gyroscope can be advantageous. Results additionally show a positive contribution of the thermometer in behavior identification but evidences the need for further confirmation in future work, considering different seasons of different years and scenarios including more diverse animals’ behavior. Full article

(This article belongs to the Special Issue Artificial Intelligence Tools to Optimize Livestock Production)

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