A systematic literature review on deep learning applications for precision cattle farming

Mahmud, Sultan; Zahid, Azlan; Das, Anup; Muzammil, Muhammad; Khan, Muhammad Usman

doi:10.1016/j.compag.2021.106313

Cited by 76 publications

(45 citation statements)

References 58 publications

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“…With the advancement of sensor technology and connectivity, there are some research efforts on Internet of Technology (IoT) applications with drones [23], [24], with a particular interest in livestock management. The data collection, analysis and real-time decision-making process are entertained with the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) tools in this field [25], [26], [27]. Softwarization of UAV network is described in [28] along with the various application fields and research directions.…”

Section: Figure 1 Different Aspects Of Livestock Management Related T...mentioning

confidence: 99%

Livestock Management With Unmanned Aerial Vehicles: A Review

et al. 2022

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The ease of use and advancements in drone technology is resulting in the widespread application of Unmanned Aerial Vehicles (UAVs) to diverse fields, making it a booming technology. Among UAVs' several applications, livestock agriculture is one of the most promising, where UAVs facilitate various operations for efficient animal management. But the field is characterized by multiple environmental, technical, economic, and strategic challenges. However, the use of advanced technological techniques like Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), Deep Learning (DL), advanced sensors, etc. along with the assurance of animal welfare while operating the UAVs, can lead to widespread adoption of drone technology amongst livestock farmers. This paper discusses livestock management research where UAVs monitor farm animals via detection, counting, tracking animals, etc. In this article, an attempt has been made to elucidate different aspects and broader issues around livestock management while highlighting the associated challenges, opportunities, and prospects. This work is the first review paper on the subject matter with all the necessary information and analysis, to the best of our knowledge. Therefore, the article promises to provide interested researchers with detailed information about the field, guiding future research.

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Section: Figure 1 Different Aspects Of Livestock Management Related T...mentioning

confidence: 99%

Livestock Management With Unmanned Aerial Vehicles: A Review

et al. 2022

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“…The feature maps were shared for subsequent RPN layers and fully connected layers. The feature extraction process in Faster R-CNN is the same as that in CNN and can be performed using some common structures, such as the commonly used Visual Geometry Group (VGG) and ResNet [36]. In this study, the VGG16 model was used, which is a convolutional neural network model that was proposed by Simonyan and Zisserman [37].…”

Section: Faster R-cnnmentioning

confidence: 99%

Tree Trunk Recognition in Orchard Autonomous Operations under Different Light Conditions Using a Thermal Camera and Faster R-CNN

Jiang

Noguchi

Ahamed

2022

Sensors

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In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to recognize tree trunks using a deep learning system. Therefore, the objective of this study was to use a thermal camera to detect tree trunks at different times of the day under low-light conditions using deep learning to allow robots to navigate. Thermal images were collected from the dense canopies of two types of orchards (conventional and joint training systems) under high-light (12–2 PM), low-light (5–6 PM), and no-light (7–8 PM) conditions in August and September 2021 (summertime) in Japan. The detection accuracy for a tree trunk was confirmed by the thermal camera, which observed an average error of 0.16 m for 5 m, 0.24 m for 15 m, and 0.3 m for 20 m distances under high-, low-, and no-light conditions, respectively, in different orientations of the thermal camera. Thermal imagery datasets were augmented to train, validate, and test using the Faster R-CNN deep learning model to detect tree trunks. A total of 12,876 images were used to train the model, 2318 images were used to validate the training process, and 1288 images were used to test the model. The mAP of the model was 0.8529 for validation and 0.8378 for the testing process. The average object detection time was 83 ms for images and 90 ms for videos with the thermal camera set at 11 FPS. The model was compared with the YOLO v3 with same number of datasets and training conditions. In the comparisons, Faster R-CNN achieved a higher accuracy than YOLO v3 in tree truck detection using the thermal camera. Therefore, the results showed that Faster R-CNN can be used to recognize objects using thermal images to enable robot navigation in orchards under different lighting conditions.

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“…A filter size of 3-by-3 is the smallest size at which the notion of left/ right, up/down, and center can be captured, making the decision function more discriminative. Other often used networks in precision farming include ResNet, DenseNet, Inception V3, and MobileNet, but no significant difference beforehand has been demonstrated (Mahmud et al 2021).…”

Section: Cnn Architecture and Trainingmentioning

confidence: 99%

Deep learning image recognition of cow behavior and an open data set acquired near an automatic milking robot

Koskela

Pereira

Pölönen

et al. 2022

AFSci

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Production animals enjoying good health and well-being are more productive and have a higher output quality. Several technical solutions have been used to monitor the animals’ welfare: those based on computer vision provide cost-efficient and scalable options. In this work, we performed a continuous two-month image acquisition of cows in front of an automatic milking station and divided the data into ten different classes related to the most important activities appearing in the images. The data consisted of almost 19 hours of video, equivalent to more than 1.7 million still images. Based on these imaged, we then implemented a convolutional neural network classifier to recognize the cows' behavior. The network was tested using cross-validation methodology and achieved an 86% precision rate and 85% recall rate in the classification. The data and the Python program code used in this study are made available. An image data set that directly resembles the harsh conditions inside a barn and can be used for deep learning purposes has not been previously made available.

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A systematic literature review on deep learning applications for precision cattle farming

Cited by 76 publications

References 58 publications

Livestock Management With Unmanned Aerial Vehicles: A Review

Livestock Management With Unmanned Aerial Vehicles: A Review

Tree Trunk Recognition in Orchard Autonomous Operations under Different Light Conditions Using a Thermal Camera and Faster R-CNN

Deep learning image recognition of cow behavior and an open data set acquired near an automatic milking robot

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