Perspectives on the use of unmanned aerial systems to monitor cattle

Barbedo, Jayme Garcia Arnal; Koenigkan, Luciano Vieira

doi:10.1177/0030727018781876

Cited by 45 publications

(37 citation statements)

References 45 publications

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“…As mentioned before, all image blocks generated during the labeling process had 224 × 224 pixels. In order to simulate coarser GSDs, the original image blocks were downsampled to 112 × 112 pixels and 56 × 56 pixels, simulating GSDs of 2 and 4 cm/pixel or, equivalently, simulating flight altitudes of 60 and 120 m, which is the current legal limit in most countries without the need for some special exemption [1].…”

Section: Methodsmentioning

confidence: 99%

“…Unmanned aerial vehicles (UAVs), also known as unmanned aerial systems (UAS) and drones, are becoming commonplace in agriculture. New applications are being constantly proposed, both in the crop and livestock production chains [1,2]. In the specific case of cattle monitoring, there are a few applications in which UAVs can have immediate impact, including the estimation of the number of animals, monitoring of anomalous events (diseased animals, calf birth, etc.)…”

Section: Introductionmentioning

confidence: 99%

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A Study on the Detection of Cattle in UAV Images Using Deep Learning

Barbedo

Koenigkan

Santos

et al. 2019

Sensors

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Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and convolutional neural networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: (1) to determine the highest possible accuracy that could be achieved in the detection of animals of the Canchim breed, which is visually similar to the Nelore breed (Bos taurus indicus); (2) to determine the ideal ground sample distance (GSD) for animal detection; (3) to determine the most accurate CNN architecture for this specific problem. The experiments involved 1853 images containing 8629 samples of animals, and 15 different CNN architectures were tested. A total of 900 models were trained (15 CNN architectures × 3 spacial resolutions × 2 datasets × 10-fold cross validation), allowing for a deep analysis of the several aspects that impact the detection of cattle using aerial images captured using UAVs. Results revealed that many CNN architectures are robust enough to reliably detect animals in aerial images even under far from ideal conditions, indicating the viability of using UAVs for cattle monitoring.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Study on the Detection of Cattle in UAV Images Using Deep Learning

Barbedo

Koenigkan

Santos

et al. 2019

Sensors

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“…In order to simulate coarser GSDs, the original image blocks were downsampled to 112 x 112 pixels and 56 x 56 pixels, simulating GSDs of 2 and 4 cm/pixel or, equivalently, simulating flight altitudes of 60 and 120 m, which is the current legal limit in most countries without the need for some special exemption [1].…”

Section: Methodsmentioning

confidence: 99%

A Study on the Detection of Cattle in UAV Images Using Deep Learning

Barbedo¹,

Koenigkan²,

Santos³

et al. 2019

Preprint

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Unmanned Aerial Vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive cattle farming, as production areas tend to be expansive and animals tend to be more loosely monitored. With the advent of deep learning, and Convolutional Neural Networks (CNNs) in particular, extracting relevant information from aerial images has become more effective. Despite the technological advancements in drone, imaging and machine learning technologies, the application of UAVs for cattle monitoring is far from being thoroughly studied, with many research gaps still remaining. In this context, the objectives of this study were threefold: 1) to determine the highest possible accuracy that could be achieved in the detection of animals of the Canchim breed, which is visually similar to the Nelore breed (\textit{Bos taurus indicus}); 2) to determine the ideal Ground Sample Distance (GSD) for animal detection; 3) to determine the most accurate CNN architecture for this specific problem. The experiments involved 1,853 images containing 8,629 samples of animals, and 15 different CNN architectures were tested. A total of 900 models were trained (15 CNN architectures * 3 spacial resolutions * 2 datasets * 10-fold cross validation), allowing for a deep analysis of the several aspects that impact the detection of cattle using aerial images captured using UAVs. Results revealed that many CNN architectures are robust enough to reliably detect animals in aerial images even under far from ideal conditions, indicating the viability of using UAVs for cattle monitoring.

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“…There has been very limited work on use of UAS for monitoring livestock particularly for animal detection, feeding behavior, and health monitoring. For the review of these techniques , see [42].…”

Section: Unmanned Aerial Systemsmentioning

confidence: 99%

DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

et al. 2019

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Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense objects are sparsely located. Raw aerial images represent sparse distributions of data in most situations. To address this issue, we propose a novel and exceedingly portable end-to-end model, DisCountNet, and an example dataset to test it on. DisCountNet is a two-stage network that uses theories from both detection and heat-map networks to provide a simple yet powerful design. The first stage, DiscNet, operates on the theory of coarse detection, but does so by converting a rich and high-resolution image into a sparse representation where only important information is encoded. Following this, CountNet operates on the dense regions of the sparse matrix to generate a density map, which provides fine locations and count predictions on densities of objects. Comparing the proposed network to current state-of-the-art networks, we find that we can maintain competitive performance while using a fraction of the computational complexity, resulting in a real-time solution.

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Perspectives on the use of unmanned aerial systems to monitor cattle

Cited by 45 publications

References 45 publications

A Study on the Detection of Cattle in UAV Images Using Deep Learning

A Study on the Detection of Cattle in UAV Images Using Deep Learning

A Study on the Detection of Cattle in UAV Images Using Deep Learning

DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

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