Abstract
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.
Highlights
Unmanned aerial vehicles (UAVs), known as unmanned aerial systems (UAS) and drones, are becoming commonplace in agriculture
Convolutional neural networks (CNNs) architectures are used as classifiers, which means that the term “detection” refers to the recognition that a given image block contains at least part of an animal
Taking into consideration the results reported in the literature for other breeds and other experimental setups, it seems evident that deep learning architectures are remarkably successful in extracting relevant information that can lead to accurate detection of cattle in images captured by UAVs
Summary
Unmanned aerial vehicles (UAVs), known as unmanned aerial systems (UAS) and drones, are becoming commonplace in agriculture. 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.) and measurement of body traits. All of those applications have one step in common: before the extraction of more sophisticated information, the animals need to be detected in the images. Convolutional neural networks (CNNs) architectures are used as classifiers, which means that the term “detection” refers to the recognition that a given image block contains at least part of an animal. It is worth noting that there are other ways to detect animals, such as using algorithms that estimate bounding boxes around the objects [3,4], and algorithms that delineate the animal bodies through semantic segmentation [5,6]
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