Towards the fully automated monitoring of ecological communities

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Abstract

<p id="d1641453e407">High‐resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real‐time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components—for example, individual behaviours and traits, and species abundance and distribution—is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high‐throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high‐resolution, multidimensional and standardised data across complex ecologies. </p><p class="first" id="d1641453e410">Monitoring living organisms with high‐resolution and multidimensional is a complex and labour‐intensive task, yet it is fundamental to understand and predict the dynamics of ecological communities in an era of global change and biodiversity declines. Here, we review existing technologies for automated data recording and processing, and we present novel frameworks that combine these technologies into automated monitoring pipelines that detect, track, classify and count multiple species, and even record behavioural and morphological traits at resolutions which have previously been impossible to achieve. We illustrate a solution to one of the greatest challenges in ecology and conservation: the ability to rapidly generate high resolution, multidimensional and critically, standardised data across complex ecologies. <div class="boxed-text panel" id="ele14123-blkfxd-0001"> <a class="named-anchor" id="ele14123-blkfxd-0001">  </a> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/d0781a3f-8f4c-4057-b39d-25095c915b9c/PubMedCentral/image/ELE-25-2753-g007.jpg"/> </div> <div class="panel-content"/> </div> </p>

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DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Pranav Mamidanna, Venkatesh N. Murthy, Mackenzie Weygandt Mathis … (2018)

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.

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Is Open Access

Using Deep Learning for Image-Based Plant Disease Detection

Sharada P. Mohanty, David Hughes, Marcel Salathé (2016)

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.