Leaf Recognition with Deep Learning and Keras using GPU computing

Our work is about using Deep Learning for leaf recognition using Keras and GPU computation. We used 17 CNNs of "Kaggle" [1], a Machine Learning training webpage that using simple challenges with prizes help people to learn how to use Deep Learning. Kaggle made a challenge in August, 30th, 2016 that was about Leaf Recognition. In that challenge more than 1500 users participated in it. They made teams to participate and we grab 17 codes of them to see how their codes was working. We updated the codes, because they were written in 1.0 version of Keras, and we use 2.0.6 version and then we made a ranking to test their accuracy in leaf recognition using the dataset provided by kaggle. Also we downloaded two more datasets to make more tests with them. On the other hand, we found two papers in "the ImageClef Competition" [2], and we implemented them from the beggining to see how simple is to transform a paper into code. ECOCUAN team, from 2015, used a tunned AlexNet CNN and KDETUT, from 2017, used a ResNet50 modified CNN. Another paper found on the net was one that use ResNet26 CNN, so we think that was a good idea to make a ranking of the three of them to see which is better.El nostre treball es basa en utilitzar Deep Learning per al reconeixement de fulles d'arbres utilitzant la llibreria Keras i fent servir una GPU per als càlculs de la xarxa. Hem utilitzat 17 CNNs de la web "Kaggle" que va fer una competició amb alguns premis per a la gent que volia aprendre Deep Learning. Kaggle va fer el concurs el 30 d'Agost del 2016 i en aquest concurs van participar més de 1500 usuaris. Van fer equips per participar i nosaltres hem agafat 17 codis d'aquests grups per veure com funcionen. Vam actualitzar els codis ja que eren fets amb la versió 1.0 de Keras i nosaltres utilitzem la versió 2.0.6 i després vam fer un ranking per veure la seva precisió en el reconeixement de fulles utilitzant el dataset que Kaggle proporcionava. També vam utilitzar dos datasets més per ampliar resultats. Per altra banda, vam trobar dos papers en la competició ImageClef que vam implementar des de 0 per veure com de fàcil és implementar una xarxa des d'un paper. ECOCUAN team del 2015 utilitzava una xarxa Alexnet tunejada y KDETUT del 2017 feia dues modificacions a la xarxa tipus ResNet50. També vam trobar un paper que feia servir la xarxa ResNet26 i vam pensar que era una bona idea utilitzar-lo per comparar amb la de KDETUT.Nuestro trabajo se basa en utilizar Deep Learning para el reconocimiento de hojas de árboles usando la librería Keras y cálculos mediante GPU. Hemos usado 17 CNNs encontradas en la web Kaggle que hizo una competición de reconocimiento de hojas con algunos premios para la gente que quería aprender Deep Learning. Kaggle hizo el concurso el 30 de Agosto de 2016 y participaron más de 1500 usuarios. Hicieron equipos para concursar y nosotros cogimos 17 de estos códigos para ver cómo funcionaban. Actualizamos los códigos ya que estaban hechos en la versión 1.0 de keras y nosotros teníamos la 2.0.6 y después hicimos un ranking para ver su precisión en el reconocimiento de hojas utilizando el dataset proporcionado por Kaggle. También utilizamos dos datasets más para ampliar resultados. Por otra parte, encontramos dos papeles de la competición ImageClef que implementamos desde cero para ver cómo de fácil es implementar una red desde un papel. ECOCUAN team del 2015 utilizaba una red tipo AlexNet tuneada y KDETUT del 2017 hacía dos modificaciones a la red tipo ResNet50. También encontramos otro papel que utilizaba la red ResNet26 y pensamos que era una buena idea compararlo con la red de KDETUT

The Flora Incognita app - interactive plant species identification

Being able to identify plant species is an important factor for understanding biodiversity and its change due to natural and anthropogenic drivers. We discuss the freely available Flora Incognita app for Android, iOS and Harmony OS devices that allows users to interactively identify plant species and capture their observations. Specifically developed deep learning algorithms, trained on an extensive repository of plant observations, classify plant images with yet unprecedented accuracy. By using this technology in a context-adaptive and interactive identification process, users are now able to reliably identify plants regardless of their botanical knowledge level. Users benefit from an intuitive interface and supplementary educational materials. The captured observations in combination with their metadata provide a rich resource for researching, monitoring and understanding plant diversity. Mobile applications such as Flora Incognita stimulate the successful interplay of citizen science, conservation and education

AI naturalists might hold the key to unlocking biodiversity data in social media imagery

The increasing availability of digital images, coupled with sophisticated artificial intelligence (AI) techniques for image classification, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations. We investigated whether an AI plant species classifier could extract previously unexploited biodiversity data from social media photos (Flickr). We found over 60,000 geolocated images tagged with the keyword “flower” across an urban and rural location in the UK and classified these using AI, reviewing these identifications and assessing the representativeness of images. Images were predominantly biodiversity focused, showing single species. Non-native garden plants dominated, particularly in the urban setting. The AI classifier performed best when photos were focused on single native species in wild situations but also performed well at higher taxonomic levels (genus and family), even when images substantially deviated from this. We present a checklist of questions that should be considered when undertaking a similar analysis

Machine learning using digitized herbarium specimens to advance phenological research

Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth

Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients

Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd-sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co-occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70-year period. The direct comparison of the two data sets reveals that the crowd-sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real-time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd-sourced biodiversity data can effectively guide conservation measures

Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification

International audienceIn modern classification tasks, the number of labels is getting larger and larger, as is the size of the datasets encountered in practice. As the number of classes increases, class ambiguity and class imbalance become more and more problematic to achieve high top-1 accuracy. Meanwhile, Top-K metrics (metrics allowing K guesses) have become popular, especially for performance reporting. Yet, proposing top-K losses tailored for deep learning remains a challenge, both theoretically and practically. In this paper we introduce a stochastic top-K hinge loss inspired by recent developments on top-K calibrated losses. Our proposal is based on the smoothing of the top-K operator building on the flexible "perturbed optimizer" framework. We show that our loss function performs very well in the case of balanced datasets, while benefiting from a significantly lower computational time than the state-of-the-art top-K loss function. In addition, we propose a simple variant of our loss for the imbalanced case. Experiments on a heavy-tailed dataset show that our loss function significantly outperforms other baseline loss functions

Identifying uncertainties in scenarios and models of socio-ecological systems in support of decision-making

There are many sources of uncertainty in scenarios and models of socio-ecological systems, and understanding these uncertainties is critical in supporting informed decision-making about the management of natural resources. Here, we review uncertainty across the steps needed to create socio-ecological scenarios, from narrative storylines to the representation of human and biological processes in models and the estimation of scenario and model parameters. We find that socio-ecological scenarios and models would benefit from moving away from “stylized” approaches that do not consider a wide range of direct drivers and their dependency on indirect drivers. Indeed, a greater focus on the social phenomena is fundamental in understanding the functioning of nature on a human-dominated planet. There is no panacea for dealing with uncertainty, but several approaches to evaluating uncertainty are still not routinely applied in scenario modeling, and this is becoming increasingly unacceptable. However, it is important to avoid uncertainties becoming an excuse for inaction in decision-making when facing environmental challenges.</p

Pl@ntNet app in the era of deep learning

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