#Leaf guide portable#
The fast development and ubiquity of relevant information technologies in combination with the availability of portable devices such as digital cameras and smartphones results in a vast number of digital images, which are accumulated in online databases. More than 10 years ago, Gaston and O’Neill proposed that developments in artificial intelligence and digital image processing could make automated species identification realistic. Accelerating the identification process and making it executable by non-experts is highly desirable, especially when considering the continuous loss of plant biodiversity. Species identification is essential for studying the biodiversity richness of a region, monitoring populations of endangered species, determining the impact of climate change on species distributions, payment of environmental services, and weed control actions. For the first time, this study provides a systematic guideline allowing researchers to spend available acquisition resources wisely while yielding the optimal classification accuracy.Īccurate plant identification represents the basis for all aspects of related research and is an important component of workflows in plant ecological research. In conclusion, the way in which leaf images are acquired and preprocessed does have a substantial effect on the accuracy of the classifier trained on them. Imaging the typically stronger textured backside of a leaf does not result in higher accuracy, but notably increases the acquisition cost. The permanent use or disuse of a flash light has negligible effects. Cropping the image to the leaf’s boundary substantially improves accuracy, while precise segmentation yields similar accuracy at a substantially higher effort.
We yield the highest classification accuracy using destructive back light images, i.e., holding the plucked leaf against the sky for image acquisition. The most effective, non-destructive way to record herbaceous leaves is to take an image of the leaf’s top side. We extract image features using a convolutional neural network, classify species using the resulting feature vectors and discuss classification accuracy in relation to the required effort per combination.
#Leaf guide manual#
Per image type-preprocessing combination, we also quantify the manual effort required for their implementation. Image types vary in terms of in-situ image recording conditions: perspective, illumination, and background, while the preprocessing strategies compare non-preprocessed, cropped, and segmented images to each other. In this paper, we systematically study nine image types and three preprocessing strategies.
#Leaf guide how to#
Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way. Substantially matured machine learning techniques generate the need for more training data (aka leaf images). Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Contrary to flowers and fruits, leaves are available throughout most of the year. Overleaf has published a wide range of in-depth technical articles for readers interested in the low-level behaviour of TeX engines.Automated species identification is a long term research subject. Multilingual typesetting is discussed, with examples, in the article Multilingual typesetting on Overleaf using polyglossia and fontspec. These include Arabic, Chinese, French, German, Greek, Italian, Japanese, Korean, Portuguese, Russian, and Spanish. Overleaf and L aT eX have support for a large selection of languages. Start with our Learn L aT eX in 30 minutes guide.įor more specific introductions, have a look at:
A complete list of topics is provided on the left hand-side, but here is a selection of useful articles:
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