Crowdsourcing for botanical data collection towards to automatic plant identification: A review

Nguyen, Thi Thanh Nhan; Le, Thi‐Lan; Vu, Hai; Sam, Hoang Van; Tran, Thanh-Hai

doi:10.1016/j.compag.2018.10.042

Cited by 14 publications

(11 citation statements)

References 28 publications

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“…Several of the apps allow users to confirm identification by feedback to the developers (see Table 1 ); this type of crowdsourcing information can lead to continuing improvement in the ability of the app to identify future samples ( Nguyen et al 2018 ). Nevertheless, I am concerned that without rigorous quality control of feedback such an approach can potentially build in error.…”

Section: Resultsmentioning

confidence: 99%

“…The potential for combining several images of different plant organs is a very powerful tool for enhancing identification performance ( Nguyen et al 2018 ; Rzanny et al 2019 ). This useful tool is implemented in PlantNet, Plant.id, Flora Incognita, and though not tested in the present study, would be likely to improve accuracy further.…”

Section: Resultsmentioning

confidence: 99%

“…Leafsnap ( Kumar et al 2012 ), for example, was an early automated app for species identification using a large data base of leaf images and provides the basis for PlantSnap evaluated here. Other work has confirmed that deep neural networks can be extraordinarily powerful at detailed recognition even using noisy data ( Krause et al 2016 ) and this approach is widely used in the current apps as outlined in many recent papers and reviews that outline progress with development of image recognition for plant identification ( Mata-Montero and Carranza-Rojas 2016 ; Cappellato et al 2017 ; Nguyen et al 2018 ; Wäldchen and Mäder 2018 ; Wäldchen et al 2018 ; Rzanny et al 2019 ). Image recognition technology is now maturing so rapidly that there are now a number of automatic plant identification apps available for smartphones and tablets, so it is timely to consider the state of the art in this technology.…”

Section: Introductionmentioning

confidence: 87%

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What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora

Jones

2020

AoB PLANTS

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There has been a recent explosion in development of image recognition technology and its application to automated plant identification so it is timely to consider its potential for field botany. Nine free apps or websites for automated plant identification and suitable for use on mobile phones or tablet computers in the field were tested on a disparate set of 38 images of plants or parts of plants chosen from the higher plant flora of Britain and Ireland. There were large differences in performance with the best apps identifying more than 50% of samples tested to genus or better. Although the accuracy is good for some of the top-rated apps, for any quantitative biodiversity study or for ecological surveys there remains a need for validation by experts or against conventional floras. Nevertheless, the better-performing apps should be of great value to beginners and amateurs and may usefully stimulate interest in plant identification and nature. Potential uses of automated image recognition plant identification apps are discussed and recommendations made for their future use.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 87%

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What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora

Jones

2020

AoB PLANTS

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“…We also believe that equipping of old texts with metadata (such as UDC numbers) can be further enhanced by implementing the wisdom of the crowds. As concluded by Nguyen et al (2018), the goal is to obtain a sufficient amount of quality data. Existing systems for "mass outsourcing", or.crowdsourcing are usually based on one of three social network structures, as reported by Silvertown et al (2015):…”

Section: Resultsmentioning

confidence: 99%

Automatic classification of older electronic texts into the Universal Decimal Classification–UDC

Kragelj

Borštnar

2020

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PurposeThe purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.Design/methodology/approachThe general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.FindingsResults suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.Research limitations/implicationsThe main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.Practical implicationsThe classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.Social implicationsThe proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.Originality/valueThese findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.

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“…Unmanned aerial vehicles (UAVs), manned ground vehicles (MGVs), and tractor-based high-throughput phenotyping platforms (HTPPs) can rapidly obtain high-resolution top-view images of crop canopies. Researchers can extract phenotypic parameters [8], such as plant size [9], shape [10], and color [11], from the acquired images. For some specific phenotypic parameters, these approaches can be substituted for traditional manual measurements, improving the efficiency of collecting plant phenotypic information.…”

Section: Introductionmentioning

confidence: 99%

High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network

Wen

Guo

et al. 2020

Preprint

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Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or ground high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R 2 =0.96-0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.

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Crowdsourcing for botanical data collection towards to automatic plant identification: A review

Cited by 14 publications

References 28 publications

What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora

What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora

Automatic classification of older electronic texts into the Universal Decimal Classification–UDC

High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network

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