Leafsnap: A Computer Vision System for Automatic Plant Species Identification

By Neeraj Kumar, Peter N. Belhumeur, Arijit Biswas, David W. Jacobs, W. John Kress, Ida C. Lopez, João V. B. Soares. 2012

841 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Organic Chemistry, Plant Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Organic Chemistry

In Plant Science

Citations in the corpus

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition (2015, background): “Considerable work has also gone into the challenging task of curating finegrained datasets [4,58,27,30,31,59,65,60,70] and developing interactive methods for recognition with a human in the loop [6,62,61,63].”
Recognition in Terra Incognita (2018, background): “Natural world datasets such as the iNaturalist dataset [1], CUB200 [32], Oxford Flowers [33], LeafSnap [34], and NABirds700 [35] are focused on finegrained species classification and detection.”
The Devil is in the Tails: Fine-grained Classification in the Wild (2017, background): “Fine-Grained Visual Classification – The vision community has released many fine-grained datasets covering several domains such as birds [5, 50, 52, 56], dogs [23, 34], airplanes [36, 51], flowers [37], leaves [28], trees [55] and cars[24, 32].”
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets (2014, methodology): “Our architecture first computes an estimate of the object’s pose; this is used to compute local image features which are, in turn, used for classification.”
The iNaturalist Challenge 2017 Dataset (2017, background): “The vision community has released many fine-grained datasets covering several domains such as birds [42, 40, 2, 38, 16], dogs [14, 28, 22], airplanes [23, 39], flowers [26], leaves [18], trees [41] and cars [17, 20, 46, 6].”
Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review (2017, methodology): “Compared to [76], users can identify the species without internet and also use existing digital images as query image, i.e., for identifying a species.”
Deep-plant: Plant identification with convolutional neural networks (2015, methodology): “[3] proposed an automatic plant species identification system namely Leafsnap.”
Is Rotation a Nuisance in Shape Recognition? (2014, methodology): “In order to generate all possible shifts of a time series, we have to shift one curve n times in a circular order, where n is the number of possible start points.”
Fine-grained recognition without part annotations (2015, background): “birds [44, 47, 4], aircraft [42, 34], cars [41, 27, 32], flowers [35, 1], leaves [30], and dogs [25, 33].”
Collecting a Large-scale Dataset of Fine-grained Cars (2013, background): “Unlike naturally-occurring objects [9] [3] [4] [6], the class list of cars changes on a yearly basis, the appearance of some models of cars remains constant from year to year, and typical car websites may even list cars which differ only in terms of non-visual features, e.g. their engine, as…”
Fine-Grained Crowdsourcing for Fine-Grained Recognition (2013, background): “Our work shares the same end goal as existing work on fine grained recognition [16, 6, 15, 25, 19, 37, 36], but our approach is more aligned with the general line of research that places humans in the loop [20, 11, 22, 29, 9, 5, 24, 23, Figure 2.”
The iNaturalist Species Classification and Detection Dataset (2017, background): “The vision community has released many fine-grained datasets covering several domains such as birds [44, 42, 2, 40, 18], dogs [16, 29, 23], airplanes [24, 41], flowers [27], leaves [20], food [10], trees [43], and cars [19, 22, 48, 7].”
Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images (2015, methodology): “Known approaches use time-lapse image data, 3-D information as input or images are acquired under well-defined conditions [1, 9, 18].”
POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation (2013, background): “Additional fine-grained recognition work has been demonstrated on trees [13], flowers [19], butterflies [28, 8], and dogs [16, 21, 23].”