Review of Plant Identification Based on Image Processing

Wang, Zhaobin; Li, Huale; Zhu, Ying; Xu, Tianfang

doi:10.1007/s11831-016-9181-4

Cited by 80 publications

(45 citation statements)

References 51 publications

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“…Optical imaging techniques are important tools extensively used for probing a number of materials properties [1]. These imaging techniques are non-destructive and particularly convenient for dealing with biological and other complex materials [2]. Liquid crystals are among these materials widely studied via optical and image processing methods [3].…”

Section: Introductionmentioning

confidence: 99%

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Sigaki

Souza

et al. 2019

Phys. Rev. E

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Imaging techniques are essential tools for inquiring a number of properties from different materials. Liquid crystals are often investigated via optical and image processing methods. In spite of that, considerably less attention has been paid to the problem of extracting physical properties of liquid crystals directly from textures images of these materials. Here we present an approach that combines two physics-inspired image quantifiers (permutation entropy and statistical complexity) with machine learning techniques for extracting physical properties of nematic and cholesteric liquid crystals directly from their textures images. We demonstrate the usefulness and accuracy of our approach in a series of applications involving simulated and experimental textures, in which physical properties of these materials (namely: average order parameter, sample temperature, and cholesteric pitch length) are predicted with significant precision. Finally, we believe our approach can be useful in more complex liquid crystal experiments as well as for probing physical properties of other materials that are investigated via imaging techniques.

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Section: Introductionmentioning

confidence: 99%

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Sigaki

Souza

et al. 2019

Phys. Rev. E

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“…The Flavia dataset was originally published in [26] and consists of 1907 scanned leaf images on white backgrounds covering 32 different plant species. Although this presents a more constrained view for an agricultural robotics setting when compared to the Redlands dataset, the Flavia dataset provides many more images and a greater variety of distinct species and is a commonly used dataset in leaf classification literature [27] [28] [16]. For the segmentation stage of our pipeline, we use a different segmentation method than that presented in Section 2.1.1 and instead use the procedure outlined in the original paper [26].…”

Section: Datasetsmentioning

confidence: 99%

A rapidly deployable classification system using visual data for the application of precision weed management

Hall

Dayoub²,

Pérez

et al. 2018

Computers and Electronics in Agriculture

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In this work we demonstrate a rapidly deployable weed classification system that uses visual data to enable autonomous precision weeding without making prior assumptions about which weed species are present in a given field. Previous work in this area relies on having prior knowledge of the weed species present in the field. This assumption cannot always hold true for every field, and thus limits the use of weed classification systems based on this assumption. In this work, we obviate this assumption and introduce a rapidly deployable approach able to operate on any field without any weed species assumptions prior to deployment. We present a three stage pipeline for the implementation of our weed classification system consisting of initial field surveillance, offline processing and selective labelling, and automated precision weeding. The key characteristic of our approach is the combination of plant clustering and selective labelling which is what enables our system to operate without prior weed species knowledge. Testing using field data we are able to label 12.3 times fewer images than traditional full labelling whilst reducing classification accuracy by only 14%. * The authors are with the

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“…Moreover, recent technical trends, including minimization of high-quality sensors, have led to new possibilities in the use of unmanned aerial systems (UAS) in various fields and applications [20]. In particular, UAS are now used in ecology and agriculture in a spectrum of research topics [20,21], such as plant identification [22] and weed management [23,24], or pest control and insect monitoring [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Recognize the Little Ones: UAS-Based In-Situ Fluorescent Tracer Detection

Teickner

Lehmann

Guth

et al. 2019

Drones

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In ecological research, a key interest is to explore movement patterns of individual organisms across different spatial scales as one driver of biotic interactions. While various methods exist to detect and record the presence and movements of individuals in combination with UAS, addressing these for smaller animals, such as insects, is challenging and often fails to reveal information on potential interactions. Here, we address this gap by combining the UAS-based detection of small tracers of fluorescent dyes by means of a simple experiment under field conditions for the first time. We (1) excited fluorescent tracers utilizing an UV radiation source and recorded images with an UAS, (2) conducted a semi-automated selection of training and test samples to (3) train a simple SVM classifier, allowing (4) the classification of the recorded images and (5) the automated identification of individual traces. The tracer detection success significantly decreased with increasing altitude, increasing distance from the UV radiation signal center, and decreasing size of the fluorescent traces, including significant interactions amongst these factors. As a first proof-of-principle, our approach has the potential to be broadly applicable in ecological research, particularly in insect monitoring.

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Review of Plant Identification Based on Image Processing

Cited by 80 publications

References 51 publications

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

A rapidly deployable classification system using visual data for the application of precision weed management

Recognize the Little Ones: UAS-Based In-Situ Fluorescent Tracer Detection

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