Plant Taxonomy in Hainan Based on Deep Convolutional Neural Network and Transfer Learning

Liu, Wei; Feng, Wenlong; Huang, Mengxing; Han, Guilai; Lin, Jialun

doi:10.1109/trustcom50675.2020.00197

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…Studies Flavia dataset [49] [8], [17], [23], [25], [29], [36], [43] ICL leaf dataset [22] [46] LeafSnap dataset [21] [19] Own-authored dataset [1], [3], [10], [13], [26], [29], [35], [42] UCI Iris dataset [9] [26] UCI Leaf dataset [38] [2], [27], [30], [41], [51], [44] UCI One-hundred plant species leaves dataset [26] [20], [51] Uninformed [4], [31] We realized that a wide variety of datasets was used in the selected studies, and many authors chose to create their own datasets (8 studies of 25), possibly by not finding available datasets with the desired characteristics or information. Even so, public datasets were also widely used, with Flavia dataset [49], and UCI Leaf Dataset [38] being the most used (7 and 6 studies of 25, respectively).…”

Section: Datasetmentioning

confidence: 99%

“…Some studies [1,10,19] used leaves from Acer species plants to illustrate samples of the datasets used or to illustrate some steps taken in their approaches. The species Acer Palmatum is mentioned in the following works, as an example of the species available on the dataset used: [2], [3], [13], [20], [23], [25], [26], [27], [29], [30], [31], [35], [36], [44], [41], [42], [43], [46], [51].…”

Section: Datasetmentioning

confidence: 99%

“…In addition to Acer Palmatum, other Acer species are also mentioned, such as Acer Buergerainum [13,23,25,29,43,46], Acer Negundo [3,42,44], Acer Rubrum [19,51], Acer Saccharinum [19,51], Acer Platanoides [19,51], Acer Pseudoplatanos [19], Acer Pensylvanicum [19], Acer Campestre [21,26,51], Acer Capillipes [21], Acer Circinatum [26,51], Acer Mono [26,51], Acer Opalus [31], Acer ginnala Maxim. Subsp.…”

Section: Datasetmentioning

confidence: 99%

“…[29] Support Vector Machine (SVM) [19] (Leaf/Non-Leaf Classification), [13], [8], [10], [25], [27], [35] Transfer Learning approach with AlexNet [21], [23] Transfer Learning approach with ResNet, GoogLeNet and VGGNet [23] Naive Bayes (NB) [8], [27], [30] * This study employed Machine Learning algorithms for the task of identifying relevant features and concepts in a numerical dataset, in order to shape the domains and quality dimensions of conceptual space.…”

Section: Machine Learning Algorithms Used For Species Classificationmentioning

confidence: 99%

See 3 more Smart Citations

Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Ascari

Casanova

Fávero

et al. 2023

Anais Do XIV Computer on the Beach - COTB'23

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Plant species identification is a task of interdisciplinary interest,desirable in many contexts, such as gardening, botanical research,and agriculture. For some plant species such as the Acer Palmatum,the characteristics of leaves, petioles, and trunks can drasticallyvary among the different genera of the same subspecies. ComputerVision and Machine Learning research areas made possible thecreation of different classifiers trained to assist in species plantrecognition based on digital images. However, the success of thetraining of a classification model is directly linked to the qualityand adequacy of the dataset used. For the classification of AcerPalmatum plants, datasets composed of samples regarding the differentvarieties within this genus were not identified. Thus, in thispaper, we proposed the creation of a new dataset and of a classifierto support the identification of distinct plant genera of the subspeciesAcer Palmatum. We believe that our proposal aggregatesrelevant information not currently available, and will encouragefurther work aimed at automatically classifying between genera ofsome plant species which task is considered non-trivial even forexperienced growers.

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Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Machine Learning Algorithms Used For Species Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Ascari

Casanova

Fávero

et al. 2023

Anais Do XIV Computer on the Beach - COTB'23

View full text Add to dashboard Cite

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Deep neural networks for explainable feature extraction in orchid identification

Apriyanti,

Spreeuwers,

Lucas

2023

Appl Intell

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Automated image-based plant identification systems are black-boxes, failing to provide an explanation of a classification. Such explanations are seen as being essential by taxonomists and are part of the traditional procedure of plant identification. In this paper, we propose a different method by extracting explicit features from flower images that can be employed to generate explanations. We take the benefit of feature extraction derived from the taxonomic characteristics of plants, with the orchids as an example domain. Feature classifiers were developed using deep neural networks. Two different methods were studied: (1) a separate deep neural network was trained for every individual feature, and (2) a single, multi-label, deep neural network was trained, combining all features. The feature classifiers were tested in predicting 63 orchid species using naive Bayes (NB) and tree-augmented Bayesian networks (TAN). The results show that the accuracy of the feature classifiers is in the range 83-93%. By combining these features using NB and TAN the species can be predicted with an accuracy of 88.9%, which is better than a standard pre-trained deep neural-network architecture, but inferior to a deep learning architecture after fine-tuning of multiple layers. The proposed novel feature extraction method still performs well for identification and is explainable, as opposed to black-box solutions that only aim for the best performance. Graphical abstract

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An Efficient Machine Learning Model for Identification of Seagrasses through Morphometrics

Dubey,

Thanikkal,

Dilipan

et al. 2024

2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)

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Plant Taxonomy in Hainan Based on Deep Convolutional Neural Network and Transfer Learning

Cited by 5 publications

References 25 publications

Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Deep neural networks for explainable feature extraction in orchid identification

An Efficient Machine Learning Model for Identification of Seagrasses through Morphometrics

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