Plant Classification Using Weighted k-NN Variants

Britto, Larissa; Pacífico, Luciano D. S.

doi:10.5753/eniac.2018.4404

Cited by 6 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The performance of the Extreme Learning Machine (ELM) approach in [54] greatest results for ELM. In [55], the author assessed the effectiveness of several machine learning algorithms for classifying herbal, fruit, and vegetable plants based on their leaves.…”

Section: Figure5 Features For Detection Of Medicinal Plant Leafmentioning

confidence: 99%

A Review on Detection of Medical Plant Images

Rao

Kumar

Konda

2023

IJRITCC

View full text Add to dashboard Cite

Both human and non-human life on Earth depends heavily on plants. The natural cycle is most significantly influenced by plants. Because of the sophistication of recent plant discoveries and the computerization of plants, plant identification is particularly challenging in biology and agriculture. There are a variety of reasons why automatic plant classification systems must be put into place, including instruction, resource evaluation, and environmental protection. It is thought that the leaves of medicinal plants are what distinguishes them. It is an interesting goal to identify the species of plant automatically using the photo identity of their leaves because taxonomists are undertrained and biodiversity is quickly vanishing in the current environment. Due to the need for mass production, these plants must be identified immediately. The physical and emotional health of people must be taken into consideration when developing drugs. To important processing of medical herbs is to identify and classify. Since there aren't many specialists in this field, it might be difficult to correctly identify and categorize medicinal plants. Therefore, a fully automated approach is optimal for identifying medicinal plants. The numerous means for categorizing medicinal plants that take into interpretation based on the silhouette and roughness of a plant's leaf are briefly précised in this article.

show abstract

Section: Figure5 Features For Detection Of Medicinal Plant Leafmentioning

confidence: 99%

A Review on Detection of Medical Plant Images

Rao

Kumar

Konda

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…In contrast, a poor selection of the k-value may result in an inaccurate evaluation of the new value. The choice of k-value must be appropriate, since using one that is too small may result in missing the trend, while using one that is too big can create issues with other variables [90][91][92][93]. Some researchers mentioned that the best match for the k-value is found by comparing multiple k-values to the data to minimise a specific inaccuracy [94].…”

Section: An Application Of K-nearest Neighbour Techniquementioning

confidence: 99%

Benchmarking Socio-Economic Impacts of High-Speed Rail Networks Using K-Nearest Neighbour and Pearson’s Correlation Coefficient Techniques through Computational Model-Based Analysis

2022

View full text Add to dashboard Cite

Not only have high-speed rail (HSR) services stimulated the economy of many countries, but they have also significantly uplifted quality of lives (QoL) of countless people. For many decades, the aspiration for HSR network development has dramatically risen, and HSR networks have inevitably become an icon of civilisation. However, only a few successful HSR networks globally can truly generate socio-economic impacts on their societies. This research aims to understand the impact of HSR networks on social and economic impacts and to provide recommendations for success. This study is the world’s first to examine the benefits of HSR across all community demographic groups, including young and elderly people. The findings will illustrate the QoL, economic, and educational elements’ advantages in explicit terms. It has established two interconnected models via Python to codify a novel customised model for socio-economic evaluation. ‘Pearson correlation coefficient’ and ‘K-Nearest Neighbour’ techniques are applied to bolster the reliability of the research findings. The outcomes have been reviewed by 30 international HSR specialists. The benchmarking exhibits that socio-economic impacts apparently occur across vast areas. The insight stemming from this benchmarking also offers policy implications and empirical data for long-term HSR improvement, assisting the government in developing new methods for sustainable communities.

show abstract

“…The final step in automatic plant recognition systems is the classification phase [Britto andPacifico 2018, Pacifico et al 2018b]. Some methods commonly employed as classifiers for plant identification systems are: K-Nearest Neighbors classifier [Cover and Hart 1967], Decision Tree classifier [Mitchell et al 1997], Naive Bayes classifier [Mitchell et al 1997, De Stefano et al 2012, Support Vector Machine [Haykin 2001] and Artificial Neural Networks [Haykin 2001].…”

Section: A Brief Review On Automatic Plant Identification Systemsmentioning

confidence: 99%

Classificação de Espécies de Plantas Usando Extreme Learning Machine

Britto¹,

Pacífico²

2019

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

View full text Add to dashboard Cite

Plants play an important role in nature, but correct plant species identification is still a challenging task for non-specialized people. Many works have been proposed towards the development of automatic plant species recognition systems through Machine Learning methods, but most of them lack the proper experimental analysis. In this work, we evaluate the performance of a general-purpose Artificial Neural Network to perform plant classification task: the Extreme Learning Machine (ELM).We compare ELM with several classifiers from plant recognition literature by means of three real-world data sets obtained from different image processing and feature extraction processes. A statistical hypothesis test is employed to perform proper experimental evaluation.

show abstract

Plant Classification Using Weighted k-NN Variants

Cited by 6 publications

References 10 publications

A Review on Detection of Medical Plant Images

A Review on Detection of Medical Plant Images

Benchmarking Socio-Economic Impacts of High-Speed Rail Networks Using K-Nearest Neighbour and Pearson’s Correlation Coefficient Techniques through Computational Model-Based Analysis

Classificação de Espécies de Plantas Usando Extreme Learning Machine

Contact Info

Product

Resources

About