Bacterial foraging optimization based adaptive neuro fuzzy inference system

Murugan, C. Arul; Sureshkumaar, G.; Kannan, Nithiyananthan; Thomas, Sunil

doi:10.11591/ijece.v10i4.pp3568-3575

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…However, some plant features, such as stem diameter, leaf number, and leaf area are not measured easily by such non-contact measurement methods [9]. Therefore, improving the quality of plant features estimation is one of the primary concerns of present-day plant management [10]- [15].…”

Section: Introductionmentioning

confidence: 99%

Digital image processing methods for estimating leaf area of cucumber plants

Ngo

Nguyen

et al. 2022

IJEECS

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Increasingly <span>emerging technologies in agriculture such as computer vision, artificial intelligence technology, not only make it possible to increase production. To minimize the negative impact on climate and the environment but also to conserve resources. A key task of these technologies is to monitor the growth of plants online with a high accuracy rate and in non-destructive manners. It is known that leaf area (LA) is one of the most important growth indexes in plant growth monitoring system. Unfortunately, to estimate the LA in natural outdoor scenes (the presence of occlusion or overlap area) with a high accuracy rate is not easy and it still remains a big challenge in eco-physiological studies. In this paper, two accurate and non-destructive approaches for estimating the LA were proposed with top-view and side-view images, respectively. The proposed approaches successfully extract the skeleton of cucumber plants in red, green, and blue (RGB) images and estimate the LA of cucumber plants with high precision. The results were validated by comparing with manual measurements. The experimental results of our proposed algorithms achieve 97.64% accuracy in leaf segmentation, and the relative error in LA estimation varies from 3.76% to 13.00%, which could meet the requirements of plant growth monitoring </span>systems.

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Section: Introductionmentioning

confidence: 99%

Digital image processing methods for estimating leaf area of cucumber plants

Ngo

Nguyen

et al. 2022

IJEECS

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“…Some research observes that the use of MRI considered bettering than CT scans. Similarly, Murugan et al [10], works with six class classification task. But, the accuracy was noted as 90% in plant disease classification.…”

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confidence: 99%

ADEMNET architecture: An innovative solution for adaptive multi-class balancing problem in image classification

Papanna Umalakshmi,

Sathyanarayana,

Chicktotlikere Nagappa

et al. 2024

IJEECS

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<span>In the field of medical image processing, achieving high performance in the classification of four types of dementia poses a significant challenge. This research presents a novel approach that outperforms existing methodologies, bringing about a transformative impact in this specialized domain. The method integrates the adaptive synthetic–nominal (ADASYN) technique with a DEMNET framework, resulting in a substantial performance improvement of 95.45% compared to current benchmarks. Through meticulous experimentation on a dementia dataset encompassing four distinct types, we consistently demonstrate significant enhancements achieved by the refined strategy. This innovation not only raises the performance standard but also provides a robust and adaptable solution that can be easily integrated into existing systems. The implications of this advancement open up new avenues for both research and practical applications. This work exemplifies the power of innovative approaches to push the limits of performance and establishes a new benchmark for excellence within this specific domain.</span>

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