Plant classification based on stacked autoencoder

Yang, Mengmeng; Nayeem, Arifur; Shen, Lingling

doi:10.1109/itnec.2017.8284906

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…The proposed model for automated cultivar differentiation in CAU yields highly promising results, surpassing existing methods in the literature by a significant margin [4][5][6][7]. Its efficacy and robustness are evident in the swift processing time of only 0.34 s per image, enabling the accurate identification of CAU cultivars.…”

Section: Discussionmentioning

confidence: 86%

“…While these approaches have proven efficacious in certain scenarios, they are notably deficient in their capacity to accurately classify specific cultivars such as CAU. This limitation primarily stems from their inability to discern the intricate nuances and complex attributes that are unique to various CAU cultivars, resulting in inefficiencies and inaccuracies in classification processes [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Innovative Use of an Artificial Multiple Intelligence System (AMIS): The adaptation of AMIS for decision fusion in the P-AMIS-E model optimizes the classification accuracy. 4.…”

mentioning

confidence: 99%

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Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

Sriprateep,

Khonjun,

Golinska-Dawson

et al. 2024

Mathematics

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The classification of certain agricultural species poses a formidable challenge due to their inherent resemblance and the absence of dependable visual discriminators. The accurate identification of these plants holds substantial importance in industries such as cosmetics, pharmaceuticals, and herbal medicine, where the optimization of essential compound yields and product quality is paramount. In response to this challenge, we have devised an automated classification system based on deep learning principles, designed to achieve precision and efficiency in species classification. Our approach leverages a diverse dataset encompassing various cultivars and employs the Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning model (P-AMIS-E). This model integrates ensemble image segmentation techniques, including U-Net and Mask-R-CNN, alongside image augmentation and convolutional neural network (CNN) architectures such as SqueezeNet, ShuffleNetv2 1.0x, MobileNetV3, and InceptionV1. The culmination of these elements results in the P-AMIS-E model, enhanced by an Artificial Multiple Intelligence System (AMIS) for decision fusion, ultimately achieving an impressive accuracy rate of 98.41%. This accuracy notably surpasses the performance of existing methods, such as ResNet-101 and Xception, which attain 93.74% accuracy on the testing dataset. Moreover, when applied to an unseen dataset, the P-AMIS-E model demonstrates a substantial advantage, yielding accuracy rates ranging from 4.45% to 31.16% higher than those of the compared methods. It is worth highlighting that our heterogeneous ensemble approach consistently outperforms both single large models and homogeneous ensemble methods, achieving an average improvement of 13.45%. This paper provides a case study focused on the Centella Asiatica Urban (CAU) cultivar to exemplify the practical application of our approach. By integrating image segmentation, augmentation, and decision fusion, we have significantly enhanced accuracy and efficiency. This research holds theoretical implications for the advancement of deep learning techniques in image classification tasks while also offering practical benefits for industries reliant on precise species identification.

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Section: Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

Sriprateep,

Khonjun,

Golinska-Dawson

et al. 2024

Mathematics

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“…SAE is suitable for classification problems. For instance, in plant classification [14], three different auto-encoders were evaluated. The method achieved up to 93% accuracy for classification.…”

Section: Related Workmentioning

confidence: 99%

Tuning Stacked Auto-encoders for Energy Consumption Prediction: A Case Study

Öztürk¹

2019

IJITCS

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Energy is a requirement for electronic devices. A processor is a substantial part of computer components in terms of energy consumption. A great concern has risen over recent years about computers with regard to the energy consumption. Taking accurate information about energy consumption of a processor allows us to predict energy flow features. However, using traditional classifiers may not enhance the accuracy of the prediction of energy consumption. Deep learning shows great promise for predicting energy consumption of a processor. Stacked auto-encoders has emerged a robust type of deep learning. This work investigates the effects of tuning stacked auto-encoder in computer processor with regard to the energy consumption. To search parameter space, a grid search based training method is adopted. To prepare data to prediction, a data preprocessing algorithm is also proposed. According to the obtained results, on average, the method provides 0.2% accuracy improvement along with a remarkable success in reducing parameter tuning error. Further, in receiver operating curve analysis, tuned stacked auto-encoder was able to increase value of are under the curve up to 0.5.

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ZS-DML: Zero-Shot Deep Metric Learning approach for plant leaf disease classification

Zabihzadeh,

Masoudifar

2023

Multimed Tools Appl

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Plant classification based on stacked autoencoder

Cited by 3 publications

References 24 publications

Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

Tuning Stacked Auto-encoders for Energy Consumption Prediction: A Case Study

ZS-DML: Zero-Shot Deep Metric Learning approach for plant leaf disease classification

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