Progressive Transfer Learning Approach for Identifying the Leaf Type by Optimizing Network Parameters

Joshi, Deepa; Mishra, Vidyanand; Srivastav, Honey; Goel, Diksha

doi:10.1007/s11063-021-10521-x

Cited by 17 publications

(7 citation statements)

References 43 publications

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“…This model leverages three components, namely MLP, Bayesian Network, and QUEST, achieving an impressive accuracy score of 95.59%. In [30], progressive transfer learning was utilized to classify leaf types, referencing datasets like Flavia, LeafSnap, and MalayaKew (MK-D1 and MK-D2). The results revealed the following accuracy rates: Flavia 100%, MK-D1 99.05%, MK-D2 99.89%, and LeafSnap 97.95%.…”

Section: Discussionmentioning

confidence: 99%

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EfficientNet Ensemble Learning: Identifying Ethiopian Medicinal Plant Species and Traditional Uses by Integrating Modern Technology with Ethnobotanical Wisdom

Kiflie,

Sharma,

Haile

et al. 2024

Computers

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Ethiopia is renowned for its rich biodiversity, supporting a diverse variety of medicinal plants with significant potential for therapeutic applications. In regions where modern healthcare facilities are scarce, traditional medicine emerges as a cost-effective and culturally aligned primary healthcare solution in developing countries. In Ethiopia, the majority of the population, around 80%, and for a significant proportion of their livestock, approximately 90% continue to prefer traditional medicine as their primary healthcare option. Nevertheless, the precise identification of specific plant parts and their associated uses has posed a formidable challenge due to the intricate nature of traditional healing practices. To address this challenge, we employed a majority based ensemble deep learning approach to identify medicinal plant parts and uses of Ethiopian indigenous medicinal plant species. The primary objective of this research is to achieve the precise identification of the parts and uses of Ethiopian medicinal plant species. To design our proposed model, EfficientNetB0, EfficientNetB2, and EfficientNetB4 were used as benchmark models and applied as a majority vote-based ensemble technique. This research underscores the potential of ensemble deep learning and transfer learning methodologies to accurately identify the parts and uses of Ethiopian indigenous medicinal plant species. Notably, our proposed EfficientNet-based ensemble deep learning approach demonstrated remarkable accuracy, achieving a significant test and validation accuracy of 99.96%. Future endeavors will prioritize expanding the dataset, refining feature-extraction techniques, and creating user-friendly interfaces to overcome current dataset limitations.

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Section: Discussionmentioning

confidence: 99%

“…Current studies emphasize the growing significance of deep learning in identifying medicinal plants based on their leaves [29][30][31]. Increasingly, deep learning techniques are being employed for the identification and classification of both general plant groups and specific medicinal plant species [29,[32][33][34].…”

Section: Introductionmentioning

confidence: 99%

EfficientNet Ensemble Learning: Identifying Ethiopian Medicinal Plant Species and Traditional Uses by Integrating Modern Technology with Ethnobotanical Wisdom

Kiflie,

Sharma,

Haile

et al. 2024

Computers

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“…Two-class and multi-class SVM give 99% accuracy. In this study [7], deep learning was utilised to classify leaf images. Five Medicinal plants.…”

Section: Review Of Previous Workmentioning

confidence: 99%

Identification and classification of medicinal plants using leaf with deep convolutional neural networks

Bhelkar¹,

Sharma²

2022

ijhs

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The Indian medical practise of Ayurveda has gained international renown. Herbal preparations are the basis of Ayurveda medicine. The pharmaceutical industry is beginning to pay more attention to medicinal plants because they have fewer adverse effects and reactions than modern medicine and are also less expensive. In recent years, numerous Deep learning, machine learning algorithms that are both effective and reliable have been utilised for plant classifications by using images of leaf. In this work, 45 distinct medicinal plant leaves were used, and a deep learning model was applied in order to achieve a high degree of accuracy in the classification and recognition procedures that were carried out with the help of computer vision techniques. After categorising the leaves of numerous medicinal plants, the Xception model has a 97.65% accuracy rate.

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“…However, precisely delineating the boundary between the general and task-specific features is challenging. Various research endeavors substantiated the viability and applicability of TL across diverse domains, including medical (Albayrak, 2022; Wang et al , 2021; Yadlapalli et al , 2022), plant science (Joshi et al , 2021), mechanic (Mao et al , 2020) and additive manufacturing (Li et al , 2021). Nagorny et al (2020) used polarimetric images to train VGG16 and MobileNetV2 networks via TL approach for quality inspection of injection parts.…”

Section: Introductionmentioning

confidence: 99%

In-situ quality inspection system of injection parts based on transfer learning

Yang,

Shan,

Jin

et al. 2024

RIA

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Purpose This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology. Design/methodology/approach The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach. Findings Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%. Originality/value The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

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Progressive Transfer Learning Approach for Identifying the Leaf Type by Optimizing Network Parameters

Cited by 17 publications

References 43 publications

EfficientNet Ensemble Learning: Identifying Ethiopian Medicinal Plant Species and Traditional Uses by Integrating Modern Technology with Ethnobotanical Wisdom

EfficientNet Ensemble Learning: Identifying Ethiopian Medicinal Plant Species and Traditional Uses by Integrating Modern Technology with Ethnobotanical Wisdom

Identification and classification of medicinal plants using leaf with deep convolutional neural networks

In-situ quality inspection system of injection parts based on transfer learning

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