Automated Real-Time Identification of Medicinal Plants Species in Natural Environment Using Deep Learning Models—A Case Study from Borneo Region

Malik, Owais Ahmed; Ismail, Nazrul; Yahya, Umar

doi:10.3390/plants11151952

Cited by 27 publications

(4 citation statements)

References 27 publications

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“…It uses a computer vision system with a deep learning model trained on a dataset of plant images, a knowledge base, and a mobile application for user interaction (Tiwari et al, 2020). The author discussed how to use deep convolutional neural networks (CNN) to identify the leaves of medicinal plants (Malik et al, 2022). However, a deep CNN model can be developed and improved to speed up the identification process.…”

Section: Related Workmentioning

confidence: 99%

Effective medical leaf identification using hybridization of GMM-CNN

Rao,

Kumar,

Rao

2023

IJERR

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Medical plants play a vital role in curing many diseases. These plants, along with their leaves, have medicinal values. If these leaves are identified appropriately, they can be chosen directly to have more significant relief from the disease. Therefore, the identification of these medical species is a challenging task. The ecologically motivated Convolutional Neural Networks (CNNs) have substantially contributed to computer visual research. This article introduces a unique approach to medical leaf identification based on the hybridization of the Gaussian mixture model and a Convolutional Neural Network (GMM-CNN). The experimentation is performed on the Flavia dataset and is carried out using benchmark evaluation metrics. The parameters like index volume, probability of random index, and global consistency error are evaluated. The Python simulation model is utilized for the evaluation of the proposed methodology. The hybrid technique combining GMM and CNN has considerable potential in medical leaf identification. The experimental findings indicate that the hybrid strategy exhibits superior performances. The methodology suggested in this study demonstrates exceptional levels of accuracy, precision, and recall when applied to a wide range of medical leaf categories. Moreover, integrating Gaussian Mixture Model (GMM) and Convolutional Neural Network (CNN) addresses concerns associated with a scarcity of training data by offering a more resilient structure for extracting features and performing classification. By combining the advantages of statistical modelling with deep learning, we develop a resilient and precise system that has the potential to enhance botanical research, medical diagnostics, and environmental monitoring applications.

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Section: Related Workmentioning

confidence: 99%

Effective medical leaf identification using hybridization of GMM-CNN

Rao,

Kumar,

Rao

2023

IJERR

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“…We compared our method with the other conventional deep CNN architectures including ResNet-50 [38], DenseNet-121 [39], EffecientNet-b3 [40], ShuffleNet-v2 [41] and MobileNet-v3 [42]. Each of them was proved to be efficient in tomato disease classification.…”

Section: Accuracy Performancementioning

confidence: 99%

MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization

Yin

Zhao

et al. 2023

Agriculture

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Tomato disease classification based on images of leaves has received wide attention recently. As one of the best tomato disease classification methods, the convolutional neural network (CNN) has an immense impact due to its impressive performance. However, better performance is verified by independent identical distribution (IID) samples of tomato disease, which breaks down dramatically on out-of-distribution (OOD) classification tasks. In this paper, we investigated the corruption shifts, which was a vital component of OOD, and proposed a tomato disease classification method to improve the performance of corruption shift generalization. We first adopted discrete cosine transform (DCT) to obtain the low-frequency components. Then, the weight of the feature map was calculated by multiple low-frequency components, in order to reduce the influence of high-frequency variation caused by corrupted perturbation. The proposed method, termed as a multiple low-frequency attention network (MLFAnet), was verified by the benchmarking of ImageNet-C. The accuracy result and generalization performance confirmed the effectiveness of MLFAnet. The satisfactory generalization performance of our proposed classification method provides a reliable tool for the diagnosis of tomato disease.

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“…Therefore, we need an automated system that can identify the bamboo species quickly and without human involvement, relieving all stakeholders in the bamboo industry. Hence, a significant number of researchers have carried out investigations in favor of the automated categorization of plants according to their different characteristics in images [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

Sawarkar,

Shrimankar,

Ali

et al. 2024

Applied Sciences

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Bamboos, also known as non-timber forest products (NTFPs) and belonging to the family Poaceae and subfamily Bambusoideae, have a wide range of flowering cycles from 3 to 120 years; hence, it is difficult to identify species. Here, the focus is on supervised machine learning (ML) and deep learning (DL) as a potential automated approach for the identification and classification of commercial bamboo species, with the help of the majority multiclass voting (MajMulVot) algorithm. We created an image dataset of 2000 bamboo instances, followed by a texture dataset prepared using local binary patterns (LBP) and gray-level cooccurrence matrix (GLCM)-based methods. First, we deployed five ML models for the texture datasets, where support vector machine (SVM) shows an accuracy rate of 82.27%. We next deployed five DL-based convolutional neural network (CNN) models for bamboo classification, namely AlexNet, VGG16, ResNet18, VGG19, and GoogleNet, using the transfer learning (TL) approach, where VGG16 prevails, with an accuracy rate of 88.75%. Further, a MajMulVot-based ensemble approach was introduced to improve the classification accuracy of all ML- and DL-based models. The ML-MajMulVot enhanced the accuracy for the texture dataset to 86.96%. In the same way, DL-MajMulVot increased the accuracy to 92.8%. We performed a comparative analysis of all classification models with and without K-fold cross-validation and MajMulVot methods. The proposed findings indicate that even difficult-to-identify species may be identified accurately with adequate image datasets. The suggested technology can also be incorporated into a mobile app to offer farmers effective agricultural methods.

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Automated Real-Time Identification of Medicinal Plants Species in Natural Environment Using Deep Learning Models—A Case Study from Borneo Region

Cited by 27 publications

References 27 publications

Effective medical leaf identification using hybridization of GMM-CNN

Effective medical leaf identification using hybridization of GMM-CNN

MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

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