Identification of Medicinal Plants in Ardabil Using Deep learning : Identification of Medicinal Plants using Deep learning

Abdollahi, Jafar

doi:10.1109/csicc55295.2022.9780493

Cited by 30 publications

(11 citation statements)

References 17 publications

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“…Specifically, 93.5% (n=29) of these studies opted for classification techniques. Additionally, certain studies explored alternative avenues, including (Abdollahi, 2022), Mask-RCNN (Almazaydeh et al, 2022), PNN (Azeez and Rajapakse, 2019), and Xception (Quoc and Hoang, 2020; Roopashree and Anitha, 2021).…”

Section: Deep Learning Task and Methodsmentioning

confidence: 99%

“…In terms of feature extraction, most primary studies 83.8% (n=26) used a pre-trained model with transfer learning. Only a small percentage of studies 16.1% (n=5) used alternative techniques such as digital morphological changes (P and Patil, 2020), morphological changes (Abdollahi, 2022), attention-based feature map (Akter and Hosen, 2020), Susuki Algorithm (Diqi and Mulyani, 2021), and Zernik, Hu for shape extraction, as well as GLCM for texture extraction (Muneer and Fati, 2020).…”

Section: Studied Organs and Feature Extraction Techniquesmentioning

confidence: 99%

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Deep learning for medicinal plant species classification and recognition: a systematic review

Mulugeta,

Sharma,

Mesfin

2024

Front. Plant Sci.

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Knowledge of medicinal plant species is necessary to preserve medicinal plants and safeguard biodiversity. The classification and identification of these plants by botanist experts are complex and time-consuming activities. This systematic review’s main objective is to systematically assess the prior research efforts on the applications and usage of deep learning approaches in classifying and recognizing medicinal plant species. Our objective was to pinpoint systematic reviews following the PRISMA guidelines related to the classification and recognition of medicinal plant species through the utilization of deep learning techniques. This review encompassed studies published between January 2018 and December 2022. Initially, we identified 1644 studies through title, keyword, and abstract screening. After applying our eligibility criteria, we selected 31 studies for a thorough and critical review. The main findings of this reviews are (1) the selected studies were carried out in 16 different countries, and India leads in paper contributions with 29%, followed by Indonesia and Sri Lanka. (2) A private dataset has been used in 67.7% of the studies subjected to image augmentation and preprocessing techniques. (3) In 96.7% of the studies, researchers have employed plant leaf organs, with 74% of them utilizing leaf shapes for the classification and recognition of medicinal plant species. (4) Transfer learning with the pre-trained model was used in 83.8% of the studies as a future extraction technique. (5) Convolutional Neural Network (CNN) is used by 64.5% of the paper as a deep learning classifier. (6) The lack of a globally available and public dataset need for medicinal plants indigenous to a specific country and the trustworthiness of the deep learning approach for the classification and recognition of medicinal plants is an observable research gap in this literature review. Therefore, further investigations and collaboration between different stakeholders are required to fulfilling the aforementioned research gaps.

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Section: Deep Learning Task and Methodsmentioning

confidence: 99%

Section: Studied Organs and Feature Extraction Techniquesmentioning

confidence: 99%

Deep learning for medicinal plant species classification and recognition: a systematic review

Mulugeta,

Sharma,

Mesfin

2024

Front. Plant Sci.

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“…Hussin N A C proposed a shape feature extraction method to identify plants by scale invariant feature transformation (SIFT) and gridded color moments (GBCM) for color feature extraction [11]. Abdollahi J carried out a research work on the use of convolutional neural networks (CNN) to differentiate leaf species in India [12]. Prasad S proposed a new method for extracting features from natural images such as plant leaves for the automatic identification of living plant species [13].…”

Section: Related Workmentioning

confidence: 99%

A Quantitative Plant Type Recognition algorithm based on Gene Expression Programming

Deng

Song

2023

J. Phys.: Conf. Ser.

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Efficient and accurate plant type identification is an important foundation for biological research. The existing traditional machine learning classification algorithms have the defects of low classification accuracy in solving plant type recognition and cannot quantitatively give the corresponding class recognition model. In this paper, we propose a Quantitative Plant Type Recognition algorithm based on Gene Expression Programming (QPTR-GEP). Firstly, the plant type data set to be processed is preprocessed; then, the GEP population corresponding to plant type identification is constructed, and the corresponding quantitative identification function expressions are obtained after genetic operations such as mutation, recombination and transposition; finally, the classification expression with the highest fitness is used as the classifier corresponding to a single plant type, and the classifier model integrating the data set containing all plant type is put into the data set for plant type identification. Experimental results on the open source Iris demonstrate that our proposed algorithm outperforms SVM and Decision Tree algorithms for plant type identification.

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“…The research landscape in medicinal plant identification through image processing and deep learning algorithms encompasses various studies with notable achievements. Abdollahi et al [1] achieved an accuracy of 98.05% using a deep convolutional neural network to identify 30 plants in Iran. Abhinav and Amal [2] developed a real-time web application with CNN-based processing for plant identification.…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

“…In research led by Abdollahi and colleagues [1], a powerful deep learning network achieved 98.05% accuracy in identifying 30 medicinal plants. The proposed model went even further, reaching an impressive 99% accuracy.…”

Section: Introductionmentioning

confidence: 99%

HerbaVisionNet: Optimized CNN And Resnet50v2 Model For Enhanced Medicinal Plant Identification And Application Design

Goyal,

Garg,

Goyal

2023

ijmst

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Addressing the escalating demand for medicinal plants, this study undertakes the critical task of establishing an efficient identification system. Employing Convolutional Neural Networks (CNNs) with ResNet50v2 architecture, the project endeavours to develop a robust model capable of classifying 30 distinct medicinal plant types from high-resolution images. This classification relies on the precision of feature extraction to ensure accurate identification. Notably, the dataset used is meticulously curated to ensure its adaptability to the diverse botanical characteristics inherent in medicinal plants. Central to achieving high accuracy is the ResNet50v2 model, which forms the cornerstone of the project. Integrated into a real-time web application implemented with Flask, this model facilitates swift and accessible plant identification for various users, including researchers, herbalists, and enthusiasts. The study underscores the efficacy of deep learning algorithms in medicinal plant identification, showcasing remarkable accuracy and reliability in classification tasks. Beyond mere identification, the model demonstrates an ability to precisely categorize plant species, thereby bridging the gap between identification and exploration. This precision is underscored by an exceptional 99% accuracy rate in distinguishing between 30 medicinal plant species, attributed to meticulous model training with a diverse, high-resolution dataset capturing intricate plant features. Continual efforts are directed towards expanding the dataset to encompass variations in real-world scenarios. Strategies to address class imbalance through oversampling techniques and class weight adjustments are being rigorously pursued. Additionally, the model undergoes refinement through fine-tuning its architecture and integrating advanced data augmentation techniques. The research significantly contributes to the broader field of AI applications in sustainable practices, particularly in the conservation of medicinal plant resources. Serving as a potent tool for accurate identification, it holds promising implications for future advancements in deep learning and botanical research.

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Identification of Medicinal Plants in Ardabil Using Deep learning : Identification of Medicinal Plants using Deep learning

Cited by 30 publications

References 17 publications

Deep learning for medicinal plant species classification and recognition: a systematic review

Deep learning for medicinal plant species classification and recognition: a systematic review

A Quantitative Plant Type Recognition algorithm based on Gene Expression Programming

HerbaVisionNet: Optimized CNN And Resnet50v2 Model For Enhanced Medicinal Plant Identification And Application Design

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