MYLPHerb-1: A Dataset of Malaysian Local Perennial Herbs for the Study of Plant Images Classification under Uncontrolled Environment

Pushpanathan, Kalananthni; Hanafi, Marsyita; Masohor, Syamsiah; Ilahi, Wan Fazilah Fazlil

doi:10.47836/pjst.30.1.23

Cited by 5 publications

(1 citation statement)

References 47 publications

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“…Analyzing the efficiency of deep learning across a wide range of tasks involving medicinal plants and identifying the dominant deep learning classifier algorithms employed for these tasks poses an intricate difficulty. An analysis of primary research reveals that deep learning methods, encompassing families like VGG16 (Paulson and Ravishankar, 2020;Pudaruth et al, 2021), VGG19 (Paulson andRavishankar, 2020;Pushpanathan et al, 2022), CNN (Akter and Hosen, 2020;Indrani et al, 2020;P and Patil, 2020;Paulson and Ravishankar, 2020), MobileNetV2 (Abdollahi, 2022), DenseNet (Banita Pukhrambam and Sahayadhas, 2022;Oppong et al, 2022), Faster-RCNN (Senevirathne et al, 2020 and Xception (Quoc and Hoang, 2020; Roopashree and Anitha, 2021), have attained accuracy levels surpassing 97% for tasks linked to categorizing, recognizing, and segmenting Medicinal Plant Species.…”

Section: Deep Learning Tasks and Methodsmentioning

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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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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