Implementation of CNN for Plant Leaf Classification

Diqi, Mohammad; Mulyani, Sri

doi:10.35842/ijicom.v2i2.28

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…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%

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: 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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“…Farmers can lower economic losses and increase overall farm profitability by modifying their harvesting schedules and marketing tactics in response to yield losses linked to certain diseases. 5. Providing Knowledge to Farmers: Deep learning models can be used to provide decision assistance tools that farmers can access via websites or mobile apps.…”

Section: Introductionmentioning

confidence: 99%

“…The deep CNN achieves 96.46% using validation data. [5]In this paper they develop CNN for the classification of plant petals photos that will enable users to identify different kinds of medicinal plants . The public can benefit from this research by learning to identify five different kinds of therapeutic plants, such as spinach Duri, Dadap Serep, moringa, and Javanese ginseng achieve training accuracy of 86% [6]They have created an automated method for classifying medicinal plants in order to speed up the identification of helpful plant species.…”

Section: Introductionmentioning

confidence: 99%

An Improved CNN model for Identifying Tomato Leaf Diseases

BHARALI

2024

tjes

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A major loss in gross domestic product, quantity and quality of products produced, as well as tomato production, is caused by diseases in tomato leaves due to which farmers have a difficult time in controlling and monitoring the health of tomato leaves, one of which is leaf disease. In our project, we developed an Enhanced CNN by using data augmentation techniques to identify the seven classes(blight ,leaf curl, leaf miner ,Alteneria, leaf spot, cutwork infected) of tomato leaf diseases. Using 27807 trainable parameters, the enhanced CNN obtains the maximum training accuracy of ninety nine point nine eight percent(99.98%) and validating accuracy of ninety eight point four percent(98.4%) .With fewer parameters, The Enhanced CNN can more accurately determine the type of illness of tomato leaf. Our Enhanced CNN model also determine the type of illness of tomato leaf when tested with the images of diseased tomato leaf collected from the internet sources.Using 152850 trainable parameters the enhanced CNN obtain the maximum training accuracy of 99.68% and validation accuracy of 89%.

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