Deep feature-based plant disease identification using machine learning classifier

Hassan, Sk Mahmudul; Maji, Arnab Kumar

doi:10.1007/s11334-022-00513-y

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…[24]. After CNN's pre-trained feature extraction model extracts features, the features may contain greater dimensions that need more computation and redundant information.Therefore, before classifying fingerprints, we use a principal component analysis(PCA) to reduce the noise and the feature dimension before fingerprint classification [25]. PCA is a statistical method for discovering correlations between features and shrinking the dimensionality of the data.…”

Section: Principal Component Analysis(pca)mentioning

confidence: 99%

Integration of EfficientNetB0 and Machine Learning for Fingerprint Classification

Alhijaj,

Khudeyer

2023

IJCAI

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A fingerprint is a common form of biometric technology used in human identification. The classification of fingerprints is crucial in identification systems because it significantly reduces the time required to identify a person and allows for the possibility of using fingerprints to distinguish between genders and identify individuals. Fingerprints are the most reliable identifiers because they are unique and impossible to fake. As a method of personal identification, fingerprints remain the best and most trustworthy. Fingerprint classification is crucial in a wide variety of settings, such as airports, banks, and emergencies involving explosives and natural disasters. This study proposes a deep learning strategy for determining whether a fingerprint belongs to a male or female person. With the help of pre-trained convolutional neural networks (CNN) in computer vision and an extremely powerful tool that has achieved significant success in image classification and pattern recognition. This work includes the use of the SOCOFing fingerprint dataset for training and employing a state-of-the-art model for feature extraction called EfficientNetB0, which was trained on the ImageNet dataset. Then feeding the extracted features into a principal component analysis (PCA) to decrease the dimension of these features and random forest RF classifier for fingerprint classification. Lastly, the tests showed that the proposed strategy outperformed the previous categorization methods in terms of accuracy (99.91%), speed for execution time, and efficiency.Povzetek: V članku je opisana metoda globokega učenja za ugotavljanje, ki skoraj 100% ugotovi, ali prstni odtis pripada moškemu ali ženski.

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Section: Principal Component Analysis(pca)mentioning

confidence: 99%

Integration of EfficientNetB0 and Machine Learning for Fingerprint Classification

Alhijaj,

Khudeyer

2023

IJCAI

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“…Traditional diagnostic methods are often time-consuming and susceptible to human error. Therefore, automation and artificial intelligence technologies have significant potential for diagnosing and monitoring plant diseases [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…These networks are used in image classification, object recognition, face recognition and many other tasks. The main purpose of CNNs is to recognize features in data and learn these features in a hierarchical way [5].…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Detection by Using Mobilentv2 and Xception on Filtered and Enhanced Images

Yamaçli,

Yildirim

2024

ETJ

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The gathering, sorting, and processing of plant leaf images serves as the foundation for this study. These are crucial first steps in the plant health monitoring process that guarantee reliable findings. The work classifies and detects plant leaf photos, extracting data on plant health using state-of-the-art deep learning models like Xception and MobileNetV2. In order to assess the effectiveness of the system, additional filters are applied to the photos of plant leaves in order to adjust characteristics like brightness, contrast, sharpness, and blur. The study's results show that the deep learning models employed could accurately determine the health of plant leaves, offering important new information for related future research.

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“…The authors pointed out the datasets availability and influence in models performance. Hassan et al[54] study firmly ratified the need of technology to replace manual actions in decision making, from inception of image capturing to model decision making for image classification by CNN models. State of Art CNN Models…”

mentioning

confidence: 99%

Zero-Shot Transfer Learning Framework for Plant Leaf Disease Classification

Satya Rajendra Singh,

Sanodiya

2023

IEEE Access

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Agriculture's pivotal role in sustaining livelihoods and driving economic growth is widely recognized, yet various challenges like the adverse effects of climate change and limited resource availability hinder its productivity. Notably, plants are susceptible to various viruses and bacteria, impacting yield and food security. The emergence of deep learning, particularly convolutional neural networks (CNNs), has transformed agriculture by facilitating tasks such as disease detection. However, a significant challenge arises from the often unrealistic assumption that training and testing data share the same distribution. To address this, domain adaptation and transfer learning techniques have been employed, bridging the gap between different data distributions. Therefore, a novel framework named 'Zero-Shot Transfer Learning' is introduced. This addresses the challenge of improving classifier performance when trained on a source domain with different classes and tested on a target domain, exemplified by tomato and potato datasets. More specifically, in this framework, we include different CNN models along with techniques such as data augmentation, synthetic data generation, and robust discriminative losses, enhancing classifier performance in zero-shot scenarios. Extensive experiments on plant leaf disease classification under the zero-shot Transfer Learning assumption demonstrate the superiority of the proposed framework for effective disease classification. Ultimately, this framework holds the potential to promote crop yield optimization and ensure food security.

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Deep feature-based plant disease identification using machine learning classifier

Cited by 10 publications

References 28 publications

Integration of EfficientNetB0 and Machine Learning for Fingerprint Classification

Integration of EfficientNetB0 and Machine Learning for Fingerprint Classification

Plant Disease Detection by Using Mobilentv2 and Xception on Filtered and Enhanced Images

Zero-Shot Transfer Learning Framework for Plant Leaf Disease Classification

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