Transfer learning on VGG16 for the Classification of Potato Leaves Infected by Blight Diseases

Akther, Jesmin; Harun-Or-Roshid, Muhammad; Nayan, Al-Akhir; Kibria, Muhammad Golam

doi:10.1109/etcce54784.2021.9689792

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…There will be a possibility of imbalance in the number of samples from each class, which can affect accuracy in classification. During the classification process, the application of the transfer learning model can also be used as the basis for a previously trained model, which will facilitate the classification process compared to doing the process raw or from a model that has not been trained before (Akther et al, 2021;Islam et al, 2019;Sagar & Jacob, 2020).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Potato Leaf Disease Identification With Transfer Learning Approach Using Mobilenetv1 Architecture

Brawijaya,

Rahmawati,

Haryanto

2024

pilar

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Diseases affecting potato leaves frequently lead to significant setbacks for farmers, reducing the overall harvest and the quality of the potatoes. Given the critical need for prompt disease detection, this research introduces the use of the MobileNet framework grounded in the Convolutional Neural Network (CNN) for adept detection of potato leaf ailments. During the research, potato leaf images undergo processing, and their distinct features are gleaned using CNN. Then, harnessing the MobileNet framework, these images undergo classification to ascertain the existence of diseases. The aspiration is that the formulated model can pinpoint diseases with notable precision, rapid feedback, and enhanced computational adeptness. Initial findings underscore the potential of this methodology in discerning potato leaf diseases, providing renewed optimism for farmers grappling with plant health issues. Experiments using the Transfer Learning approach showed good performance in classification and displayed a high accuracy rate of 99.2%.

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Section: Introductionmentioning

confidence: 99%

Optimization of Potato Leaf Disease Identification With Transfer Learning Approach Using Mobilenetv1 Architecture

Brawijaya,

Rahmawati,

Haryanto

2024

pilar

View full text Add to dashboard Cite

show abstract

“…Table 3 shows AlexNet obtained the best accuracy and precision on the COMBO 3 test dataset and SegNet comprises an encoder and decoder network and a pixel-wise classification layer. The encoder network has 13 convolutional layers that match the first 13 convolutional layers of the VGG16 [37]. We deleted the eventually linked layers from the SegNet encoder network to reduce the number of parameters and keep higher resolution feature maps at the deepest encoder output.…”

Section: B Performance Measurement Using Alexnetmentioning

confidence: 99%

Mediastinal Lymph Node Detection and Segmentation using Deep Learning

Nayan

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Automatic lymph node (LN) segmentation and detection for cancer staging are critical. In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal LNs. Despite its low contrast and variety in nodal size and form, LN segmentation remains a challenging task. Deep convolutional neural networks frequently segment items in medical photographs. Most state-of-the-art techniques destroy image's resolution through pooling and convolution. As a result, the models provide unsatisfactory results. Keeping the issues in mind, a well-established deep learning technique UNet++ was modified using bilinear interpolation and total generalized variation (TGV) based upsampling strategy to segment and detect mediastinal lymph nodes. The modified UNet++ maintains texture discontinuities, selects noisy areas, searches appropriate balance points through backpropagation, and recreates image resolution. Collecting CT image data from TCIA, 5-patients, and ELCAP public dataset, a dataset was prepared with the help of experienced medical experts. The UNet++ was trained using those datasets, and three different data combinations were utilized for testing. Utilizing the proposed approach, the model achieved 94.8% accuracy, 91.9% Jaccard, 94.1% recall, and 93.1% precision on COMBO_3. The performance was measured on different datasets and compared with state-of-the-art approaches. The UNet++ model with hybridized strategy performed better than others.

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A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

Gülmez

2024

Potato Res.

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This review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role in efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and the efficacy of prominent algorithms like ResNet, VGG, and MobileNet variants for disease classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random search, genetic algorithms, and Bayesian optimization are examined, and their impact on model performance is assessed. Challenges including dataset scarcity, variability in disease symptoms, and the generalization of models across diverse environmental conditions are addressed in the discussion section. Opportunities for advancing CNN-based disease detection, including the integration of multi-spectral imaging and remote sensing data, and the implementation of federated learning for collaborative model training, are explored. Future directions propose research into robust transfer learning techniques and the deployment of CNNs in real-time monitoring systems for proactive disease management in potato agriculture. Current knowledge is consolidated, research gaps are identified, and avenues for future research in CNN-based disease detection strategies to sustain potato farming effectively are proposed by this review. This study paves the way for future advancements in AI-driven disease detection, potentially revolutionizing agricultural practices and enhancing food security. Also, it aims to guide future research and development efforts in CNN-based disease detection for potato agriculture, potentially leading to improved crop management practices, increased yields, and enhanced food security.

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Transfer learning on VGG16 for the Classification of Potato Leaves Infected by Blight Diseases

Cited by 9 publications

References 25 publications

Optimization of Potato Leaf Disease Identification With Transfer Learning Approach Using Mobilenetv1 Architecture

Optimization of Potato Leaf Disease Identification With Transfer Learning Approach Using Mobilenetv1 Architecture

Mediastinal Lymph Node Detection and Segmentation using Deep Learning

A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture

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