Conventional Data Augmentation Techniques for Plant Disease Detection and Classification Systems

Talasila, Srinivas; Rawal, Kirti; Sethi, Gaurav

doi:10.1007/978-981-19-0011-2_26

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Data augmentation refers to generating a considerable amount of data from limited available data. This work employed rotation augmentation (45°, 90°, 135°, 180°, 225°, 270°, and 315°) and mirror symmetry augmentation (horizontal symmetry and vertical symmetry) (shown in Figure 3) [29]. Eight thousand images increased the training set pair with the mentioned augmentation techniques.…”

Section: Data Augmentationmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Leaf Region Segmentation Using High‐Resolution Super HAD CCD and ISOCELL GW1 Sensors

2023

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Super HAD CCD and ISOCELL GW1 imaging sensors are used for capturing images in high-resolution cameras nowadays. These high-resolution camera sensors were used in this work to acquire black gram plant leaf diseased images in natural cultivation fields. Segmenting plant leaf regions from the black gram cultivation field images is a preliminary step for disease identification and classification. It is also helpful for the farmers to assess the plants’ health and identify the diseases in their early stages. Even though plant leaf region segmentation has been effectively handled in many contributions, no universally applicable solution exists to solve all issues. Therefore, an approach for extracting leaf region from black gram plant leaf images is presented in this article. The novelty of the proposed method is that MobileNetV2 has been utilized as a backbone network for DeepLabv3+ layers to segment plant leaf regions. The DeepLabv3+ with MobileNetV2 segmentation model exhibited superior performance compared to the other models (SegNet, U-Net, DeepLabv3+ with ResNet18, ResNet50, Xception, and InceptionResNetV2) in terms of accuracy of 99.71%, Dice of 98.72%, and Jaccard/IoU of 97.47% when data augmentation was applied. The algorithms were developed and trained using MATLAB software. Each of the experimental trials reported in this article surpasses the prior findings.

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Section: Data Augmentationmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Leaf Region Segmentation Using High‐Resolution Super HAD CCD and ISOCELL GW1 Sensors

2023

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“…Data augmentation techniques helps to improve the designed model's performance and to reduce overfitting problem, which is crucial in deep learning. In this work, 15 data augmentation techniques were employed, such are belonging to rotation, mirror symmetry, illumination correction, shifting/translation, and noise injection [10] . Fig.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Black gram Plant Leaf Disease (BPLD) dataset for recognition and classification of diseases using computer-vision algorithms

et al. 2022

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“…Several data augmentation techniques are employed to expand the dataset's size and diversity, thereby improving the model's performance. The study also draws upon relevant research papers on plant disease detection using deep learning to bolster its findings [7].…”

Section: Black Gram Plant Leaf Disease and Deep Learningmentioning

confidence: 99%

Deep Learning-Based Classification of Black Gram Plant Leaf Diseases: A Comparative Study

YASIN,

KURSUN,

KOKLU

2023

icat

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The escalating incidence of plant diseases presents considerable obstacles to the agricultural domain, resulting in substantial reductions in crop yield and posing a threat to food security. To address the pressing concern of Black Gram Plant Leaf Diseases (BPLD), this research endeavors to tackle disease classification through the application of a deep learning methodology. The approach leverages a comprehensive dataset that encompasses Anthracnose, Leaf Crinkle, Powdery Mildew, and Yellow Mosaic diseases, all of which affect the black gram crop. By employing this advanced technique, we aim to contribute valuable insights to combat BPLD effectively. Our research applies deep learning models, including Darknet-53, ResNet-101, GoogLeNet, and EfficientNet-B0, to classify plant diseases. Darknet-53 achieved 98.51% accuracy, followed by ResNet-101 (97.51%), GoogLeNet (96.52%), and EfficientNet-B0 (77.61%). These findings demonstrate the potential of deep learning for accurate disease identification, benefiting agriculture. The study provides a comparative analysis of deep learning models for Black Gram Plant Leaf Disease (BPLD) classification, revealing Darknet-53 and ResNet-101 as superior performers. Implementing these models in real-world agricultural scenarios holds promise for early disease detection and intervention, reducing potential crop losses. The high accuracy achieved signifies significant progress in automating disease recognition, benefiting the agricultural sector.

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Conventional Data Augmentation Techniques for Plant Disease Detection and Classification Systems

Cited by 8 publications

References 17 publications

[Retracted] Deep Learning‐Based Leaf Region Segmentation Using High‐Resolution Super HAD CCD and ISOCELL GW1 Sensors

[Retracted] Deep Learning‐Based Leaf Region Segmentation Using High‐Resolution Super HAD CCD and ISOCELL GW1 Sensors

Black gram Plant Leaf Disease (BPLD) dataset for recognition and classification of diseases using computer-vision algorithms

Deep Learning-Based Classification of Black Gram Plant Leaf Diseases: A Comparative Study

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