Evaluation of Data Augmentation for Image-Based Plant-Disease Detection

Kobayashi, Kenichi; Tsuji, Junpei; Noto, Masato

doi:10.1109/smc.2018.00379

Cited by 18 publications

(7 citation statements)

References 6 publications

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“…The results generated were under a controlled environment [17]. Interesting research was performed utilizing a fuzzy classifier, principal of grouping both negative and positive data set points into fuzzy groups [18]. These groups are then given a membership function defined by the truth value.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Rehman¹,

Anwar²,

Tufail³

2020

ISI

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Plants disease identification plays a major role in agriculture yield prevention. Traditionally, manual plant surface examination is conducted which is time-consuming and relatively less efficient. Therefore, this study incorporates machine vision-based techniques, for plant disease identification (i.e. healthy leaf, Alternaria Alternate, and bacterial blight). The developed method employs a dataset comprised of more than 10,000 data points. Initially, Image processing is performed followed by image pre-processing techniques for noise removal. Subsequently, image segmentation is performed for the region of interest (ROI). A multiclass classifier is introduced for plant disease analysis, which results in a percentage of disease spreading in healthy leaf. The results were compared with other methods and it is evident that the developed method has shown 95% accuracy in plant disease identification.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Rehman¹,

Anwar²,

Tufail³

2020

ISI

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“…Kobayashi et al [ 67 ] utilized several augmentation techniques, including rotation, shear conversion, cutout, and horizontal and vertical direction, to expand the size of the dataset in order to improve detection accuracy. Geetharamani et al [ 16 ] utilized augmentation operations such as flipping, principal component analysis, rotation, scaling, noise injection, and gamma correction to expand the dataset’s size to approximately 61,400 images.…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Detection and Classification: A Systematic Literature Review

Ramanjot

Mittal

Wadhawan

et al. 2023

Sensors

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A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.

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“…Frechet Inception Distance (Fid) [34] was used to evaluate GAN designs. We produced 1000 real-time segmented photos and 4000 PlantVillage images for this investigation.…”

Section: Dataset and Experimental Designmentioning

confidence: 99%

A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

Vasudevan¹,

Karthick²

2023

Computer Systems Science and Engineering

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Crop protection is a great obstacle to food safety, with crop diseases being one of the most serious issues. Plant diseases diminish the quality of crop yield. To detect disease spots on grape leaves, deep learning technology might be employed. On the other hand, the precision and efficiency of identification remain issues. The quantity of images of ill leaves taken from plants is often uneven. With an uneven collection and few images, spotting disease is hard. The plant leaves dataset needs to be expanded to detect illness accurately. A novel hybrid technique employing segmentation, augmentation, and a capsule neural network (CapsNet) is used in this paper to tackle these challenges. The proposed method involves three phases. First, a graph-based technique extracts leaf area from a plant image. The second step expands the dataset using an Efficient Generative Adversarial Network E-GAN. Third, a CapsNet identifies the illness and stage.The proposed work has experimented on real-time grape leaf images which are captured using an SD1000 camera and PlantVillage grape leaf datasets. The proposed method achieves an effective classification of accuracy for disease type and disease stages detection compared to other existing models.

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Evaluation of Data Augmentation for Image-Based Plant-Disease Detection

Cited by 18 publications

References 6 publications

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Plant Disease Detection and Classification: A Systematic Literature Review

A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

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