Comparison of Pre-Trained Models Using Transfer Learning for Detecting Plant Disease

Chellapandi, Bincy; Vijayalakshmi, M.; Chopra, Shalu

doi:10.1109/icccis51004.2021.9397098

Cited by 30 publications

(10 citation statements)

References 4 publications

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“…In this paper, a mobile application based on deep learning was created to classify tomato leaf diseases when a picture of the leaf plant was taken with a mobile camera. Transfer learning techniques of VGG16 [21], VGG19 [22], and MobileNet_v2 [23], [24] models were used in training the data in addition to using a proposed model for CNN [25]. The model that achieves the best result in the test dataset is converted into a tensorflow lite model (TFLM) open source deep learning framework to run pre-trained models on Android or iOS.…”

Section: Methodsmentioning

confidence: 99%

Design a mobile application to detect tomato plant diseases based on deep learning

Abdulla

Marhoon

2022

Bulletin EEI

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Plant diseases consider the most dominant matter for farmers' concerns becausethe operation of discovering and dealing with them requires accuracy, experience, and time. Therefore, this paper proposes an approach to classify seven varieties of tomato diseases using deep learning models. A dataset of 10448 images from PlantVillage and google utilize to train the deep learning (CNN models). The trained models proved their ability to classify with high accuracy, as the highest testing accuracy reached 95.71% for the proposed model for 50 epochs only. The resulted best model is published to a mobile application using the android studioplatform, this application enables the farmer to classify plant diseases accurately and easily. The proposed model and mobile application could be extended to classify as many plant diseases as possible.

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Section: Methodsmentioning

confidence: 99%

Design a mobile application to detect tomato plant diseases based on deep learning

Abdulla

Marhoon

2022

Bulletin EEI

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“…B.V. et al [ 33 ] utilized the flip operation for the purpose of increasing the count of images in the dataset. Chelleapandi et al [ 69 ] carried out five different data augmentation operations, including rotation, filling, flipping, zooming, and shearing, using the Keras library to enhance the dataset. Pandian et al [ 34 ] utilized neural style transfer, position and color augmentation, deep convolutional generative adversarial network, and principal component analysis to increase the number of images from 55,448 to 234,008.…”

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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“…Here 98.7% performance was achieved using ResNet50. For 38 types of damaged plant leaves, Chellapandi et al [24] compared deep learning and transfer learning models. VGG19, InceptionV3, ResNet50, Vgg16, InceptionResnetV2, DenseNet and MobileNet were the models used.…”

Section: Literature Reviewmentioning

confidence: 99%

“…• Detection of disease through large no of plants, using various techniques [6]. • In training model more classes of plant diseases can be added [24]. • Detecting different phases of disease in plant leaves [29].…”

Section: Challenges and Future Directionmentioning

confidence: 99%

A Survey on the State of Art Approaches for Disease Detection in Plants

Sharma

Yadav²,

Saini

2022

IJRITCC

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Agriculture is the main factor for economy and contributes to GDP. The growth of the economy of many countries is based on agriculture. As a result, the yield factor, quality and volume of agricultural products, play a critical role in economic development. Plant diseases and pests have become a major determinant of crop yields throughout the years, as such illnesses in plants offer a serious threat and impediment to higher yields or production in the agriculture industry. As a result, From the outset, it becomes the major duty to correctly monitor the plants, to detect diseases thoroughly, and to determine methods of controlling or monitoring these plant diseases pests in order to achieve a higher rate of production growth and minimal crop damage. Using machine vision, deep learning methods and tools for extracting and classifying features, It could be possible to build a reliable disease detection system. Numerous researchers have created and deployed various ways for detecting plant diseases and pests. The potential of these methods has been examined in this work.

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Comparison of Pre-Trained Models Using Transfer Learning for Detecting Plant Disease

Cited by 30 publications

References 4 publications

Design a mobile application to detect tomato plant diseases based on deep learning

Design a mobile application to detect tomato plant diseases based on deep learning

Plant Disease Detection and Classification: A Systematic Literature Review

A Survey on the State of Art Approaches for Disease Detection in Plants

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