Classification and Grading of Okra-ladies finger using Deep Learning

Raikar, Meenaxi M.; Meena, S M; Kuchanur, Chaitra; Girraddi, Shantala; Benagi, Pratiksha

doi:10.1016/j.procs.2020.04.258

Cited by 61 publications

(19 citation statements)

References 14 publications

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“…The CNN structure involves image acquisition, convolutional layer, pooling layer, and fully connected layer. The research used four types of pre-trained CNN models (SqueezeNet (Ucar & Korkmaz, 2020), GoogLeNet (Raikar et al, 2020), ResNet-50 (Mkonyi et al, 2020), and AlexNet (Jiang et al, 2019)) provided in Matlab R2020b. SqueezeNet is a convolutional neural network that is 18 layers deep.…”

Section: Methodsmentioning

confidence: 99%

Untitled

2022

JST

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Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world.In order to lower production costs, Indonesian producers and retailers often mix highpriced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method's development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best

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Section: Methodsmentioning

confidence: 99%

Untitled

2022

JST

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“…CNN structures covered image acquisition, convolutional layer, pooling layer, and fully connected layer. The research used the deep learning application with four types of pretrained CNN models (SqueezeNet [35], GoogLeNet [36], Resnet50 [37], and AlexNet [38]) provided in MATLAB R2020b platform. ReLu activation function is given as:…”

Section: Methodsmentioning

confidence: 99%

Classification of water stress in cultured Sunagoke moss using deep learning

et al. 2021

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Water stress greatly determines plant yield as it affects plant metabolism, photosynthesis rate, chlorophyll content index, number of leaves, physiological, biochemical compound, and vegetative growth. The research aimed to detect and classify water stress of cultured Sunagoke moss into several categories i.e. dry, semi-dry, wet, and soak by using a low-cost commercial visible light camera combined with a deep learning model. Cultured Sunagoke moss is a commercial product which has the potential use as rooftop-greening and wall-greening material. This research compared the performance of four convolutional neural network models, such as SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional neural network model according to the training and validation result was ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this also gained an accuracy rate at 87.50%. However, the best result of the convolutional neural network model on data testing using confusion matrices on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128 mini-batch size, and average testing accuracy of 94.15%. It can be concluded that based on the overall results, convolutional neural network model seems promising as a smart irrigation system that real-time, non-destructive, rapid, and precise method when controlling water stress of plants.

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“…With these terms the author finds the number of hidden layers with test and training loss. The classification of Okra's plant disease [23], depending on pod length, using different techniques. Different models are used to recognize wormholes, insects, and pests with AlexNet, GoogleNet, and ResNet50.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

Kaur

Harnal

Tiwari

et al. 2022

Sensors

128

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Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

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Classification and Grading of Okra-ladies finger using Deep Learning

Cited by 61 publications

References 14 publications

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Classification of water stress in cultured Sunagoke moss using deep learning

Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

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