Varietal classification of barley by convolutional neural networks

Kozłowski, Michał; Górecki, Przemysław; Szczypiński, Piotr M.

doi:10.1016/j.biosystemseng.2019.06.012

Cited by 55 publications

(28 citation statements)

References 21 publications

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“…An appropriate learning rate could enable the convergence of the target function to its local minimum at appropriate timing. Therefore, under the condition of guaranteeing regular training, it could reduce the training time cost by configuring the learning rate to an appropriate range [ 29 ].…”

Section: Results and Analysismentioning

confidence: 99%

Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network

Ren

Zhang

Sun

et al. 2021

Sensors

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A pepper quality detection and classification model based on transfer learning combined with convolutional neural network is proposed as a solution for low efficiency of manual pepper sorting at the current stage. The pepper dataset was amplified with data pre-processing methods including rotation, luminance switch, and contrast ratio switch. To improve training speed and precision, a network model was optimized with a fine-tuned VGG 16 model in this research, transfer learning was applied after parameter optimization, and comparative analysis was performed by combining ResNet50, MobileNet V2, and GoogLeNet models. It turned out that the VGG 16 model output anticipation precision was 98.14%, and the prediction loss rate was 0.0669 when the dropout was settled as 0.3, learning rate settled as 0.000001, batch normalization added, and ReLU as activation function. Comparing with other finetune models and network models, this model was of better anticipation performance, as well as faster and more stable convergence rate, which embodied the best performance. Considering the basis of transfer learning and integration with strong generalization and fitting capacity of the VGG 16 finetune model, it is feasible to apply this model to the external quality classification of pepper, thus offering technical reference for further realizing the automatic classification of pepper quality.

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Section: Results and Analysismentioning

confidence: 99%

Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network

Ren

Zhang

Sun

et al. 2021

Sensors

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“…CNN's are made up of input layers that receive the raw pixels of the image, thus the network's input number corresponds to the image size, that is, W (width) x H (height) times the colour channel number. In colour images the number of channels is equal to 3 (red, green and blue, for example), so an image 300 pixels wide, 300 pixels tall and with 3 colour channels, would give the CNN a 300x300x3 entry, totalling 270,000 entries [27].…”

Section: Proposed Cnn Modelmentioning

confidence: 99%

“…The various layers of convolution, implemented in this model, generate many representations of data and act as descriptors of characteristics, initially in the first layers with more common information and increasing the detailing of the characteristics as the number of layers deepens [16,22,26,27] In addition to the convolution layers, we have MaxPooling, which is used to decrease the dimensionality of the image, so that, faster training is possible with a smaller number of parameters [42].…”

Section: Proposed Cnn Modelmentioning

confidence: 99%

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Nutritional Evaluation of Brachiaria brizantha cv. marandu using Convolutional Neural Networks

et al. 2020

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The identification of plant nutritional stress based on visual symptoms is predominantly done manually and is performed by trained specialists to identify such anomalies. In addition, this process tends to be very time consuming, has a variability between crop areas and is often required for analysis at various points of the property. This work proposes an image recognition system that analyzes the nutritional status of the plant to help solve these problems. The methodology uses deep learning that automates the process of identifying and classifying nutritional stress of Brachiaria brizantha cv. marandu. An image recognition system was built and analyzes the nutritional status of the plant using the digital images of its leaves. The system identifies and classifies Nitrogen and Potassium deficiencies. Upon receiving the image of the pasture leaf, after a classification performed by a convolutional neural network (CNN), the system presents the result of the diagnosed nutritional status. Tests performed to identify the nutritional status of the leaves presented an accuracy of 96%. We are working to expand the data of the image database to obtain an increase in the accuracy levels, aiming at the training with a larger amount of information presented to CNN and, thus, obtaining results that are more expressive.

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“…As CNNs também foram utilizadas para identificação de formas em imagens, sendo aplicadas na identificação do tamanho e qualidade de grãos de cevada (KOZŁOWSKI et al, 2019), tal como na detecção e contagem de órgãos vegetais, no cultivo da uva (GRIMM et al, 2019). TRAN et al (2019) implementaram modelos de CNNs para realizar a identificação de deficiência dos macronutrientes Cálcio, Nitrogênio e Potássio.…”

Section: Introductionunclassified

Desenvolvimento de modelos de redes neurais convolucionais para avaliação nutricional da >i<Brachiaria brizantha>/i<

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One possibility to perform nutritional assessment on plants is by analyzing the symptoms visually presented on their leaves. This evaluation is carried out by the technique of leaf diagnosis by specialized people, most of which is done manually, thus requiring specialized labor, which makes it difficult to use, especially in the Amazon region where this labor is still scarce. The objective of this study was the development and apply of Convolutional Neural Networks (CNN) models, which perform the classification of the Brachiaria brizantha cv. Marandu nutritional status using the image of its leaves. Six CNN models were implemented and evaluated: one based on AlexNet and the pre-trained VGG-16, VGG-19, Inception-V3, ResNet-50 and MobileNetV2.All pre-trained models used transfer of learning that saves time and obtains a better result in the identification of deficiencies. To classify them, a set of image data was created, both for deficient and healthy leaves, grown in a greenhouse to serve as the information to be learned by the models during training. These models classify the deficiencies of Potassium, Nitrogen and Phosphorus, in addition to identifying whether the plant is healthy. This technology can improve the production of Brazilian pastures and, consequently, improve the number of animals per pasture area, thus contributing to a more sustainable production, especially in the Amazon region. Of all the models tested, the best accuracy was VGG-16 with 96.93%, in test data.

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Varietal classification of barley by convolutional neural networks

Cited by 55 publications

References 21 publications

Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network

Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network

Nutritional Evaluation of Brachiaria brizantha cv. marandu using Convolutional Neural Networks

Desenvolvimento de modelos de redes neurais convolucionais para avaliação nutricional da >i<Brachiaria brizantha>/i<

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