Convolutional Neural Network (CNN) for gland images classification

Haryanto, Toto; Wasito, Ito; Suhartanto, Heru

doi:10.1109/icts.2017.8265646

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…Li et al in [28] enhance the performance of CNN by changing the number of Convolution layers and the Pooling layers in models such as VGG, AlexNet, and CaffeNet in the classification of distant sensing scene and their results confirmed that the number of layers are directly proportional to the accuracy of the model as reported by Sinha et al and Haryanto et al in [29] and [30] respectively. In the same vein, in [31], Zhang et al determine the accuracy and scalability of Tomato ripeness employing the use of CNN in the design of Tomato reaping robot for classification and prediction.…”

Section: Review Of Related Workmentioning

confidence: 57%

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Peter¹,

Damuut²,

Abdulkadir³

2020

AJNNA

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In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.

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Section: Review Of Related Workmentioning

confidence: 57%

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Peter¹,

Damuut²,

Abdulkadir³

2020

AJNNA

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“…Li, Xia, Du, Lin, & Samat (2017) altered the number of pooling layers and convolutional layers in three pretrained networks to improve their performance. The results obtained suggest a correlation between the accuracy of classification and the number of layers as seen in (Sinha, Verma, & Haidar, 2017;Haryanto, Wasito, & Suhartanto, 2017 (Peter et al, 2017;Peter & Abdulkadir, 2018) for determining the optimum ripeness of maize from its leaves. However, Peter et al (2020) stated that maize leaves are not very reliable for classification purposes.…”

Section: Introductionmentioning

confidence: 86%

Determining the Optimum Maturity of Maize Using Googlenet

Peter¹

2021

FJS

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Climatic changes, animal and human activities that lead to desertification and deforestation have affected the increase in agricultural produce especially in sub-Sahara Africa. Several efforts have been put in place to reduce these effects. However, that has not fully resolved the problem food shortages due to the growing population in sub-Sahara Africa. The application of image processing and convolutional neural network in the determination of the optimum maturity of SAMMAZ 17 variety of maize plant is being considered to mitigate for the shortage of food production. The optimum maturity is determined by using GoogleNet pre trained network on 3000 samples of maize comb captured using a camera at different maturity stages in a farmland. GoogleNet pre-trained network gave an accuracy of 82.44%. The result obtained showed a 10.44% improvement over an earlier result using Alexnet pre-trained network. The results suggest that when made operational there is a window of opportunity for increase in the production of food in sub-Sahara Africa

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“…The output is used as the input of the convolution layer at the fast connection, and the output of the former layer is connected to the next convolution layer. The output of this convolution layer and the output of the convolution layer on the fast connection line are used as the input of the next stage [14]. Here we call it Class B residual block, and the structure is shown in Figure 4.…”

Section: Residual Block Designmentioning

confidence: 99%

Detection of pig based on improved RESNET model in natural scene

Song

Fang

Wang

et al. 2021

Applied Mathematics and Nonlinear Sciences

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The behaviours of the pig are often closely related to their health. Pig recognition is very important for pig behaviour analysis and digital breeding. Currently, the early signs and abnormal behaviours of sick pigs in breeding farms are mainly completed by human observation. However, visual inspection is labour intensive and time-consuming, and it suffers from the problems of individual experiences and varying environments. An improved ResNet model was proposed and applied to detect individual pigs in this study based on deep learning knowledge. The developed model captured the features of pigs applying across layer connections, and the ability of feature expression was improved by adding a new residual module. The number of layers was reduced to minimise the net complexity. Generally, the ResNet frame was developed by reducing the number of convolution layers, constructing different types of the residual module and adding the number of convolution kernels. The training accuracy and testing accuracy reached 98.2% and 96.4%, respectively, when using the improved model. The experiment results showed that the method proposed in this paper for checking living situations and disease prevention of commercial pigs in pig farms is potential.

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Convolutional Neural Network (CNN) for gland images classification

Cited by 12 publications

References 16 publications

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Determining the Optimum Maturity of Maize Using Googlenet

Detection of pig based on improved RESNET model in natural scene

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