CN7023 Artificial Intelligence and Machine Vision Sample
Introduction
Objective
The different application is used with the analysis of the classification and identification of the fruit with the use of deep learning techniques that is built with the feature extraction. The convolution neural network is analyzed with the self service that is categorized with the machine learning techniques. The main objective of this article is to identify and classify the fruits using the deep machine learning techniques (Gopal, 2020).
The online retailer is presented with the identification of the sales that is self serviced with the machine learning algorithm. This is addressed with the capturing of the data with the digital camera and sensor that is developed with the self service system. Convolution neural network is the large set of system that is used with the data categorization with the fruit image. The system depends on the use of various technologies that is enhanced with the identification process that is explicitly accessed with the convolution neural network that has more features.
Overview
A deep learning technique is used with the specification of the classification of fruit that is extracted over the various features. The automatic system is provided with the classification of fruit that is extracted with the neural network that is serviced with the fruit identification. The online retailer identifies the fruit with the supermarket that is presented with the agricultural field by adopting the different techniques.
The charges were deducted with the use of technology that is utilized with the deep learning techniques that is explicitly accessed with the use of convolution neural network. This model is accomplished with the use of convolution neural network that is obtained with the trained dataset based on performance. The deep learning model is accomplished with the utilization of the labeled data (Hu, 2021).
This constitutes the connected layers that is classified with the increased performance and accuracy that is developed with the deep learning model. This is utilized with the extraction of the feature that is classified with the increased performance that is described with the trained dataset.
Simulation
Deep learning techniques are specified with the computer that is performed with the classification that is extracted with the features. MATLAB is used with the classification of the fruit dataset that is connected with the different layers. The process is simulated with the deep CNN framework that is estimated with the robotic agriculture that is harvested with the modified with the inception method.
The experiment is conducted with the contextual information that is captured with the cultivation process that is measured with the localization accuracy. This fruit is represented with the evaluation process that is transformed with the watershed algorithm that is detected with the accuracy (Al-Hazaimeh, 2019). The fruit is detected with the evaluation of the accuracy that is decided with the cultivation practices.
This CNN framework is presented with the detection of fruits using the various machine learning techniques. Fruit detection is considered with the automatic harvesting that is determined with the crop automation that increases the accuracy and performance based on classification.
Dataset
Dataset consist of different images that are categorized with the different images that is downloaded with the kaggle dataset. This image is analyzed with the raspberry, strawberry and tomato. This RGB images were represented with the three different channels R, G and B that are utilized with the dataset.
The random channel is selected for various fruit that is analyzed with the selection of the dataset with the different image. Fruits were viewed with the different angle to represent the fruit classification that is classified and identified with the different fruits based on the classification and prediction.
Figure 1: Fruit dataset
Sometimes the fruit may be fresh or half eaten with the seeds that are analyzed with the different light effect that is decorated with the plastic bags. This black or white image is presented with the various challenges that is proposed with the vary challenges. The dataset is preprocessed and resized based on the cropped data that is removed with the additional information that is validated and trained with the different dataset (Tsujimoto, 2020).
The data over the dataset is trained dataset that is validated and trained over dataset that increases the accuracy and performance in identification of dataset. Dataset is break into validated and trained dataset that is validated with the stochastic gradient descent that is analyzed with the machine learning techniques.
Convolution Neural Networks
Convolution neural network is used with the different layers that are connected with the different size that includes the normalized layer, batch normalized layer based on different size. Convolution layer is considered with the neuron that is connected with the local region that is receptive field that is analyzed with the neurons.
This parameter is stride with the kernel that is connected with the fully numerous categories that is controlled with the connected layers. This is utilized with the numerous categories that are compromised with the different image size. CNN based accurate detection is classified with the fruit quality that is analyzed with the critical task.
This quality is controlled with the fruit harvesting that is analyzed with the performed with the internal and external damage over the various maturity and disease (Yadav, 2018).
CNN is controlled with the process that might determine internal and external damage that is adopted over the fruit quality control. This is identified with the papaya farm that is classified with the accuracy and performance that is explored with the effective classification.
This model is supported with the explored with the detection of the effective classification. This CNN model is identified with the normal condition that is classified with the increased accuracy that is compared with the support vector machine.
Convolution Neural Network Architecture
This proposed framework is used with the deep learning technique that is comprised with the feature extraction and classification. This input image is cropped with the unwanted information that is resized with the three colors such as red, green and blue.
This feature is analyzed with the unwanted information that is features that is applied over the connected layers. This two block is rectified with the convolution that is consecutive layers. The two dimensional feature is flattened with the one dimension that is flattened with the various categories that is accessed with the softmax function (Tripathi, 2021).
Each block is rectified with the linear unit that is offered with the non linear feature vector. This is applied with the inner and outer most layers that are connected with the various categories that have more probability over the classification layer.The CNN layer is applied with the outer most layers that are fully connected layer that is applied over the spatial dimension that is outermost layer.
The CNN layer is flattened with trained data that is analyzed with the different level that is applied with the outermost layer that is connected with the different layers. This is identified with the probability that categorizes the connected layers with the regularization of the network.
Trained dataset
The dataset is categorized with the different fruits that are analyzed with the various parameters that are utilized over the same type of fruits. The image is analyzed with the preprocessed and dataset that is resized that is validated and optimized with the learning data that is mini batch size is 128 with the initial learning rate 0.01.
This deep learning model is provided with the fruit identification and classification that is analyzed with the self service system that is analyzed with the accomplished dataset. Tensor flow is presented with the development of convolution neutral network that is allowed with the machine learning model (Sriwong, 2019).
This is designed and developed with the mathematical collection of workflow that is optimized with the evaluation and expression that is accessed with the cloud platform. MATLAB is the simulator tool that is used with the pre trained dataset that is analyzed with the imported model that is addressed with the multi fruit classifications.
Data Validation
The trained dataset is validated with the different technique that is trained during the network process that is required with the trained data. This CNN framework is analyzed with the same shape that is preprocessed with the data that is required with the recommended data. This trained deep learning method is used with the creation of convolution neural network that is based on time consuming feature (Salama, 2019).
This is type of task definition that is trained and developed based on validated process that is achieved with the optimal configuration. The data is pre defined and accessed with the input and output layer that is developed with the various visual classification that is applied with the trained network.
Assembling the average model is predicted with the increased accuracy that is provided with the predicted model. The trained data model is predicted with the various approaches that is adopted with the predicted dataset that is analyzed with the machine learning techniques.
Data Testing
After the successful training, the dataset is evaluated with the different process that is addressed over various dataset. This is depicted based on the confusion matrix that is classified with the result that is analyzed with the misclassification method. This dataset is provided with the noticed application that is processed and predicted with the various applications that is depicted with the confusion matrix that is classified and tested with the different application.
This performance is measured and secured with the fully connected dataset that is analyzed with the misclassification based system (Yiwere, 2019). The testing dataset is taken and analyzed with the various application that is analyzed with the behavioral aspect that is noted with the created model that is accessed with the fruit classification based created model. This is provided with the fruit based classification that is analyzed with the AlexNet architecture that is noted with the learning rate that is accessed with the augmentation process.
Deep Learning Algorithm
Deep learning is one of the machines learning technique that is developed with the computation address that is used with the identification and classification of the fruit dataset that is specified with the controlled data that is applied with the fruit classification dataset.
This is used with the fruit quality that might focus with the comprehensive survey that is analyzed with the image processing system that is offered with the better understanding. This CNN is used with the illustration of the various applications that is compared with the extensive review that is applied with the fruit processing system (Shahdoosti, 2019).
This provides the efficient dataset that is analyzed with the other hand that is accessed with the external and internal damage that is processed with the CNN to classify and address the various operations over the image classification techniques.
This algorithm is provided with the fruit classification that is determined with the existing survey that is offered with the various fruit classification techniques. This trained dataset is used with the prediction if the network architecture that is achieved with the optimal configuration that increases the benefit over the data. The trained model is preprocessed and analyzed with the data that increases the performance and accuracy.
Result Obtained
Test set
The dataset is preprocessed and analyzed with the cropped data that is validated and tested with the additional information. This trained and validated dataset is trained and validated over the various applications that is analyzed with the stochastic gradient descent that is analyzed with the validated dataset that is analyzed with the various epochs (Shamsolmoali, 2019).
The data is trained and accessed with the optimizer that is analyzed and augmented with the various classification methods. The image is trained with the augmented dataset that is validated with the trained dataset that is augmented over the random rotation between 0 degree and 90 degree.
Figure 2: Dataset is analyzed with Convolution neural network
The data is analyzed with the different pattern that is classified with the fruit classification that increases the improved accuracy. The neural network is analyzed with the increased accuracy that is 97.39% that is analyzed with the classification fruit identification.
This is provided with the classification accuracy that increases to improved accuracy. This is validated with the labeled data that is accurately accessed with the fruit classification and prediction that is trained with the different dataset (Bongulwar, 2021). The trained dataset is revealed with the increased accuracy that has more iteration with the various fruit specifications. The type of pattern is analyzed with the trained dataset that increases the accuracy and performance.
Figure 3: Type of Pattern
Accuracy
CNN architecture is used with the general categorization that is pre-trained with the various modifications that is applied and supported with the various features. This is analyzed with the various tools that might classify the various applications that is characterized with the objective that is observed and analyzed with the machine learning process (Rosciszqwskip, 2017). This CNN is supported and implemented with the use of MATLAB that increases the performance and accuracy with the observed data.
Figure 4: Accuracy per each epoch
The two different dataset is analyzed with the different fruit that includes the strawberry, raspberry and tomato and this is categorized based on various classifications. The accuracy for the two different fruit is identified as 97.39 and 96.91%.
Figure 5: Accuracy per each epoch
Confusion Matrix
The confusion matrix is developed with the fruit classification that is analyzed with the various categories of fruits (Murugesan, 2018). This matrix is drawn with the target class to the output class that measures the performance and this is analyzed with the raspberry, strawberry and tomato fruits.
Figure 6: Confusion Matrix
Critical analysis
The CNN model is used with the classification and prediction of the dataset that is analyzed with the fruit detection that increases the accuracy from 96.91 to 97.31%. This fruit classification is analyzed with the number of data that is extracted with the dataset that is analyzed with the fruit classifier. This is reviewed with the various characteristics that is detected over the various model that is accessed with the behavior of the data.
This RGB image is used with the CNN model to highlight the various dataset that is analyzed with the fruit classification and quality control that is accessed with the highlighted characteristics that is analyzed with the fruit classification techniques. The fruit were classified and analyzed with the different characteristics that increase the misclassification over the data that might increases the accuracy.
This is reviewed with the various applications that are offered with the complex detection that is classified with the fruit classification that is developed with the CNN model that is improved with the extracted with the classified model (Naranjo Torres, 2018). The RGB image is presented and controlled over the retail market that is applied with the general information that is categorized with the classified information.
This is highlighted with the various applications that is more complex and detected with the automatic harvest that is processed with the various techniques. After the training process, the data is proposed with the convolution layer that is depicted with the confusion matrix that provides the various accuracy percentages. This is depicted with the trained dataset that increases various accuracies for different fruits.
Conclusion
Thus the deep learning method based machine learning techniques is used with the image classification and prediction of fruit based on various characteristics that is applied with the simulation. The fruit is classified and the process is directed with the fruit classification that is applied with the various retailers. The fruit quality is determined based on the various applications that is used with the identification of internal and external damage of fruit that is estimated with the automatic harvesting.
This is processed with the various parameters that is accessed with the superior case that is qualified with the internal and external data that is analyzed with the CNN based approach. The accuracy is measured with the various classification based on the categories that is analyzed with the CNN model.
The accuracy is measured with the different fruits that are very superior and analyzed with the robotic harvesting sector. This deep learning model is classified and predicted over the developed application that is addressed with the self service system that is accomplished with the accuracy that is addressed with the dataset. Thus the accuracy for the two different fruit is identified as 97.39 and 96.91% respectively and this is represented as addressed with different dataset.
References
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