A comparative study of deep learning models with

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Deep Learning

Dr. Shamik Tiwari
(UPES University, India)

The classification of plants is one of the most important aims for botanists since
plants have a significant part in the natural life cycle. In this work, a leaf-based
automatic plant classification framework is investigated. The aim is to compare two
different deep learning approaches named Deep Neural Network (DNN) and deep
Convolutional Neural Network (CNN). In the case of deep neural network, hybrid
shapes and texture features are utilized as hand-crafted features while in the case
of the convolution non-handcraft, features are applied for classification. The offered
frameworks are evaluated with a public leaf database. From the simulation results,
it is confirmed that the deep CNN-based deep learning framework demonstrates
superior classification performance than the handcraft feature based approach.

Tiwari, S. (2020). A Comparative Study of Deep Learning Models With Handcraft
Features and Non-Handcraft Features for Automatic Plant Species
Identification. International Journal of Agricultural and Environmental
Information Systems (IJAEIS), 11(2), 44-57.

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This paper investigates the application of deep convolutional neural network (CNN) for herbal plant recognition through leaf identification. Traditional plant identification is often time-consuming due to varieties as well as similarities possessed within the plant species. This study shows that a deep CNN model can be created and enhanced using multiple parameters to boost recognition accuracy performance. This study also shows the significant effects of the multi-layer model on small sample sizes to achieve reasonable performance. Furthermore, data augmentation provides more significant benefits on the overall performance. Simple augmentations such as resize, flip and rotate will increase accuracy significantly by creating invariance and preventing the model from learning irrelevant features. A new dataset of the leaves of various herbal plants found in Malaysia has been constructed and the experimental results achieved 99% accuracy.

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The purpose of this study is to identify plant species using leaf venation features. Leaf venation features were obtained through the extraction of leaf venation features. The leaf image segmentation was performed to obtain the binary image of the leaf venation which is then determined the branching point and ending point. From these points, the extraction of leaf venation feature was performed by calculating the value of straightness, a different angle, length ratio, scale projection, skeleton length, number of segments, total skeleton length, number of branching points and number of ending points. So that from the extraction of leaf venation features 19 features were obtained. Identification of plant species was carried out using Support Vector Machine (SVM) with RBF kernel. The learning model was built using 75% of the training data. The testing results using 25% of the data on the training model, obtained an accuracy of 82.67%, with an average of precision of 84% and recall of 83%.

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Water Industry Process Automation & Control Monthly - April 2024

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STEAM NOZZLES AND TURBINES Flow of steam through nozzles, shapes of nozzles, effect of friction, critical pressure ratio, supersaturated flow - impulse and reaction principles, velocity diagram, work done and efficiency – types of compounding - governors. AIR COMPRESSORS Classification - working principle - type of compressors, work of compression with and without clearance - volumetric efficiency - isothermal and isentropic efficiency of reciprocating compressors - multistage air compressor with inter cooling.

Thermal Engineering -unit - III & IV.ppt

Thermal Engineering -unit - III & IV.ppt

Thermal Engineering -unit - III & IV.ppt

DineshKumar4165

data_management_and _data_science_cheat_sheet.pdf

data_management_and _data_science_cheat_sheet.pdf

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GAS POWER CYCLES Cycles: Otto, Diesel, Dual, Brayton - Calculation of mean effective pressure - Air standard efficiency - Comparison of cycles INTERNAL COMBUSTION ENGINES Classification - Components and their function - valve timing diagram and port timing diagram - actual and theoretical p-v diagram of two stroke and four stroke engines – carburettor - diesel pump and injector system - battery and magneto ignition system - principles of combustion and detonation in CI engines - lubrication and cooling systems - performance parameters and calculations.

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Thermal Engineering Unit - I & II . ppt

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DineshKumar4165

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FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads

FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads

FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads

Arindam Chakraborty, Ph.D., P.E. (CA, TX)

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ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf

ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf

ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf

N-Grade deals with the maintenance of university, department, faculty, student information within the university. N-Grade is an automation system, which is used to store the department, faculty, student, courses and information of a university. Starting from registration of a new student in the university, it maintains all the details regarding the attendance and marks of the students. The project deals with retrieval of information through an INTRANET based campus wide portal. It collects related information from all the departments of an organization and maintains files, which are used to generate reports in various forms to measure individual and overall performance of the students.

University management System project report..pdf

University management System project report..pdf

University management System project report..pdf

Unit 2- Effective stress & Permeability.pdf

Unit 2- Effective stress & Permeability.pdf

Unit 2- Effective stress & Permeability.pdf

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Water Industry Process Automation & Control Monthly - April 2024

Water Industry Process Automation & Control Monthly - April 2024

Water Industry Process Automation & Control Monthly - April 2024

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Thermal Engineering -unit - III & IV.ppt

Thermal Engineering -unit - III & IV.ppt

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Unit 2- Effective stress & Permeability.pdf

Unit 2- Effective stress & Permeability.pdf

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A comparative study of deep learning models with

1. Dr. Shamik Tiwari (UPES University, India)

2. The classification of plants is one of the most important aims for botanists since plants have a significant part in the natural life cycle. In this work, a leaf-based automatic plant classification framework is investigated. The aim is to compare two different deep learning approaches named Deep Neural Network (DNN) and deep Convolutional Neural Network (CNN). In the case of deep neural network, hybrid shapes and texture features are utilized as hand-crafted features while in the case of the convolution non-handcraft, features are applied for classification. The offered frameworks are evaluated with a public leaf database. From the simulation results, it is confirmed that the deep CNN-based deep learning framework demonstrates superior classification performance than the handcraft feature based approach.

3. Tiwari, S. (2020). A Comparative Study of Deep Learning Models With Handcraft Features and Non-Handcraft Features for Automatic Plant Species Identification. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 11(2), 44-57.