An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
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Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
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Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
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Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
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Review on Data Securing Techniques for Internet of Medical Things
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Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
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Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
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A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
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Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
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Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
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Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 4 | Issue - 3 | september 2022
Published
19 September, 2022
Rice cultivation in Nepal is effect by many factors, one of the main factor is rice leaf diseases which limits the crops production. Image classification of rice leaf classify different rice leaf diseases. Image dataset of rice leaf diseases is taken from open source platform. Pre-processing of image is done which is followed by feature extraction and classification of images. This thesis presents image classification of rice leaf diseases into four classes, namely: Brown Spot, Healthy, Hispa, Leaf Blast using Convolutional Neural Network (CNN) architecture EfficicentNet-B0 and EfficicentNet-B3 based on fine-tuning with cyclical learning rate and based on discriminative fine-tuning. It is found that the test accuracy of EfficientNet-B0 is 81.96% and EfficientNet-B3 is 85.12% based on fine-tuning with cyclical learning rate and the test accuracy of EfficienNet-B0 is 83.99% and EfficientNet-B3 is 89.18% based on discriminative fine-tuning for 15 epochs. The results also conclude that the CNN architectures work better with discriminative fine-tuning than on fine-tuning with cyclical learning rate. The classification models of EfficicentNet-B0 and EfficicentNet-B3 are evaluated by recall, precision and F1-score metrics.
KeywordsRice leaf diseases convolutional neural network EfficientNet discriminative fine-tuning fine-tuning cyclical learning rate
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