Lightweight Few Shot Model for Plant Leaf Disease Classification Using an Aggregated Loss Function

2022 ◽  
Author(s):  
Pradeep Singh ◽  
SHANKEY GARG
Keyword(s):  
Loss Function ◽  
Disease Classification ◽  
Plant Leaf ◽  
Leaf Disease

Plant Leaf Disease Classification Using Grid Search Based SVM

2020 ◽  
Author(s):  
Monu Bhagat ◽  
Dilip Kumar ◽  
Isharul Haque ◽  
Hemant Singh Munda ◽  
Ravi Bhagat
Keyword(s):  
Disease Classification ◽  
Grid Search ◽  
Plant Leaf ◽  
Leaf Disease

scholarly journals Fusing Convolutional Neural Networks to Improve the Accuracy of Plant Leaf Disease Classification

2020 ◽  
pp. 9-19
Author(s):  
Bhavana Nerkar ◽  
Sanjay Talbar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Disease Classification ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Highly Effective ◽  
Effective Network ◽  
Number Of Classes

Aims: This text aims to improve the accuracy of plant leaf disease detection using a fused convolutional neural network architecture Study Design:  In this study, propose a hybrid CNN architecture, that adds a bio-inspired layer to the existing CNN architecture in order to improve the accuracy and reduce the delay needed for leaf disease classification. Place and Duration of Study: National institute of electronics and information technology Aurangabad, between June 2018 and September 2020. Methodology: Convolutional neural networks (CNNs) have become a de-facto technique for classification of multi-dimensional data. Activation functions like rectified linear unit (ReLU), softmax, sigmoid, etc. have proven to be highly effective when doing so. Moreover, standard CNN architectures like AlexNet, VGGNet, Google net, etc. further assist this process by providing standard and highly effective network layer arrangements. But these architectures are limited by the speed due to high number of calculations needed to train and test the network. Moreover, as the number of classes increase, there is a reduction in validation and testing accuracy for the networks. In order to remove these drawbacks, hybrid CNN architecture, that adds a bio-inspired layer to the existing CNN architecture in order to improve the accuracy and speed of leaf classification. Results: The developed system was tested on different kinds of leaf diseases, and it was observed that the proposed system obtains more than 98% accuracy for both testing and validation sets. Conclusion: It is observed that the delay is reduced, while the accuracy is improved by the most effective classifiers. This encourage us to use the proposed system for real-time leaf image disease detection.

Plant leaf disease classification using deep neural network

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
T. Meera Devi ◽  
Arivazhagan T. Shangar ◽  
R. Yashwin ◽  
J.S. Shabhareesh ◽  
N. Kasthuri
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Disease Classification ◽  
Plant Leaf ◽  
Leaf Disease

scholarly journals DETECTION OF PLANT LEAF DISEASES IN AGRICULTURE USING RECENT IMAGE PROCESSING TECHNIQUES – A REVIEW

2019 ◽  
Vol 7 (1) ◽  
pp. 29-33
Author(s):  
Arpan Singh Rajput ◽  
Shailja Shukla ◽  
S. S. Thakur
Keyword(s):  
Image Processing ◽  
State Of The Art ◽  
Disease Classification ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Processing Techniques ◽  
Large Work ◽  
Respective Product ◽  
Automatic Technique

Purpose: Agricultural productivity is something on which the economy highly depends in India as well in all over the world. India is an agriculture-dependent country; wherein about 70% of the population depends on agriculture. Methodology: This is one of the main reasons that disease detection in agriculture plays an important role, as having the disease in plant leaf is quite natural. If proper observations are not taken in the agriculture field then it causes serious effects on plants due to which respective product quality and productivity are affected. Detection of plant leaf disease through effective and accurate automatic technique is beneficial at the starting stage as it reduces a large work of monitoring in big farms of crops. Result: This paper presents the review on the state of the art disease classification techniques presently used using image processing that can be used for plant leaf disease detection in agriculture.

Plant leaf disease classification using EfficientNet deep learning model

2021 ◽  
Vol 61 ◽  
pp. 101182
Author(s):  
Ümit Atila ◽  
Murat Uçar ◽  
Kemal Akyol ◽  
Emine Uçar
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Disease Classification ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Deep Learning Model
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