scholarly journals Tomato leaf disease classification by exploiting transfer learning and feature concatenation

2022 ◽  
Author(s):  
Mehdhar S. A. M. Al‐gaashani ◽  
Fengjun Shang ◽  
Mohammed S. A. Muthanna ◽  
Mashael Khayyat ◽  
Ahmed A. Abd El‐Latif
Keyword(s):  
Transfer Learning ◽  
Tomato Leaf ◽  
Disease Classification ◽  
Leaf Disease

Transfer Learning based Rice Leaf Disease Classification with Inception-V3

2021 ◽  
Author(s):  
Kotharu Uma Venkata Ravi Teja ◽  
Bhumula Pavan Venkat Reddy ◽  
Likhitha Reddy Kesara ◽  
Kotaru Drona Phani Kowshik ◽  
Lakshmi Anchitha Panchaparvala
Keyword(s):  
Transfer Learning ◽  
Disease Classification ◽  
Rice Leaf ◽  
Leaf Disease

Performance Analysis of Deep Neural Networks for Tomato Leaf Disease Classification with Server-Based Computing

2021 ◽  
Author(s):  
Kartik E. Cholachgudda ◽  
Rajashekhar C. Biradar ◽  
Kouame Yann Olivier Akansie ◽  
R. Lohith ◽  
Aras Amruth.Raj Purushotham
Keyword(s):  
Neural Networks ◽  
Performance Analysis ◽  
Deep Neural Networks ◽  
Tomato Leaf ◽  
Disease Classification ◽  
Leaf Disease

scholarly journals Analysis of Tomato Leaf Disease Identification Techniques

2021 ◽  
Vol 2 (2) ◽  
pp. 98-103
Author(s):  
Gaurav Chopra ◽  
Pawan Whig
Keyword(s):  
Learning Algorithm ◽  
State Of The Art ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Disease Classification ◽  
Identification System ◽  
Great Success ◽  
Tomato Crop ◽  
Leaf Disease ◽  
Comparable Accuracy

India loses thousands of metric tons of tomato crop every year due to pests and diseases. Tomato leaf disease is a major issue that causes significant losses to farmers and possess a threat to the agriculture sector. Understanding how does an algorithm learn to classify different types of tomato leaf disease will help scientist and engineers built accurate models for tomato leaf disease detection. Convolutional neural networks with backpropagation algorithms have achieved great success in diagnosing various plant diseases. However, human benchmarks in diagnosing plant disease have still not been displayed by any computer vision method. Under different conditions, the accuracy of the plant identification system is much lower than expected by algorithms. This study performs analysis on features learned by the backpropagation algorithm and studies the state-of-the-art results achieved by image-based classification methods. The analysis is shown through gradient-based visualization methods. In our analysis, the most descriptive approach to generated attention maps is Grad-CAM. Moreover, it is also shown that using a different learning algorithm than backpropagation is also possible to achieve comparable accuracy to that of deep learning models. Hence, state-of-the-art results might show that Convolutional Neural Network achieves human comparable accuracy in tomato leaf disease classification through supervised learning. But, both genetic algorithms and semi-supervised models hold the potential to built precise systems for tomato leaf detection.

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