Method of Tomato Leaf Disease Recognition Based on Image and Spectrum

2021 ◽  
pp. 854-862
Author(s):  
Ronghua Gao ◽  
Huarui Wu ◽  
Xiang Sun ◽  
Jingqiu Gu
Keyword(s):  
Tomato Leaf ◽  
Leaf Disease

scholarly journals Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 28822-28831
Author(s):  
Changjian Zhou ◽  
Sihan Zhou ◽  
Jinge Xing ◽  
Jia Song
Keyword(s):  
Tomato Leaf ◽  
Dense Network ◽  
Leaf Disease ◽  
Disease Identification

scholarly journals Tomato Leaf Disease Detection Using Hybrid CNN-RNN Model

2021 ◽  
Author(s):  
Hepzibah Elizabeth David ◽  
K. Ramalakshmi ◽  
R. Venkatesan ◽  
G. Hemalatha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Computational Effort ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Tomato Production ◽  
Classification Image ◽  
Leaf Disease ◽  
Learning Techniques

Tomato crops are infected with various diseases that impair tomato production. The recognition of the tomato leaf disease at an early stage protects the tomato crops from getting affected. In the present generation, the emerging deep learning techniques Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long-Short Term Memory (LSTMs) has manifested significant progress in image classification, image identification, and Sequence Predictions. Thus by using these computer vision-based deep learning techniques, we developed a new method for automatic leaf disease detection. This proposed model is a robust technique for tomato leaf disease identification that gives accurate and better results than other traditional methods. Early tomato leaf disease detection is made possible by using the hybrid CNN-RNN architecture which utilizes less computational effort. In this paper, the required methods for implementing the disease recognition model with results are briefly explained. This paper also mentions the scope of developing more reliable and effective means of classifying and detecting all plant species.

scholarly journals ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network

2020 ◽  
Vol 167 ◽  
pp. 293-301 ◽  
Author(s):  
Mohit Agarwal ◽  
Abhishek Singh ◽  
Siddhartha Arjaria ◽  
Amit Sinha ◽  
Suneet Gupta
Keyword(s):  
Neural Network ◽  
Tomato Leaf ◽  
Convolution Neural Network ◽  
Disease Detection ◽  
Leaf Disease

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

Tomato Leaf Disease Recognition Using Depthwise Separable Convolution

2022 ◽  
pp. 341-351
Author(s):  
Syed Md. Minhaz Hossain ◽  
Khaleque Md. Aashiq Kamal ◽  
Anik Sen ◽  
Kaushik Deb
Keyword(s):  
Tomato Leaf ◽  
Leaf Disease
Sign in / Sign up

Export Citation Format

Share Document