A Convolutional Neural Network Architecture for Tomato Leaf Disease Detection Using Data Augmentation

2021 ◽  
pp. 507-516
Author(s):  
Matta Bharati Devi ◽  
K. Amarendra
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Data Augmentation ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Neural Network Architecture ◽  
Leaf Disease ◽  
Using Data

scholarly journals Disease Detection in Tomato Plants Using Deep Learning

2020 ◽  
Author(s):  
Marimuthu S ◽  
Bhuvana J ◽  
Mirnalinee T T
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Soil Quality ◽  
Tomato Plant ◽  
Network Architecture ◽  
Disease Detection ◽  
Neural Network Architecture ◽  
Disease Category ◽  
Tomato Plants

Agriculture is the backbone of the economy of any country. Productivity of the crops depends on soil quality, proper irrigation and fertilizer, appropriate pesticide. Mostly pesticides were applied without having knowledge about the type of diseases or pests. Type and the quantity of pesticide for any depends on the disease category. If we could identify the appropriate disease, then applying appropriate pesticide will increase the yield of the crop. To address this issue, we propose a method to detect the diseases in tomato plants. We have designed a Convolutional Neural Network architecture that efficiently detects the disease of tomato plant. The system is evaluated with Plant Village benchmark dataset. Results show that our network is detecting the diseases with 90.86% accuracy. We have identified a suitable variant of Convolutional Neural Network that efficiently detects the disease of tomato plant.

scholarly journals BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition

2021 ◽  
Vol 11 (15) ◽  
pp. 6845
Author(s):  
Abu Sayeed ◽  
Jungpil Shin ◽  
Md. Al Mehedi Hasan ◽  
Azmain Yakin Srizon ◽  
Md. Mehedi Hasan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Character Recognition ◽  
High Performance ◽  
Low Cost ◽  
Traditional Learning ◽  
Neural Network Architecture ◽  
Handwritten Character Recognition ◽  
Handwritten Character

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.

Sign in / Sign up

Export Citation Format

Share Document