scholarly journals LEAF DISEASE DETECTION USING IMAGE PROCESSING AND NEURAL NETWORK

2014 ◽  
Vol 1 (06) ◽  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Disease Detection ◽  
Leaf Disease

scholarly journals Smart Farming: IoT Based Plant Leaf Disease Detection and Prediction using Deep Neural Network with Image Processing

2019 ◽  
Vol 8 (9) ◽  
pp. 3081-3083 ◽  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Neural Network ◽  
Human Life ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Agricultural Laborers ◽  
Smart Farming ◽  
Texture Size

Agriculture plays a major role in human life. Almost 60% of the population is involved directly or indirectly in some agriculture activity. But Nowadays, farmers have quit agriculture and shifted to other sectors due to less adoption of automation and other reasons like increase in the requirement of agricultural laborers. So, Farmers now largely depend on adoption of cognitive solutions with technological advancements to acquire the benefits. Image processing and Internet of Things jointly produces new dimensions in the field of smart precision farming. This proposed methodology aims to create an approach for plant leaf disease detection based on deep neural network. This approach combines IoT and image processing which runs preprocessing and feature extraction techniques by considering different features such as color, texture, size and performs classification using deep learning model that expands to help identification of plant leaf disease

Wheat stem rust leaf disease detection using image processing

2021 ◽  
Author(s):  
Basim Khalid. Mohammed Ali Al-windi ◽  
Amel H. Abbas ◽  
Mohammed Shakir Mahmood
Keyword(s):  
Image Processing ◽  
Stem Rust ◽  
Disease Detection ◽  
Wheat Stem Rust ◽  
Leaf Disease

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 Tomato Leaf Disease Detection using Back Propagation Neural Network

2020 ◽  
Vol 9 (8) ◽  
pp. 529-538
Keyword(s):  
Neural Network ◽  
Traditional Approach ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Plant Diseases ◽  
Disease Detection ◽  
Large Drop ◽  
Tomato Crop ◽  
Leaf Disease ◽  
Area Of Interest

Most of the Indian economy rely on agriculture, so identifying any diseases crop in early stages is very crucial as these diseases in plants causes a large drop in the production and economy of the farmers and therefore, degradation of the crop which emphasize on the early detection of the plant disease. These days, detection of plant diseases has become a hot topic in the area of interest of the researchers. Farmers followed a traditional approach for identifying and detecting diseases in plants with naked eyes, which didn’t help much as the disease may have caused much damage to the plant. Tomato crop shares a huge portion of Indian cuisine and can be prone to various Air-Bourne and Soil-Bourne diseases. In this paper, we tried to automate the Tomato Plant Leaf disease detection by studying the various features of diseased and healthy leaves. The technique used is pattern recognition using Back-Propagation Neural network and comparing the results of this neural network on different features set. Several steps included are image acquisition, image pre-processing, features extraction, subset creation and BPNN classification.

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
Sign in / Sign up

Export Citation Format

Share Document