scholarly journals State-of-Art Deep Learning Based Tomato Leaf Disease Detection

2021 ◽  
Author(s):  
Asha Gowda Karegowda ◽  
Raksha Jain ◽  
G Devika
Keyword(s):  
Deep Learning ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Leaf Disease ◽  
State Of Art

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 Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 140
Author(s):  
Hesham Tarek ◽  
Hesham Aly ◽  
Saleh Eisa ◽  
Mohamed Abul-Soud
Keyword(s):  
Deep Learning ◽  
Climate Change Impacts ◽  
Tomato Leaf ◽  
Extreme Weather Events ◽  
High Yield ◽  
Raspberry Pi ◽  
Disease Detection ◽  
Vegetable Crops ◽  
Learning Models ◽  
Leaf Disease

Smart agriculture has taken more attention during the last decade due to the bio-hazards of climate change impacts, extreme weather events, population explosion, food security demands and natural resources shortage. The Egyptian government has taken initiative in dealing with plants diseases especially tomato which is one of the most important vegetable crops worldwide that are affected by many diseases causing high yield loss. Deep learning techniques have become the main focus in the direction of identifying tomato leaf diseases. This study evaluated different deep learning models pre-trained on ImageNet dataset such as ResNet50, InceptionV3, AlexNet, MobileNetV1, MobileNetV2 and MobileNetV3.To the best of our knowledge MobileNetV3 has not been tested on tomato leaf diseases. Each of the former deep learning models has been evaluated and optimized with different techniques. The evaluation shows that MobileNetV3 Small has achieved an accuracy of 98.99% while MobileNetV3 Large has achieved an accuracy of 99.81%. All models have been deployed on a workstation to evaluate their performance by calculating the prediction time on tomato leaf images. The models were also deployed on a Raspberry Pi 4 in order to build an Internet of Things (IoT) device capable of tomato leaf disease detection. MobileNetV3 Small had a latency of 66 ms and 251 ms on the workstation and the Raspberry Pi 4, respectively. On the other hand, MobileNetV3 Large had a latency of 50 ms on the workstation and 348 ms on the Raspberry Pi 4.

Tomato Leaf Disease Detection Using Deep Learning Techniques

2020 ◽  
Author(s):  
Surampalli Ashok ◽  
Gemini Kishore ◽  
Velpula Rajesh ◽  
S. Suchitra ◽  
S.G. Gino Sophia ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Leaf Disease ◽  
Learning Techniques

Maize Leaf Disease Detection and Classification Using Deep Learning

2021 ◽  
pp. 87-102
Author(s):  
Phani Kumar Singamsetty ◽  
G. V. N. D. Sai Prasad ◽  
N. V. Swamy Naidu ◽  
R. Suresh Kumar
Keyword(s):  
Deep Learning ◽  
Disease Detection ◽  
Maize Leaf ◽  
Leaf Disease
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