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.

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

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 Multi-Level Deep Learning Model for Potato Leaf Disease Recognition

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2064
Author(s):  
Javed Rashid ◽  
Imran Khan ◽  
Ghulam Ali ◽  
Sultan H. Almotiri ◽  
Mohammed A. AlGhamdi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Disease Detection ◽  
Potato Leaf ◽  
Crop Species ◽  
Leaf Disease ◽  
Crop Diseases ◽  
Learning Technique ◽  
Multi Level ◽  
Deep Learning Model

Potato leaf disease detection in an early stage is challenging because of variations in crop species, crop diseases symptoms and environmental factors. These factors make it difficult to detect potato leaf diseases in the early stage. Various machine learning techniques have been developed to detect potato leaf diseases. However, the existing methods cannot detect crop species and crop diseases in general because these models are trained and tested on images of plant leaves of a specific region. In this research, a multi-level deep learning model for potato leaf disease recognition has developed. At the first level, it extracts the potato leaves from the potato plant image using the YOLOv5 image segmentation technique. At the second level, a novel deep learning technique has been developed using a convolutional neural network to detect the early blight and late blight potato diseases from potato leaf images. The proposed potato leaf disease detection model was trained and tested on a potato leaf disease dataset. The potato leaf disease dataset contains 4062 images collected from the Central Punjab region of Pakistan. The proposed deep learning technique achieved 99.75% accuracy on the potato leaf disease dataset. The performance of the proposed techniques was also evaluated on the PlantVillage dataset. The proposed technique is also compared with the state-of-the-art models and achieved significantly concerning the accuracy and computational cost.

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.

scholarly journals A Survey on Deep Learning Architectures and Frameworks for Cancer Detection in Medical Images Analysis

2020 ◽  
Vol 9 (11) ◽  
pp. 28-34
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Medical Images ◽  
Early Stage ◽  
Convolutional Network ◽  
Energetic Approach ◽  
Learning Techniques ◽  
Colon And Rectum ◽  
Learning Software ◽  
High Level

The various hurdles in machine learning are beaten by deep learning techniques and then the deep learning has gradually become preeminent in artificial intelligence. Deep learning uses neural networks to kindle decisions like humans. Deep learning flourished as an energetic approach and clarity marked its success in various domains. The study includes some dominant deep learning algorithms such as convolution neural network, fully convolutional network, autoencoder, and deep belief network to analyze the medical image and to detect and diagnose of cancer at an early stage. As early as the detection of cancer than to treat the disease is uncomplicated. Early diagnosis was particularly relevant for some cancers such as breast, skin, colon, and rectum, which prohibit the chance to grow and spread. Deep learning contributes to enhanced performance and better prediction in detection of cancer with medical images. The paper presents the study of a few deep learning software frameworks such as tensor flow, theano, caffe, torch, and keras. Tensor Flow provides excellent functionality for deep learning. Keras is a high-level neural network API that operates above on tensor flow or theano. The survey winds up by presenting several future avenues and open challenges that should be addressed by the researcher in the future.

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