Deep Learning based Betelvine leaf Disease Detection (Piper BetleL.)

2020 ◽  
Author(s):  
R Kavitha lakshmi ◽  
S Nickolas
Keyword(s):  
Deep Learning ◽  
Disease Detection ◽  
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.

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

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 A Chaotic Salp Swarm Feature Selection Algorithm for Apple and Tomato Plant Leaf Disease Detection

2021 ◽  
Vol 10 (5) ◽  
pp. 3037-3045
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Detection System ◽  
Plant Diseases ◽  
Healthy Plant ◽  
Disease Detection ◽  
Test Accuracy ◽  
Plant Leaves ◽  
Leaf Disease ◽  
Deep Learning Network

Plants are seen as vital because they provide mankind with energy. Plant diseases can harm the leaf at any time between planting and harvesting, resulting in enormous losses in crop output and market value. A leaf disease detection system acts asignificant role in agricultural production. A large amount of labour is required for this process as well as an in-depth understanding of plant diseases. Determining the presence of illnesses in plant leaves requires the use of deep learning and machine learning methods, which classify the data based on a specified set. In this paper, apple and tomato leaves disease detection process is carried out by Chaotic Salp Swarm algorithm (CSSA) followed by Bi-directional Long Short Term Memory (Bi-LSTM) technique for classification. We've used the Bi-LSTM architecture to sense disease in tomato and apple leaves in studies. In order to determine the type of leaves, we trained a deep learning network using the PlantVillage dataset of damaged and healthy plant leaves. It is estimated that the trained model achieves a test accuracy of 96%.

Leaf Disease Detection Using Deep Learning

2021 ◽  
Author(s):  
Teenu Sahasra M ◽  
Sai Kumari S ◽  
Sai Meghana S ◽  
P. Rama Devi
Keyword(s):  
Deep Learning ◽  
Disease Detection ◽  
Leaf Disease

Performance of deep learning vs machine learning in plant leaf disease detection

2021 ◽  
Vol 80 ◽  
pp. 103615
Author(s):  
R. Sujatha ◽  
Jyotir Moy Chatterjee ◽  
NZ Jhanjhi ◽  
Sarfraz Nawaz Brohi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease

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 Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review

2022 ◽  
Vol 12 (2) ◽  
pp. 2079
Author(s):  
Pallepati Vasavi ◽  
Arumugam Punitha ◽  
T. Venkat Narayana Rao
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Detection System ◽  
Disease Detection ◽  
Disease Prediction ◽  
Research Papers ◽  
Comprehensive Overview ◽  
Leaf Disease ◽  
Agricultural Yield ◽  
Number Of Classes

<span lang="EN-US">A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.</span>

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