The early detection of diseases is important in agriculture for an efficient crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes. Automatic methods for classification of plant diseases also help taking action after detecting the symptoms of leaf diseases. This paper presents a Convolutional Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm based method for tomato leaf disease detection and classification. The dataset contains 500 images of tomato leaves with four symptoms of diseases. We have modeled a CNN for automatic feature extraction and classification. Color information is actively used for plant leaf disease researches. In our model, the filters are applied to three channels based on RGB components. The LVQ has been fed with the output feature vector of convolution part for training the network. The experimental results validate that the proposed method effectively recognizes four different types of tomato leaf diseases.

Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm

Three convolutional neural network models for facial expression recognition in the wild

The latest improvements in computer vision formulated through deep learning have paved the method for how to detect and diagnose diseases in plants by using a camera to capture images as basis for recognizing several types of plant diseases. This study provides an efficient solution for detecting multiple diseases in several plant varieties. The system was designed to detect and recognize several plant varieties specifically apple, corn, grapes, potato, sugarcane, and tomato. The system can also detect several diseases of plants. Comprised of 35,000 images of healthy plant leaves and infected with the diseases, the researchers were able to train deep learning models to detect and recognize plant diseases and the absence these of diseases. The trained model has achieved an accuracy rate of 96.5% and the system was able to register up to 100% accuracy in detecting and recognizing the plant variety and the type of diseases the plant was infected.

Plant Leaf Detection and Disease Recognition using Deep Learning

In this paper, we proposed Multi-model LSTM-based Pre-trained Convolutional Neural Networks (MLP-CNNs) as an ensemble majority voting classifier for the detection of plant diseases and pests. The proposed hybrid model is based on the combination of LSTM network with pre-trained CNN models. Specifically, in transfer learning, we adopted deep feature extraction from various fully connected layers of these pre-trained deep models. AlexNet, GoogleNet and DenseNet201 models are used in this work for feature extraction. The extracted deep features are then fed into the LSTM layer in order to construct a robust hybrid model for apple disease and pest detection. Later, the output predictions of three LSTM layers determined the class labels of the input images by majority voting classifier. In addition, we use an automatic scheme for determining the best choice of the network parameters of the LSTM layer. The experiments are carried out using data consisting of real-time apple disease and pest images from Turkey and the accuracy rates are calculated for performance evaluation. The experimental results show that by using the proposed ensemble combination structure, the results are comparable to, or better than, the pre-trained deep architectures.

Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests

Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.

https://www.mdpi.com/1424-8220/20/3/612/pdf

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database.

Facial emotion recognition on a dataset using convolutional neural network

Road separations, intersections, and crosswalks, which are important components of highways, are seen as significant areas for autonomous vehicles and advanced driver assistance systems because traffic accident occurrence rate is considerably high in these areas. In this study, an image processing method and a deep learning based approach on real images has been proposed in order to provide instant information for drivers and autonomous vehicles, or to develop warning systems as part of advanced driver assistance systems to prevent or minimize traffic accidents. The information is obtained from the classification of images belonging to the separations, intersections and crosswalks on the road using a new model and VggNet, AlexNet, LeNet based on Convolutional Neural Network(CNN). We have obtained high classification accuracy with our model based on CNN. The result of the study performed on different datasets showed that the proposed method is usable for driver assistance systems and an effective structure that can be used in many areas such as warning both vehicles and drivers.

Intersections and crosswalk detection using deep learning and image processing techniques

Electricity theft is a big problem faced by all energy distribution services and continues to rising. Therefore, studies on electricity theft detection techniques have increased in recent years. Unsuitable calibration and illegal calibration of energy meters during production may cause non-technical losses. Non-technical losses have been a major concern for the resulting security risks and the immeasurable loss of income. In most of the meter tampered locations, damaged meter terminals and/or illegal applications cannot be distinguishable during checking. In fact, electric distribution companies will never be able to eliminate electricity theft. But it is possible to take measure to detect, prevent and reduce it. In this paper, we developed by using deep learning methods on real daily electricity consumption data (Electricity consumption dataset of State Grid Corporation of China). Data reduction has been made by developing a new method to make the dataset more usable and to extract meaningful results. A Long Short-Term Memory (LSTM) based deep learning method has been developed for the dataset to be able to recognize the actual daily electricity consumption data of 2016. In order to evaluate the performance of the proposed method, the accuracy, prediction and recall metric was used by considering the five cross-fold technique. Performance of the proposed methods were found to be better than previously reported results.

Detection of electricity theft using data processing and LSTM method in distribution systems

To develop effective advanced driving assistance systems, it is important to accurately recognize current driving environments and make critical decisions about driving processes. Preventing accidents through the interaction between the driving assistance systems and the environment and ensuring optimum driving dynamics are the main topics in this field. Vehicles need to recognize the road type and quality at a high accuracy to ensure the most suitable driving for the road type. It is also important to use both uncomplicated and cost-effective systems when performing this detection. In this study, a deep learning-based approach that can be used in vehicle driver assistance systems is proposed to automatically recognize road type and quality. Using this approach, it is possible to determine the road type and the quality of the road using only driving images as the input data. A new convolutional neural network model is designed for classification of the driving images. Driving images obtained from Google Street View are used to evaluate the recognition system for an actual driving environment. The proposed approach shows that the road types were determined with accuracy of 91.41 %, and the pothole road–smooth road distinction was successful at 91.07 %. It can be said that the proposed method is an effective structure that can be used for advanced driving support systems, V2I communications systems, and similar intelligent transportation systems.DOI: http://dx.doi.org/10.5755/j01.eie.24.6.22293

/pdf/recognition-of-road-type-and-quality-for-advanced-driver-wxw5c0miun.pdf

Recognition of Road Type and Quality for Advanced Driver Assistance Systems with Deep Learning

In this study, a deep learning method was used to detect sleep states of the drivers in the driving environment. A convolutional neural network (CNN) model has been proposed to determine whether the eyes of certain constant face images of drivers are closed. The proposed model has a wide potential application area such as human­computer interface design, facial expression recognition, driver fatigue-sleepiness determination. This method, which was developed on driver sleepiness data, has been applied on 4,846 real eye images in the Closed Eyes In The Wild (CEW) database. Commonly used CNN models are used on the same data to compare performances of the prepared model. According to the classification results obtained, 96.5% and 92.99% of the designed model achieved success and it is seen that this structure can be used in this problem area.

Vedat Tümen

Papers

Facial emotion recognition on a dataset using convolutional neural network

Intersections and crosswalk detection using deep learning and image processing techniques

Detection of electricity theft using data processing and LSTM method in distribution systems

Recognition of Road Type and Quality for Advanced Driver Assistance Systems with Deep Learning

Detection of driver drowsiness in driving environment using deep learning methods