A Hybrid Model for Safety and Security Assessment of Autonomous Vehicles

Hybrid Model for Trajectory Planning of Agile Autonomous Vehicles

Journal of Aerospace Computing Information and Communication ◽

10.2514/1.12931 ◽

2004 ◽

Vol 1 (12) ◽

pp. 629-651 ◽

Cited By ~ 29

Author(s):

Tom Schouwenaars ◽

Bernard Mettler ◽

Eric Feron ◽

Jonathan How

Keyword(s):

Hybrid Model ◽

Trajectory Planning ◽

Autonomous Vehicles

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Drone-assisted automated plant diseases identification using spiking deep conventional neural learning

AI Communications ◽

10.3233/aic-210009 ◽

2021 ◽

pp. 1-16

Author(s):

Kubilay Demir ◽

Vedat Tümen

Keyword(s):

Neural Network ◽

Hybrid Model ◽

Autonomous Vehicles ◽

Smallholder Farmers ◽

Early Stage ◽

Plant Diseases ◽

Detection Model ◽

Neural Learning ◽

Hybrid Detection ◽

Detection And Diagnosis

Detection and diagnosis of the plant diseases in the early stage significantly minimize yield losses. Image-based automated plant diseases identification (APDI) tools have started to been widely used in pest managements strategies. The current APDI systems rely on images captured in laboratory conditions, which hardens the usage of the APDI systems by smallholder farmers. In this study, we investigate whether the smallholder farmers can exploit APDI systems using their basic and cheap unmanned autonomous vehicles (UAVs) with standard cameras. To create the tomato images like the one taken by UAVs, we build a new dataset from a public dataset by using image processing tools. The dataset includes tomato leaf photographs separated into 10 classes (diseases or healthy). To detect the diseases, we develop a new hybrid detection model, called SpikingTomaNet, which merges a novel deep convolutional neural network model with spiking neural network (SNN) model. This hybrid model provides both better accuracy rates for the plant diseases identification and more energy efficiency for the battery-constrained UAVs due to the SNN’s event-driven architecture. In this hybrid model, the features extracted from the CNN model are used as the input layer for SNNs. To assess our approach’s performance, firstly, we compare the proposed CNN model inside the developed hybrid model with well-known AlexNet, VggNet-5 and LeNet models. Secondly, we compare the developed hybrid model with three hybrid models composed of combinations of the well-known models and SNN model. To train and test the proposed neural network, 32022 images in the dataset are exploited. The results show that the SNN method significantly increases the success, especially in the augmented dataset. The experiment result shows that while the proposed hybrid model provides 97.78% accuracy on original images, its success on the created datasets is between 59.97%–82.98%. In addition, the results shows that the proposed hybrid model provides better overall accuracy in the classification of the diseases in comparison to the well-known models and LeNet and their combination with SNN.

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