The agricultural sector plays a key role in supplying quality food and makes the greatest contribution to growing economies and populations. Plant disease may cause significant losses in food production and eradicate diversity in species. Early diagnosis of plant diseases using accurate or automatic detection techniques can enhance the quality of food production and minimize economic losses. In recent years, deep learning has brought tremendous improvements in the recognition accuracy of image classification and object detection systems. Hence, in this paper, we utilized convolutional neural network (CNN)-based pre-trained models for efficient plant disease identification. We focused on fine tuning the hyperparameters of popular pre-trained models, such as DenseNet-121, ResNet-50, VGG-16, and Inception V4. The experiments were carried out using the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 classes. The performance of the model was evaluated through classification accuracy, sensitivity, specificity, and F1 score. A comparative analysis was also performed with similar state-of-the-art studies. The experiments proved that DenseNet-121 achieved 99.81% higher classification accuracy, which was superior to state-of-the-art models.
The application of electroencephalogram (EEG)-based emotion recognition (ER) to the brain–computer interface (BCI) has become increasingly popular over the past decade. Emotion recognition systems involve pre-processing and feature extraction, followed by classification. Deep learning has recently been used to classify emotions in BCI systems, and the results have been improved when compared to classic classification approaches. The main objective of this study is to classify the emotions from electroencephalogram signals using variant recurrent neural network architectures. Three architectures are used in this work for the recognition of emotions using EEG signals: RNN (recurrent neural network), LSTM (long short-term memory network), and GRU (gated recurrent unit). The efficiency of these networks, in terms of performance measures was confirmed by experimental data. The experiment was conducted by using the EEG Brain Wave Dataset: Feeling Emotions, and achieved an average accuracy of 95% for RNN, 97% for LSTM, and 96% for GRU for emotion detection problems.
In this paper a new background subtraction algorithm was developed to detect moving objects from a stable system in which visual surveillance plays a major role. Among all existing algorithms it was choosen because of low computational complexity which is the major parameter of time in VLSI. The concept of the background subtraction is to subtract the current image with respect to the reference image and compare it with to the certain threshold values. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. The test results are seen to be satisfactory. The area taken and the speed of the algorithm are also evaluated.
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