iosr-JVSP   Volume-13 ~ Issue-5 ~ Sep. - Oct. 2023

ABSTRACT: Plant disease identification by visual way is increasingly difficult and simultaneously less accurate. However in the event that disease detection technique is used, it will take less time and processing power and proves to be progressively exact. Some broad maladies in plants appear dark coloured, yellow spots, and some are infectious, viral and bacterial diseases. Image processing is being used for estimation of infected area. Image segmentation is the process of collecting images into different parts. Now a day there are various strategies used for......

Keywords: Multi Leaf Disease Detection, Pre-processing, Classification algorithms, Feature Extraction, Convolutional Neural Network (CNN) etc

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[4]. Hanson, A.M.J.; Joy, A.; Francis, J. Plant leaf disease detection using deep learning and convolutional neural network. Int. J. Eng. Sci. Comput. 2017, 7, 5324–5328

ABSTRACT: Scientists and researchers have a tremendous responsibility to improve the quality and expectancy of life, particularly human life. The researcher has chosen the presented field for research to identify possible innovative and advanced definite system designs to save numerous endangered lives of Individuals with Anxiety Disorder. The paper displays compact board can then be incorporated into an easy-to-wear device, such as a ring, which focuses on the finger and collects the physiological signals of GSR, PPG, and 3-axis sensors. This way, the user is only wearing.....

Keywords: GSR, PPG, 3-axis accelerometer

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ABSTRACT: The need for high capacity long haul telecommunication system to carry huge traffic demands in recent times has lead to the use of optic fiber communication system because of its high capacity carrying advantage over wireless systems. But optic fiber signals suffer some signal impairment issues such as nonlinearity which tends to degrade its transmission performance. This paper proposed the use of adaptive optical equalizer to mitigate such impairments. To achieve that, a simulink model of the system was first developed for simulation experiments. Then the impact of out-of-bound nonlinear signal on the three key performance indicators (Q Factor, Bit Error.....

Keywords: Nonlinear optic fiber, self-phase modulation, Kerr effect, refractive index, nonlinearity mitigation

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ABSTRACT: The RAW (Restricted Access Window) component of the IoT network is deployed to reduce traffic and channel contention in dense and heterogeneous sensor network environment. It divides sensor nodes into groups and slots, allowing channel access only to one RAW slot at a time. Several algorithms and improved channel utilization optimization models have been proposed to optimize the RAW parameters, to ensure a contention free network or at least, minimally reduce it. These techniques often rely on previous traffic demands schedules, collision analysis and send....

Keywords: Resource allocation, station, network, nodes, simulation

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ABSTRACT: Agricultural productivity is an important factor in the Indian economy. Therefore, the contribution of food and cash crops is very important to both the environment and people. Each year, crops succumb to several diseases. The diagnosis of such diseases is inadequate, and many plants die due to ignorance of the symptoms of the disease and its treatment. This is done using image processing techniques. A total of 15 cases were fed to the model, 12 of which were Bell Paper Bacterial Spot, Potato Early Bright, Potato Rate Bright, Tomato Target Spot, Tomato Mosaic Virus, Tomato Yellow Leaf Curl Virus, and Tomato Bacteria. Three cases of Spot, Tomato Early Bright, Tomato Late Bright, Tomato Leaf Mold, Tomato Septoria Leaf Spot and Tomato Spider Mite and Healthy Leaves: Bell Paper Healthy, Potato Healthy and Tomato Healthy. The test accuracy is 94.80%. Different performance matrices are derived for the same thing....

Key Word: Convolutional Neural Network (CNN), Leaf Disease, etc

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[4]. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., and Stefanovic, D.: Deep NeuralNetworks Based Recognition of Plant Diseases by Leaf Image Classification. Comput.Intell.Neurosci. 2016(2016)
[5]. Fuentes, A., Yoon, S., Kim, S. C., and Park, D. S.: A robust deep-learning-based detectorforreal-timetomatoplantdiseasesandpestsrecognition.Sensors(Switzerland)17(2017).