RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles

Gupta, Neeraj; Gupta, Saurabh; Khosravy, Mahdi; Dey, Nilanjan; Joshi, Nisheeth; Crespo, Rubén González; Patel, Nilesh

doi:10.1007/s10845-020-01610-0

Cited by 40 publications

(16 citation statements)

References 38 publications

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“…The basis of using image analysis technology to monitor agricultural conditions is the image itself. Commonly used image acquisition devices first obtain RGB (Red, Green, Blue) images, and a series of following operations are performed on RGB images, as shown in Fig 2 . An RGB image is an MxNx3 array of color pixels, where each pixel corresponds to the three components of red, green, and blue at a unique spatial location [20]. The first step in using the model to estimate agricultural parameters is to extract image features.…”

Section: Digital Image Processing Technologymentioning

confidence: 99%

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages

et al. 2021

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The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with different agricultural parameters is constructed through different densities and nitrogen fertilizer application rates based on digital image processing technology, and an estimation NC (Nitrogen Content) model is established. The population is classified through the K-Means Clustering algorithm using the feature extraction method, and the Chinese cabbage population quality BPNN (Backpropagation Neural Network) model is constructed. The nonlinear mapping relationship between different agricultural parameters and population quality, and the contribution rate of each indicator, are studied. The nitrogen utilization of Chinese cabbage is monitored effectively. Results demonstrate that the proposed NC estimation model has correlation coefficients above 0.70 in different growth stages. This model can accurately estimate the NC of the Chinese cabbage population. The results of the Chinese cabbage population quality BPNN model show that the population planting density based on the seedling number is reasonable. The constructed population quality evaluation model has a high R2 value and a comparatively low RMSE (Root Mean Square Error) value for the quality evaluation of Chinese cabbage in different periods, showing that it applies to evaluate the population quality of Chinese cabbage in different growth stages. The constructed nitrogen utilization model and quality evaluation model can monitor the nutrient utilization of crops in different growth stages, ascertain the agricultural characteristics of other yield groups in different growth stages, and clarify the performance of agricultural parameters in different growth stages. The above results can provide some ideas for crop growth intelligent detection.

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Section: Digital Image Processing Technologymentioning

confidence: 99%

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages

et al. 2021

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“…e difference between visual information and auditory information in terms of data processing is that visual information can be recorded by a computer through the intensity of the three primary colours Red-Green-Blue (RGB) of each pixel, and the intensity of the three primary colours can be rendered to a certain extent by a monitor to obtain the same, or even better than, the actual content observed by the naked eye. However, auditory information is very different, as the auditory information recorded by the computer is the intensity or the change in loudness of the sound at each moment is stored and can then be replayed through the speaker, while the perception of auditory information by the human brain is through the stimulation of the neurons of the auditory nerve for each moment of auditory information received at different frequencies, rather than the direct processing of the time-domain signal [16]. Analysed in terms of pattern recognition, the two most critical steps in music genre recognition, as in numerous classical classification recognition problems, are the extraction of audio features and the selection of classifiers.…”

Section: Status Of Researchmentioning

confidence: 99%

Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Kai¹

2021

Complexity

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Because of the difficulty of music feature recognition due to the complex and varied music theory knowledge influenced by music specialization, we designed a music feature recognition system based on Internet of Things (IoT) technology. The physical sensing layer of the system places sound sensors at different locations to collect the original music signals and uses a digital signal processor to carry out music signal analysis and processing. The network transmission layer transmits the completed music signals to the music signal database in the application layer of the system. The music feature analysis module of the application layer uses a dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference. The music feature analysis module of the application layer uses the dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference template to realize the feature recognition of the music signal and determine the music pattern and music emotion corresponding to the music feature content according to the recognition result. The experimental results show that the system operates stably, can capture high-quality music signals, and can correctly identify music style features and emotion features. The results of this study can meet the needs of composers’ assisted creation and music researchers’ analysis of a large amount of music data, and the results can be further transferred to deep music learning research, human-computer interaction music creation, application-based music creation, and other fields for expansion.

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“…A significant part of these R&D tasks needs proper test measurement data concerning the vehicle dynamics together with proper map data (i.e., the punctual environment information around the vehicle under test). Additionally, the monitoring of vehicles is of crucial importance in this technology, e.g., IoT (Internet of Things) sensors are increasingly used [ 7 , 8 ]. However, collecting such data is not straightforward and it takes a great deal of time and money.…”

Section: Introductionmentioning

confidence: 99%

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

Tihanyi

Tettamanti

Csonthó

et al. 2021

Sensors

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A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.

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RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles

Cited by 40 publications

References 38 publications

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages

Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies

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