An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

Li, Yang; Hu, Jia; Qi, Jiangtao; Sun, Huibin; Tian, Xiaochen; Liu, Huili

doi:10.3390/s20072082

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…Then, the collected information is unified, selected and eliminated, and classified and fused, so as to achieve the effect of comprehensively and accurately judging the object. For the process of multisensor data fusion technology, see Figure 1 [12].…”

Section: Multidata Fusionmentioning

confidence: 99%

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

et al. 2023

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In order to control the grassland ecological environment, an application method of multisource data fusion technology in the construction of land ecological index is proposed. Due to the high requirements for grassland environmental monitoring, the use of traditional technologies to monitor grassland environmental conditions lacks certain effectiveness, has high investment costs, and consumes a lot of manpower and material resources. The use of sensors to dynamically monitor the grassland environment is conducive to monitoring the environment from a scientific and technological level. By understanding the fusion principle and process of three fusion methods, adaptive weighted average, BP neural network, and D-S evidence theory, the construction of Bashang grassland ecological energy big data platform based on multisource data fusion is proposed. A two-level data fusion model based on grassland environmental monitoring is proposed. Several environmental parameters in the experimental environment were monitored, and the validity of the two-level fusion model was verified by two evaluation indicators, the mean absolute percentage error and the corrosion error. This suggests that a combination of BP neural network and D-S proof theory improves system performance. It provides the possibility for more comprehensive monitoring of grassland ecological environment in the future.

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Section: Multidata Fusionmentioning

confidence: 99%

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

et al. 2023

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“…In the specific case of agriculture, data can be collected at three different scales—proximal, aerial, and orbital (satellites) ( Figure 1 ). Applications that use proximal data include navigation systems for autonomous vehicles [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ], fruit detection [ 18 , 19 , 20 , 21 ], plant disease detection [ 22 , 23 , 24 ], delineation of homogeneous management zones [ 25 , 26 , 27 , 28 , 29 ], soil analysis [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ], plant phenotyping [ 37 ], among others. Aerial data (collected using UAVs) is used mostly for detection of certain objects (e.g., certain plant species and fruits) [ 38 ] and for estimation of agricultural variables (e.g., soil moisture and nitrogen content) [ 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps

Barbedo

2022

Sensors

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Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research.

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“…e standard deviation refers to the arithmetic square root of the square of the deviation between the standard value and its average in all data [17]. e dispersion of the data set can be displayed through the standard deviation [18]. erefore, the standard deviation of the data in the window can represent the intensity of human leg activity at this stage.…”

Section: Leg Motion Feature Extraction In Human Motionmentioning

confidence: 99%

Fast Recognition Algorithm for Human Motion Posture Using Multimodal Bioinformation Fusion

Zhao

Jian-hui

2022

Mathematical Problems in Engineering

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To address the problems of low feature extraction accuracy, large bias of human motion pose recognition and posture recognition error, poor recognition effect, and low recognition rate of traditional human motion posture fast recognition algorithm, we propose a human motion posture fast recognition algorithm using multimodal bioinformation fusion. First, wavelet packet decomposition with sample entropy is used to extract the human motion posture hand features such as kurtosis, time domain feature skewness, and frequency domain feature electromyogram (EMG) integral value and time domain features such as mean, standard deviation, and interquartile distance of leg motion amplitude. Second, after normalizing the two features, the human hand and leg motion feature set is obtained, and finally the feature set is used to construct a human motion posture fast recognition model based on multimodal bioinformation fusion, and the feature set is input into the recognition model, which completes the fusion of human motion posture information by improving the typical correlation analysis method, and the fusion result is used as the input of the minimum distance classifier to achieve human motion posture fast recognition. The results show that the proposed algorithm has high accuracy of feature extraction, small bias of human motion posture recognition, the posture recognition error is -0.21∼0.02, the recognition rate is always above 95%, and the practical application effect is good.

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An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

Cited by 11 publications

References 23 publications

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps

Fast Recognition Algorithm for Human Motion Posture Using Multimodal Bioinformation Fusion

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