Application Of Multisensor Data Fusion Research Articles

Wheat Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. Despite significant advancements in crop yield forecasting, existing methods often struggle with integrating diverse sensor data and achieving high prediction accuracy under varying environmental conditions. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms, namely random forest (RF), partial least squares (PLS), ridge regression (RR), k-nearest neighbor (KNN) and extreme gradient boosting decision tree (XGboost), were utilized for multi-sensor data fusion, together with three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average), for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB + MS + Texture + TIR combination demonstrating the highest fusion performance (R2 = 0.660, RMSE = 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the accuracy of wheat yield prediction. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE = 0.668 t ha−1). It significantly outperformed both the second-layer ensemble methods of stacking (R2 = 0.668, RMSE = 0.673 t ha−1) and feature-weighted (R2 = 0.667, RMSE = 0.674 t ha−1), thereby exhibiting superior predictive capabilities. This finding highlighted the third-layer ensemble method’s ability to enhance predictive capabilities and refined the accuracy of wheat yield prediction through simple average ensemble learning, offering a novel perspective for crop yield prediction and breeding selection.

Multisensor Data Fusion in IoT Environments in Dempster-Shafer Theory Setting: An Improved Evidence Distance-Based Approach.

In IoT environments, voluminous amounts of data are produced every single second. Due to multiple factors, these data are prone to various imperfections, they could be uncertain, conflicting, or even incorrect leading to wrong decisions. Multisensor data fusion has proved to be powerful for managing data coming from heterogeneous sources and moving towards effective decision-making. Dempster-Shafer (D-S) theory is a robust and flexible mathematical tool for modeling and merging uncertain, imprecise, and incomplete data, and is widely used in multisensor data fusion applications such as decision-making, fault diagnosis, pattern recognition, etc. However, the combination of contradictory data has always been challenging in D-S theory, unreasonable results may arise when dealing with highly conflicting sources. In this paper, an improved evidence combination approach is proposed to represent and manage both conflict and uncertainty in IoT environments in order to improve decision-making accuracy. It mainly relies on an improved evidence distance based on Hellinger distance and Deng entropy. To demonstrate the effectiveness of the proposed method, a benchmark example for target recognition and two real application cases in fault diagnosis and IoT decision-making have been provided. Fusion results were compared with several similar methods, and simulation analyses have shown the superiority of the proposed method in terms of conflict management, convergence speed, fusion results reliability, and decision accuracy. In fact, our approach achieved remarkable accuracy rates of 99.32% in target recognition example, 96.14% in fault diagnosis problem, and 99.54% in IoT decision-making application.

Application of Multi-sensor Data Fusion in Greenhouse

With the development of communication, computer and sensor technology, the application of Internet of things technology to agricultural monitoring is the trend of modern agricultural development. Real time and accurate acquisition of farmland environmental information is the basis of precision operation and intelligent management of agriculture, and it is also an important part of agricultural information construction. A farmland environment information monitoring system based on wireless sensor network is designed, crop growth environment parameters are collected by sensor nodes distributed in the field, using CC2530 to build ZigBee data transmission network, the information transmission between ZigBee network, GPRS network is realized by embedded gateway, and the remote monitoring of farmland environmental information is realized. Before data is transmitted, The negligent errors in the measurement data are excluded by Grubbs’ criterion, then the rest of the data are preprocessed based on the arithmetic mean and the batch estimates, lastly the data are fused using adaptive weighted fusion algo-rithm in the condition of minimal mean square error. The results show that the data by hybrid algorithm has perfect accuracy and minimal error. Using this hybrid data processing method, a large number of data can be fused into a data closest to the real situation, and more accurate environmental information can be obtained. The practical results show that, this solution enhances accuracy and reliability of the greenhouse environment detection. This system improves the information level of greenhouse planting, and applys to the management of greenhouse.

An Integrated Model for Robust Multisensor Data Fusion

This paper presents an integrated model aimed at obtaining robust and reliable results in decision level multisensor data fusion applications. The proposed model is based on the connection of Dempster-Shafer evidence theory and an extreme learning machine. It includes three main improvement aspects: a mass constructing algorithm to build reasonable basic belief assignments (BBAs); an evidence synthesis method to get a comprehensive BBA for an information source from several mass functions or experts; and a new way to make high-precision decisions based on an extreme learning machine (ELM). Compared to some universal classification methods, the proposed one can be directly applied in multisensor data fusion applications, but not only for conventional classifications. Experimental results demonstrate that the proposed model is able to yield robust and reliable results in multisensor data fusion problems. In addition, this paper also draws some meaningful conclusions, which have significant implications for future studies.

Research on Technology and Application of Multi-Sensor Data Fusion for Indoor Service Robots

The autonomous navigation process of a mobile service robot is usually in uncertain environment. The information only given by sensors has been unable to meet the demand of the modern mobile robots, so multi-sensor data fusion has been widely used in the field of robots. The platform of this project is the achievement of the important 863 Program national research project-a prototype nursing robot. The aim is to study a mobile service robot’s multi-sensor information fusion, path planning and movement control method. It can provide a basis and practical use’s reference for the study of an indoor robot’s localization.

Error and Noise Analysis in an IMU using Kalman Filter

Kalman filtering is a well-established methodology used in various multi-sensor data fusion applications. In our experiment, we first obtain measurements from the accelerometer and gyroscope and fuse them using Kalman filter in an inertial measurement unit (IMU). We estimate Kalman filter output and estimation error. The affect of process noise and measurement noise on estimation error is tested. It is explored that the measurement noise has significant role to increase estimation error in the data fusion process.

A Method for Multiple Periodic Factor Prediction Problems Using Complex Fuzzy Sets

Multiple periodic factor prediction (MPFP) problems exist widely in multisensor data fusion applications. Development of an effective prediction method should integrate information for multiple periodically changing factors. Because the uncertainty and periodicity coexist in the information used, the prediction method should be able to handle them simultaneously. In this study, complex fuzzy sets are used to represent the information with uncertainty and periodicity. A product-sum aggregation operator (PSAO) is developed for a set of complex fuzzy sets, which is used to integrate information with uncertainty and periodicity, and a PSAO-based prediction (PSAOP) method is then proposed to generate a solution of MPFP problems. This study illustrates the details of the PSAOP method through two real applications in annual sunspot number prediction and bushfire danger rating prediction. Experiments indicate that the proposed PSAOP method effectively handles the uncertainty and periodicity in the information of multiple periodic factors simultaneously and can generate accurate predictions for MPFP problems.

An Application of Multi-Sensor Data Fusion in SLS Rapid Prototype Manufacturing

There are some inevitable errors we can’t avoid in the process of powder laser sintering Rapid Prototype Manufacturing Cooling System. If we don’t take some measutes to eliminate the errors , they would influence the system’s operation directly, but if we adopt the traditional method, testing precision can’t meet the increasingly high requirement, while upgrading the hardware system will greatly enhance system cost, which can’t suit the empoldering and using of most systems. In this articles, when the number of sensors is less than 10 (N<10), a new method of data processing was introduced(including cooling water’s temperature, work jar temperature etc. ); use the distributing chart to eliminate inevitable errors, then adopt the method of arithmetic equilibrating and batch estimating to fuse the testing data, in order to estimate the true number.