ArticleSensors (Basel, Switzerland)2021
Sensor Fusion with NARX Neural Network to Predict the Mass Flow in a Sugarcane Harvester.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks.Sensors (Basel, Switzerland) · 2022Article
- Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices.Frontiers in public health · 2022Article
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Authors and funding
3 authors.
Funding
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Abstract
Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor. In this sense, the objective was to evaluate if the fusion of different sensors installed in a sugarcane harvester improves the mass flow prediction accuracy. A harvester was experimentally instrumented, and neural network models integrated sensor data along the harvester to perform the self-calibration of these sensors and estimate the mass flow. Nonlinear autoregressive networks with exogenous input (NARX) and multiple linear regression (MLR) models were compared to predict the mass flow. The prediction with the NARX showed a significant superiority over MLR. MLR decreases the estimated mass flow variability in the harvester. NARX with multi-sensor data has an RMSE of 0.3 kg s
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