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Volume: 33 | Article ID: art00020
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Adaptive Learning-based Method for Nitrate Sensor Quality Assessment in On-line Scenarios
  DOI :  10.2352/ISSN.2470-1173.2021.16.COLOR-340  Published OnlineJanuary 2021
Abstract

Nitrate sensors are commonly used to reflect the nitrate levels of soil conditions in agriculture. In a roll-to-roll system, for manufacturing Thin-Film nitrate sensors, varying characteristics of the ion-selective membrane on screen-printed electrodes are inevitable and affect sensor performance. It is essential to monitor the sensor performance in real-time to guarantee the quality of the products. We applied image processing techniques and offline learning to realize the performance assessment. However, a large variation of the sensor’s data with dynamic manufacturing factors will defeat the accuracy of the prediction system. In this work, our key contribution is to propose a system for predicting the sensor performance in on-line scenarios and making the neural networks efficiently adapt to the new data. We leverage residual learning and Hedge Back-Propagation to the on-line settings and make the predicting network more adaptive for input data coming sequentially. Our results show that our method achieves a highly accurate prediction performance with compact time consumption.

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  Cite this article 

Qingyu Yang, Kerry Maize, Ye Mi, George Chiu, Ali Shakouri, Jan Allebach, "Adaptive Learning-based Method for Nitrate Sensor Quality Assessment in On-line Scenariosin Proc. IS&T Int’l. Symp. on Electronic Imaging: Color Imaging XXVI: Displaying, Processing, Hardcopy, and Applications,  2021,  pp 340-1 - 340-7,  https://doi.org/10.2352/ISSN.2470-1173.2021.16.COLOR-340

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