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.