2023 Volume 31 Issue 4 Pages 95-110
Our first aim was to clarify the effectiveness of using a CO2 sensor with a farmer-built IoT (Internet of Things) system that had proven to be of practical use in rice nursery growing. We conducted an experiment at kikurage mushroom and strawberry growing sites to observe whether the system worked and asked the property owners to evaluate the system. At both sites the system provided CO2 concentration data with almost the same accuracy as the existing detection devices that the farmers had been using. Because our system uses an IoT study kit and Raspberry Pi, which are provided cheaply, the farmers evaluated it as practical and low-cost. We put one CO2 sensor in the strawberry greenhouse, with an annual cost of 25,620 yen; this was much lower than the cost of the existing devices that the farmer had used in the past. In the experiment in the kikurage mushroom house, we placed four CO2 sensors, with an annual cost of 72,480 yen; this was higher than the cost of the handy-type detection device that the farmer had been using. However, our system sends CO2 concentration data to cell phones automatically, and it detected high concentrations at times when the farmer was engaged elsewhere. The farmer therefore considered that the annual cost was not high.
The CO2 sensor has tendency to bring value shifts upwards; it takes place when micro dusts adhere the sensor tip. We are therefore unable to write a threshold CO2 concentration into the program to detect anomalous values, such as those occurring from changes in temperature with seasonal change. Our second aim was to clarify a method of using a state-space model to detect anomalous values with the farmer-built IoT system. We created a calculation method and used the variance in lag between the predicted and measured values as an evaluation criterion to judge the anomalous values. The method worked in a test using CO2 concentration data from the strawberry greenhouse.
