Miller Magazine Issue: 121 January 2020

69 ARTICLE MILLER / JANUARY 2020 • a grain level monitoring system that measures the grain mass and tracks grain movements automatically. • a grain status monitoring system able to sense and detect accurately temperature, relative humidity, CO 2 and O 2 gases and provide early spoilage detection, grain quality forecast, and propose the best aeration strate- gies. • a fumigation system that proposes the right dosage, monitor the fumigant concentration automatically and predicts if the treatment would be successful and when. In the following sections, a description of these readily available technologies is presented. Sensor Technology Until recently, the typical method of monitoring a mass of stored grain utilized thermocouples attached to high- strength steel cables. These cables are subject to verti- cal frictional loading during filling, storing, and emptying operations. Since these cables are supported by the bin roof, the loads imposed on them have caused failures as reported by an Iowa State University study [1]. Besides the mechanical stability risks, cable monitoring systems require advanced expertise for their installation and have high installation costs. Copper cables often get corroded by phosphine and stop working. Recent advancements in grain storage technology enabled the development of wireless sensors (Figure 1), that measure accurately all the parameters that affect grain quality (e.g. temperature, relative humidity, O 2 , and CO 2 ). They also transmit safely their data to the cloud in real-time, offering worldwide accessibility. Their advan- tages also include the ability to move with the grain, in case of grain transit, offering enhanced traceability. Carbon Dioxide (CO 2 ) monitoring Monitoring gases in a silo offer significant advantages to early spoilage detection, since gases, like CO 2 , are associated with grain respiration, mold, and insect de- velopment. Furthermore, sensors can detect variations in gas concentrations 100 times faster than variations in temperature and relative humidity. Nonetheless, the evaluation of the CO 2 readings is not straightforward, and the assist of Machine Learning al- gorithms is needed to extract useful information. Figure 2 presents the CO 2 concentrations of two silo bins as re- corded by Centaur Analytics, Inc. sensors. Even though Bin B has higher CO 2 values than Bin A (particularly for the first 16 days), the algorithm doesn't issue an alert since the grain condition is good. On the contrary, the silo manager of Bin A would receive an alert as early as the 17th day since a sudden rise of the CO 2 is detected. Grain level monitoring A fill level sensor is a contactless radar system able to measure distance accurately (+- 5 mm). Specifically, it measures the round-trip time of flight of microwaves to the grain surface. It can be easily installed on the top of a silo and it provides continuous monitoring. To translate the measured distance to the total grain mass stored in the silo one should consider the following variables: • Grain bulk density (varies with moisture content) Figure1: Example of wireless sensor installation inside a silo feeding the predictive algorithm with real-time data Figure 2: CO 2 concentrations of two silo bins as recorded by Centaur Analytics, Inc. sensors. The silo manager of Bin A would receive an alert as early as from the 17th day since a sudden rise of the CO 2 is detected.