Abstract
Gas sensor is the technique of gas detection and it is the first attempt at the application of seed germination. However, for seed germination environment, concentrations of gases vary, increasing detection difficulty because of the slow response of gas sensor. In this study, the ability of gas sensors to detect gas applied in seed germination with varying concentration was researched. Ethylene and CO were employed and four gas sensors were applied. A new method was proposed. Back Propagation Neural Network (BPNN) was introduced and their performances were compared. The results showed that the proposed method had better overall performance, whose correct prediction rate for gas concentration fixed samples was 91.1% and for gas concentration varying samples was 83.3%. Moreover, the data size needed was small and the computation process was rapid. The results indicated that the proposed method was effective for gas application in seed germination with varying concentration.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
