Title :
Recurrent Neural Networks for Moisture Content Prediction in Seed Corn Dryer Buildings
Author :
Elliott, Daniel L. ; Valentine, Russell E.
Author_Institution :
Dept. of Comput. Sci., Colorado State Univ., Fort Collins, CO, USA
Abstract :
Conditioning seed corn is a short, yet crucial, portion of the seed production process. Seed corn must be conditioned prior to removing the seed from the cob to prevent damage, requiring constant monitoring by farmers. This paper evaluates the use of an echo state network for the prediction of seed moisture content and compares it against an Elman network. The results are determined to be good enough for inclusion into a commercially available dryer monitoring system.
Keywords :
agricultural products; production engineering computing; recurrent neural nets; Elman network; moisture content prediction; recurrent neural networks; seed corn dryer buildings; seed production process; Buildings; Humidity; Moisture; Recurrent neural networks; Sensors; Temperature measurement; Training; echo state network agriculture Elman network;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4577-2068-0
Electronic_ISBN :
1082-3409
DOI :
10.1109/ICTAI.2011.161
