ORAL - International Conference of Agricultural Engineering

The information acquired bythe children from the fields, however, h strongly helped theexperts to provideproper suggestions to the farmers. For example, leaf color informationhelped the expertsto know the nitrogen condition ofthe rice plant and to give properr adviceson federalizer amount. The photographs taken by the children (Fig. 5) also helped theexperts to know the diseases and the pests in the fields to give proper advices. Withoutphotographs, it would be rather difficult for the experts to identify such diseases and pests.Aggregating such disease and pests information and mappingg them on GIS can create anearly warning system in near future. fFIGURE 5: Example of photographs taken by b children.We alsorealized that the dataa acquisitions by the children seemed to be sustainableand weare quite confidentthat the enriched database about the weather and cropp condition will leadto quantitative modeling of crops and pest predictions in the future (Laurenson et. al. 2002).We alsolearned that the family communication was accelerated and the children becamemore interested inagriculture obtainingg much knowledge about a agriculture through theproject, which canbe a trigger to reestablish the rural community. Thiss new approach ofdecisionsupport by digital-analogue fusion seems to be promising and we are planning tostart thesecond stage for much longer duration.ReferencesFukatsuu T., Hirafuji M. (2005) Field monitoring using sensor-nodes withh a web server, J.Robotics and Mechatronics, 17, 164-172langrid.org (2011) Language Grid; http://langrid.org/en/index.htmlLaurenson, M. R., Kiura T., Ninomiya S. . (2002) Providing agricultural models with mediatedaccess to heterogeneous weather databases, Applied Engineering Agric., 18, 617-625.Otuka A., NinomiyaS. (1998) Conceptual retrieval of agricultural cases with latent semanticindexingapproach, Agricultural Information Technology in Asia and Oceania 1998, 203-206.PANGAEA (2012) http://www. .pangaean.org