2023 Volume 32 Issue 1 Pages 26-37
Agriculture has off-seasons and busy seasons, and not all farmers need the same number of workers throughout the year. It is costly to maintain the same level of employment throughout the year, so farmers usually bring in temporary workers to help during the busy season. As temporary workers, however, are not engaged in farming all year round, and it is not always possible for farms to hire the same people every year, it is uncertain whether the person hired this year has the knowledge and experience to do the work in question. In addition, the weather conditions and the field and crop conditions differ by year, and work needs to be adapted to current conditions. To ensure that work be done smoothly and consistently within a limited amount of time, it is desirable to provide guidance to the temporary workers on a case-by-case basis. We propose a rapid agri-infoscience learning model that has been adapted to teaching temporary agricultural workers. Through this model, workers learn how to make the decisions necessary to execute tasks by narrowing down the number of tasks and limiting the target area to a single crop at a particular time and place. By restricting the number of factors needed to judge and work a crop at one location at a time, instructors can create exercises that are suitable for the specific situation. By using the learning support system in which the model was implemented, experiments confirmed that the instructors can create the needed exercises and temporary workers can improve the quality of their work, as evaluated by instructors.
