A Web-based training method of agricultural skills for temporary workers

Year 2023, Volume: 3 Issue: 2, 93 - 105, 01.12.2023

Dai Kusui , Hideo Shimazu Atsushi Shinjo

Abstract

We propose a Rapid Agri-InfoScience (RAIS) learning model that has been adapted to the situation of teaching temporary workers. This learning model is a model by which workers can learn how to make the decisions necessary to execute tasks by narrowing down the number of tasks and further 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 the crop at one location at a particular time, instructors can create exercises that are suitable for the situation at that time. Because the system implemented the model creates exercises based on images, the exercises can easily be adapted to different languages. This feature will lead to the realization of computer-aided instruction (CAI) that eliminates the language barrier and makes it easier to accept foreign human resources. The system is characterized by not only enabling temporary workers to learn on the farm but also enabling instructors to create exercises on the farm when necessary. Using this system, experiments confirmed that instructors can create the exercises by which temporary workers can improve the quality of their work, as confirmed by instructors visually evaluating the quality of the work.

Keywords

Agricultural work skills, E-learning, Temporary worker, Training method, Web-based learning

Supporting Institution

Keio Research Institute at SFC (Shonan Fujisawa Campus), Keio University, Ministry of Agriculture, Forestry and Fisheries (MAFF) of Japan

Thanks

This research was conducted in collaboration with the Keio Research Institute at SFC (Shonan Fujisawa Campus), Keio University through the "AI System Demonstration Project (2012-2014)" and the "Agricultural IT Intellectual Property Utilization Demonstration Project (2015)" subsidized by the Ministry of Agriculture, Forestry and Fisheries (MAFF) of Japan. We would like to express our deep appreciation to the MAFF, the instructors and members of Japan Agriculture (JA) Fukuoka Yame, Kagawa Prefectural Agricultural Experiment Station Fuchu Fruit Research Institute, JA Kagawa and their members, and everyone else involved for their assistance.

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