Development of a Knowledge-Based Crop Recommendation Model for Precision Agriculture
2013
Chung, N.S., Kongju National University, Yesan, Republic of Korea | Kim, C.H., Kongju National University, Yesan, Republic of Korea | Oh, T.S., Kongju National University, Yesan, Republic of Korea | Hong, C.K., Kongju National University, Yesan, Republic of Korea | Jang, W.S., Kongju National University, Yesan, Republic of Korea
In this research, a Knowledge-based Crop Recommendation Model(KCRM) was developed considering essential components of precision agriculture such as soil, climate, and locational information in Geographical Information System(GIS). KCRM adapt top score system using numerical value of selected factors affecting to crop growing and harvest. In case of soil, we analyzed data in Heuktoram developed and serviced by Rural Development Administration(RDA) and extracted saturn, erosion classes, drainage class, slope, and gravel content which are served more than 80% as factors for selecting suitable crop. In case of climate, temperature, precipitation, and the duration hour of sunshine are used which are essential to crop growth based on 5 year cumulative data of Korea Meteorological Administration(KMA). Locational data was acquired using digital map of Korea Land Information System( KLIS). Ten target crop were selected as corn, rape, reed, watermelon, red pepper, tomato, onion, potato, sweet potato, and chinese matrimony vine which are suggested by RDA in upland field. Four target region were selected as Jaecheon, Muan, Wonju, and Haman considered as regions where crop selection demand will be increased by four-river refurbishment project. Simulation results with no restriction show that red pepper was the higest score as 75.5 and sweet potato was the lowest score as 61.2 in average. In second simulation, We supposed the situation of planned cultivation in each crop can not planted exceeding 70,000 parcels. Simulation results sampled in Haman with size restriction show that sweet potato was the top crop acquire highest score in sampled field.
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