Abstract
Artificial Intelligence: AI based fertilizer control for improvement of rice quality and harvest amount is proposed together with intelligent drone based rice field monitoring system. Through experiments at the rice paddy fields which is situated at Saga Prefectural Research Institute of Agriculture: SPRIA in Saga city, Japan, it is found that the proposed system allows control rice crop quality and harvest amount by changing fertilizer type and supply amount. It, also, is found the most appropriate fertilizer supply management method which maximizing rice crop quality and harvest amount. Furthermore, these rice crop quality and harvest mount can be predicted in the early stage of rice leaf grow. Therefore, rice crop quality and harvest amount becomes controllable.
Highlights
In recent years, due to the diversification and lower price of sensing devices, the development of networks as infrastructure for aggregating and analyzing such information, generalization of mobile terminal devices, and higher functionalization of computers, ICT has been fully developed in the agricultural field The machine to utilize has matured
Rice paddy field monitoring with drone mounted visible and NIR: Near Infrared camera is proposed [10] while the method for rice quality evaluation through nitrogen content in rice leaves is proposed [11]
Rice crop quality is defined with protein content which is closely related to the nitrogen content in rice leaves
Summary
Due to the diversification and lower price of sensing devices, the development of networks as infrastructure for aggregating and analyzing such information, generalization of mobile terminal devices, and higher functionalization of computers, ICT has been fully developed in the agricultural field The machine to utilize has matured. Rice paddy field monitoring with drone mounted visible and NIR: Near Infrared camera is proposed [10] while the method for rice quality evaluation through nitrogen content in rice leaves is proposed [11]. The method proposed here is to utilize AI for estimation of fertilizer supply timing and amount of fertilizer together with evaluate rice quality through protein content in rice crop with observation of NDVI: Normalized Difference Vegetation Index which is acquired with visible and NIR camera mounted on drone. Method for NIR reflectance estimation with visible camera data based on regression for NDVI estimation and its application for insect damage detection of rice paddy fields is proposed and validated [16]. Www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 9, No 10, 2018 validated followed by conclusion with some discussions
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