Design of Big Data Acquisition for Professional Grower based on Smart Agricultural Machinery Systems

Smart rice transplanter, smart 2nd fertilizer applicator and yield monitoring combine harvester (i.e. smart agricultural machinery systems (SAMS)) have been employed in order to monitor spatial temporal variability of topsoil depth, soil fertility and crop status for variable rate fertilizer application.
a year ago
Author: Morimoto E
The International Tri-Conference for Precision Agriculture in 2017
Publisher: Zenodo
The last 20 years have seen an increase in agricultural technology and information systems intended to improve yields, reduce costs, and environmental sustainability production. However, these technologies haven’t delivered to practical farmer yet. This research looks at the challenges of creating step changes for precision agriculture, how to make big data which obtains from agriculture machinery for young farmer.
This presentation focused on the importance of big data solution for inheriting farm management. Smart rice transplanter, smart 2nd fertilizer applicator and yield monitoring combine harvester (i.e. smart agricultural machinery systems (SAMS)) have been employed in order to monitor spatial temporal variability of topsoil depth, soil fertility and crop status for variable rate fertilizer application. As a result of field experiment indicated that 7,000,000 dataset of soil information, 65,000 dataset of crop status and 10,000,000 dataset of yield dataset were observed from the SAMAS. Variable fertilization design based on the experience of professional farmers also result in 20% fertilizer cut than conventional way and 30% harvest efficiency was improved. We concluded that the determination of variable setting values in field management with big data, the algorithm can be reflected the judgment of professional farmers. We should not apply only from the scientific aspect.

Photo: Outline of Smart Rice Transplanter (credit: Morimoto E)

Photo: Android application for variable rate fertilizer application (credit: Morimoto E)

This article is published under license Creative Commons Attribution 4.0
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