The Minimum Agriculture-Chunk as an Elementary Data Science Component in ADAM, a Micro Targeted, Trainable, Modular, Multipurpose System for Land Farming

Panagiotis Serdaris, Konstantinos Spinthiropoulos, Michael Agrafiotis and Athanasios Zisopoulos

Journal of Agricultural Studies, 2018, vol. 6, issue 4, 84-97

Abstract: The poor Data Science support of agriculture brought us to our main idea of the research is to analyze all micro-works for every plant or tree. Then we proceed to specify targeted actions for harvest collection, micro spraying and hundreds similar simple actions. Initially we collect data from the farm. The airborne, land and underwater unmanned vehicles scan the field area with customized various sensors and cameras in various multi spectral modes. The result is minimum agro-chunk Four-Dimensional model. The unmanned vehicle on the field area receives target data. It is equipped with a general-purpose robotic arm, an absorbing bellow, a robotic pruner, a liquid spraying pipe, an underwater robotic arm and hundreds of others. It moves there and performs the commanded action. Action is flower or nuts collection, insect suction pruning and hundred more. All operations are high trainable by human intervention and the system stores its approach and logic for future action correction.

Keywords: data science; minimalism; agriculture; cultivation; harvest; sensors; robots; full spectrum; aeronautical operations; blockchain; insurance; data science; internet of things; R language; data analytics (search for similar items in EconPapers)
Date: 2018
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