Open AccessThe Minimum Agriculture-Chunk as an Elementary Data Science Component in ADAM, a Micro Targeted, Trainable, Modular, Multipurpose System for Land Farming
Author(s) -
Panagiotis Serdaris,
Konstantinos Spinthiropoulos,
Michael Agrafiotis,
Athanasios Zisopoulos
Publication year - 2018
Publication title -
journal of agricultural studies
Language(s) - English
Resource type - Journals
ISSN - 2166-0379
DOI - 10.5296/jas.v6i4.14116
Subject(s) - modular design , underwater , pruning , computer science , action (physics) , field (mathematics) , component (thermodynamics) , agriculture , data collection , artificial intelligence , tree (set theory) , agricultural engineering , mathematics , engineering , geography , operating system , agronomy , mathematical analysis , statistics , physics , thermodynamics , archaeology , quantum mechanics , pure mathematics , biology
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.