A Non-deterministic Finite Automata Model for Identification of
Ambiguities in USDA Triangle Soil Texture Classification: A Novel
Approach
Abstract
Finite automaton is the core concept of computer science used for
designing abstract mathematical machines. Soil texture classification
model plays crucial role in agriculture engineering to increase water
productivity and yield. The USDA triangle soil texture classification is
most widely used model and it comprises of 12 classes. The sand, silt
and clay fractions of soil are the input elements for USDA
classification model. In this paper, we propose a novel
non-deterministic finite automata model for USDA triangle soil texture
classification and identified the ambiguous classes. As far as we know,
this is the first time that a finite automata framework has been
proposed for soil texture classification. Experimental results of this
work, reveals that 50% of USDA triangle soil texture classes are
ambiguous. In addition, the proposed non-deterministic finite automata
model for soil texture classification opens up future research to design
unambiguous extended USDA soil texture classification model using
deterministic finite automata.