Identification of plant disease infection using soft-computing: Application to modern botany

Botany is the scientific examination of plants characters. Plant disease can be caused either by living organisms having wide range as harmful or benign ones which grow specifically on the horticultural plant leaves or deficiency of minerals e.g. iron, phosphate, nitrogen, etc. Modern agriculture requires smart and feasible techniques to replace the human intelligence with machine intelligence. Whereas a human identifies the disease infected leaves by his eye, the machine should also be capable of vision-based disease identification. The objective of this paper is to practically verify the applicability of a new computer-vision method for discrimination between the healthy and disease infected strawberry leaves which does not require neural network or time consuming trainings. The proposed method was tested under outdoor lighting condition using a regular DLSR camera without any particular lens. Since the type and infection degree of disease is approximated a human brain a fuzzy decision maker classifies the leaves over the images captured on-site having the same properties of human vision. Optimizing the fuzzy parameters for a typical horticultural field at a mid-day summer Mediterranean day in produced 96% accuracy for segmented iron deficiency and 93% accuracy for segmented using a typical human instant classification approximation as the benchmark holding higher accuracy than a human eye identifier. The fuzzy-base classifier provides approximate result for decision making on the leaf status as if it is healthy or not. (c) 2018 The Authors. Published by Elsevier B.V.