  The need for higher agricultural productivity has demanded the intensive use
of pesticides. However, their correct use depends on assessment methods that
can accurately predict how well the pesticides' spraying covered the intended
crop region. Some methods have been proposed in the literature, but their high
cost and low portability harm their widespread use. This paper proposes and
experimentally evaluates a new methodology based on the use of a
smartphone-based mobile application, named DropLeaf. Experiments performed
using DropLeaf showed that, in addition to its versatility, it can predict with
high accuracy the pesticide spraying. DropLeaf is a five-fold image-processing
methodology based on: (i) color space conversion, (ii) threshold noise removal,
(iii) convolutional operations of dilation and erosion, (iv) detection of
contour markers in the water-sensitive card, and, (v) identification of
droplets via the marker-controlled watershed transformation. The authors
performed successful experiments over two case studies, the first using a set
of synthetic cards and the second using a real-world crop. The proposed tool
can be broadly used by farmers equipped with conventional mobile phones,
improving the use of pesticides with health, environmental and financial
benefits.
