Automatic measurement of coverage area of water-based
  pesticides-surfactant formulation on plant leaves using deep learning tools
Fabio Grazioso Anzhelika A. Atsapina Gardoon L. O. Obaeed Natalia A. Ivanova
A method to efficiently and quantitatively study the delivery of a
pesticide-surfactant formulation in water solution over plants leaves is
presented. Instead of measuring the contact angle, the surface of the leaves
wet area is used as key parameter. To this goal, a deep learning model has been
trained and tested, to automatically measure the surface of area wet with water
solution over cucumber leaves, processing the frames of video footage. We have
individuated an existing deep learning model, reported in literature for other
applications, and we have applied it to this different task. We present the
measurement technique, some details of the deep learning model, its training
procedure and its image segmentation performance. Finally, we report the
results of the wet areas surface measurement as a function of the concentration
of a surfactant in the pesticide solution.