Automatic measurement of coverage area of water-based pesticides-surfactant formulation on plant leaves using deep learning tools
This provides an incremental improvement for agricultural researchers by automating wet area measurement, but it applies an existing method to new data without novel methodological contributions.
The paper tackled the problem of quantitatively measuring pesticide-surfactant coverage on plant leaves by using a deep learning model to automatically segment and measure wet areas from video footage of cucumber leaves, reporting results as a function of surfactant concentration.
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.