Deep Learning Tools for the Automatic Measurement of Coverage Area of Water-Based Pesticide Surfactant Formulation on Plant Leaves

Abstract

A method to efficiently and quantitatively study the delivery of a pesticide-surfactant formulation in a water solution to plant leaves is presented. The methodology of measurement of the surface of the leaf wet area is used instead of the more problematic measurement of the contact angle. A method based on a Deep Learning model was used to automatically measure the wet area of cucumber leaves by processing the frames of video footage. We have individuated an existing Deep Learning model, called HED-UNet, reported in the literature for other applications, and we have applied it to this different task with a minor modification. The model was selected because it combines edge detection with image segmentation, which is what is needed for the task at hand. This novel application of the HED-UNet model proves effective, and opens a wide range of new applications, the one presented here being just a first example. We present the measurement technique, some details of the Deep Learning model, its training procedure and its image segmentation performance. We report the results of the wet area surface measurement as a function of the concentration of a surfactant in the pesticide solution, which helps to plan the surfactant concentration. It can be concluded that the most effective concentration is the highest in the range tested, which is 11.25 times the CMC concentration. Moreover, a validation error on the Deep Learning model, as low as 0.012 is obtained, which leads to the conclusion that the chosen Deep Learning model can be effectively used to automatically measure the wet area on leaves.

Plant phenotyping relevance

植物葉面の濡れ面積という観測可能な葉形質を、動画画像と深層学習で自動抽出・定量する手法が研究の中心であり、学習手順と性能検証も示されているため。

Code and data availability

The paper describes custom Python/OpenCV/PyTorch HED-UNet segmentation code and video-derived wet-area measurements, but no public dataset, image, code, or model deposit is mentioned. The only URL in the text (http://www.python) is a generic Python language reference, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.