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

Fabio Grazioso (), Anzhelika Aleksandrovna Atsapina, Gardoon Lukman Obaeed Obaeed and Natalia Anatolievna Ivanova
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Fabio Grazioso: Photonics and Microfluidics Laboratory, Tyumen State University, Volodarskogo 6, Tyumen 625003, Russia
Anzhelika Aleksandrovna Atsapina: Photonics and Microfluidics Laboratory, Tyumen State University, Volodarskogo 6, Tyumen 625003, Russia
Gardoon Lukman Obaeed Obaeed: Photonics and Microfluidics Laboratory, Tyumen State University, Volodarskogo 6, Tyumen 625003, Russia
Natalia Anatolievna Ivanova: Photonics and Microfluidics Laboratory, Tyumen State University, Volodarskogo 6, Tyumen 625003, Russia

Agriculture, 2023, vol. 13, issue 12, 1-19

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.

Keywords: pesticides; surfactants; wet area; deep learning; segmentation; precision agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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