Analyzing the Efficiency of Pesticide-Surfactant Formulation Delivery on Plant Leaves
In this study, a novel method is introduced to effectively and quantitatively analyze the delivery of pesticide-surfactant formulations on the surface of plant leaves. Traditionally, researchers have relied on measuring the contact angle of the solution on the leaves to assess its wetting ability. However, this study proposes a new approach that focuses on measuring the wet area of the leaves instead.
The researchers employed a deep learning model to automatically measure the surface area of cucumber leaves wet with a pesticide-surfactant solution. To carry out this analysis, they processed video footage frames using the trained deep learning model. It is worth mentioning that the researchers modified an existing deep learning model, which had previously been applied to other applications, to serve this specific purpose.
The measurement technique itself is described in detail, providing insights into the methodology used to assess the wet areas on the leaves. Additionally, the paper elaborates on the deep learning model employed, its training procedure, and its image segmentation performance. This information helps establish the validity and reliability of the results obtained through this technique.
One notable aspect of this study is the exploration of surfactant concentration in the pesticide solution and its impact on the wet areas’ surface measurement. By varying the surfactant concentration, they were able to observe how changes affected the effectiveness of the pesticide-surfactant formulation on leaf wetting.
Overall, this study presents a promising and innovative approach to measuring the delivery efficiency of pesticide-surfactant formulations on plant leaves. By employing a deep learning model and focusing on wet area measurement instead of contact angle assessment, researchers can obtain quantitative data that may lead to further advancements in optimizing pesticide application techniques.