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The overall well-being and productivity of crops rely on a series of interconnected processes throughout their entire growth cycle. Among these processes, the quality of spraying plays a pivotal role in maintaining crop health and ensuring increased productivity. An Unmanned Aerial Vehicles assisted particle spraying system in agriculture involves the use of Unmanned Aerial Vehicles equipped with specialized equipment to distribute particles such as plant protection products, fertilizers, or other agricultural inputs over crops. This technology offers several advantages over traditional ground-based methods, including increased efficiency, precision, and reduced environmental impact. The effectiveness of spraying, in turn, hinges on various factors, one of which is the even distribution of spraying droplets. Consequently, there exists a need for a reliable, consistent, precise, and accurate automated method to assess the parameters governing this distribution. In this study, a methodology is introduced for evaluating the quality of plant spraying, and the results of this method's testing are presented. Data is gathered by employing water-sensitive papers positioned on the crops, which are then scanned using an industrial-grade camera. Subsequently, this data undergoes processing through image analysis algorithms using Matlab. The outcomes of the research demonstrate the robustness of the proposed methodology in obtaining the essential data required for determining spraying distribution. Compared to existing solutions, the presented approach offers increased reliability, consistency, precision, and automation, thereby addressing the need for a more reliable and accurate method of assessing spraying quality in agriculture.
Gogolák et al. (Fri,) studied this question.