期刊
AGRONOMY-BASEL
卷 13, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/agronomy13030678
关键词
droplet size distribution; image analysis; laser diffraction; PDPA; spray quality; spray characterization system
Plant protection control is a complex task, and droplet size plays a significant role in deposition and pesticide effectiveness. The correct droplet size ensures the required dose reaches the target area, reduces off-target losses, and enhances operator safety. This paper critically analyzes various methods for measuring droplet size, including intrusive and non-intrusive methods, and discusses their application in machine-learning-based approaches.
Plant protection control based on the spray application of plant protection products is a very complex task depending on a series of factors, among which droplet size is the most influential for deposition and pesticide effectiveness. In fact, the adoption of the correct droplet size can ensure that the required dose reaches the target area and is not wasted, minimizes the off-target losses due to evaporation, drift and run-off and, at the same time, enhances the operator's safety in terms of inhalation, ingestion and dermal exposure. In this paper, after defining some mean characteristic diameters helpful for a description of a drop population and focusing on the main drop size distribution functions for the statistical characterization of sprays, a critical analysis of known methods, both intrusive and non-intrusive, for drop size measurement is carried out by reviewing the literature. Among intrusive methods, the liquid immersion method and the use of water-sensitive papers are discussed, whereas, among non-intrusive methods, laser-based systems (laser diffraction, phase Doppler particle analysis) and high-speed imaging (shadowgrapy) are presented. Both types of method, intrusive and non-intrusive, can be used in machine-learning-based approaches exploiting regression techniques and neural network analysis.
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