Author:
Marengo Enrico,Roveri Norberto,Marengo Dario
Abstract
Today's agriculture must find increasingly innovative technological solutions with a perspective of conservation agriculture with systems that have less and less environmental impact by means of automatic control instruments for the distribution of products and the monitoring of site-specific ecological- environmental parameters. Since the symptoms of a disease attack vary depending on the nature of the pathogen and certain optical characteristics allow the development of imaging techniques for diseases detection, the plot monitoring allows to distribute the dose of product deemed suitable for the different areas into which it is divided: it is carried out by drones that, through multi-spectral analysis and specific artificial intelligence algorithms, make it possible to choose the appropriate doses of products to treat the disease at its onset or to prevent it. Processes can thus be optimised by spraying at variable rates in the quantities required at the time when pathologies are detected in order to intervene promptly for their treatment or containment. The use of these technologies allows a further reduction in Cu utilisation, compared to what was exposed at the 43° congress, thanks to applications with functionalised HAB: the timely and precise intervention suggested by the algorithms reduces the use of Cu to the bare minimum, the water consumption, the precision spraying with drones, the amount of product, its dispersion in the environment and the treatment time, safeguarding the health of the operator.
Reference20 articles.
1. Cugnetto A., Altare M., Masoero G., Guidoni S. (2023). Monitoring the seeds phenolic maturity in Nebbiolo vineyard by means of NDVI index vs foliar NIR spectroscopy. GEOdaysIT-BARI
2. Masoero G. (2022). Valorizzazione di Vezzolano e Albugnano con le Ricerche dell’Accademia di Agricoltura. Siro
3. Norberto Roveri. The role of biomimetism in developing nanostructured inorganic matrices for drug delivery
4. Qin J., Chao K., Kim M.S., Lu R., & Burks T.F. (2013). Hyperspectral and multispectral imaging for evaluating food safety and quality. Journal of Food Engineering