Detection of spider mite using artificial intelligence in digital viticulture

Quercetin precipitation has become an increasingly common issue in red wine, often resulting in visually unpleasant sediments and diminished product quality.

In vineyards, management and the surrounding landscape can have different effects on spiders. In temperate regions management (organic vs. conventional) may have less strong effects than for other crops.

El tema que me corresponde tratar en esta mini conferencia sobre “Caracterización vitivinícola de las Malvasías en Canarias”, es por razones obvias la parte que atañe a la Isla de La Palma.

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

Worldwide, with an average of 6.7 million cultivated hectares, of which exclusively 51% in Europe (faostat, 2021), the production of table and wine grapes is a leading sector, with continued growth in Europe in the area devoted to vine cultivation. during the growing season, most of the plant organs can be susceptible to several fungal and oomycete diseases, leading to important economic losses and causing detrimental effects on fruit quality. the increasingly scarce availability of fungicidal products, often also related to their relative impact on the environment, coupled with the emergence of resistance in the pathogen to these products, make defence increasingly challenging.