Is it relevant to consider remote sensing information for targeted plant monitoring?

Aljarafe Alto est une petite zone naturelle dans le département de Séville (Espagne), où le cépage autochtone cultivé est le Palomino Garrido Fino.

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.

Today, a variety of remote sensing tools are used to characterise plant performance. However, the vine is rarely studied, as a major crop specificity is canopy discontinuity. Registered images of the vineyard are anisotropic, therefore difficult to analyse.

Grape phenolic compounds are located in the internal layers of grape skins and seeds. They are synthesized via the phenyl-propanoid biosynthetic pathway which is modulated by both biotic and abiotic factors.

In previous experiments carried out in Bologna on Sangiovese grapevines raised with the Australian “Minimal Pruning” system, it has been shown that this system left an excessive burden of buds on the vine.