The phenological evolution is a crucial aspect of grapevine growth and development. Accurate detection of phenological stages can improve vineyard management, leading to better crop yield and quality traits. However, traditional methods of phenological tracking such as on-site observations are time-consuming and labour-intensive. This work proposes a scalable data-driven method to automatically detect key phenological stages of grapevines using satellite data.
Use of artificial intelligence for the prediction of microbial diseases of grapevine and optimisation of fungicide application
Plasmopara viticola, the causal agent of downy mildew (DM), and Uncinula necator, the causal agent of powdery mildew (PM), are two of the main phytopathogenic microorganisms causing major economic losses in the primary sector, especially in the wine sector, by wilting bunches and leaves with a consequent decrease in the photosynthetic rate of the plant and in the annual yield. Currently, the most widespread methods for planning spraying are based on the 3-10 rule, which states that the first application should take place when: (i) the air temperature is greater than 10°C; (ii) shoots are equal or greater than 10 cm; and (iii) a minimum of 10 mm rainfall within 24–48 hours has occurred, or at the beginning of the bud break with periodic applications according to the manufacturer’s instructions.
Agriculture plantations are complex systems whose performance critically depends on the execution of several types of tasks with precise timing and efficiency to respond to different external factors. This is particularly true for orchards like vineyards, which need to be strictly monitored and regulated, as they are sensitive to diverse types of pests, and climate conditions. In these environments, managing and optimally scheduling the available work force and resources is not trivial and is usually done by teams of senior managers based on their experience.