Predicting and controlling plant behavior in controlled environments is a growing requirement in precision agriculture. Sensor networks and artificial intelligence methods represent key aspects for optimizing the processes of data acquisition, mathematical modeling and decision making. This paper presents a general architecture for automatic greenhouse control. In particular, a preliminary model for predicting the risk of new infections of downy mildew (Peronospora belbahrii) on sweet basil in greenhouse was designed. The architecture has three main elements of innovation: new kinds of sensors are used to acquire information about the state of the plants, model predictors are generated from this information by non-trivial processing methods, and informative predictors are automatically selected using regularization techniques.
Expo-agri: first results related to a predictive model applied to peronospora infections of basil in greenhouse
Castellini, A.;Farinelli, A.;Quaglia, D.;
2018-01-01
Abstract
Predicting and controlling plant behavior in controlled environments is a growing requirement in precision agriculture. Sensor networks and artificial intelligence methods represent key aspects for optimizing the processes of data acquisition, mathematical modeling and decision making. This paper presents a general architecture for automatic greenhouse control. In particular, a preliminary model for predicting the risk of new infections of downy mildew (Peronospora belbahrii) on sweet basil in greenhouse was designed. The architecture has three main elements of innovation: new kinds of sensors are used to acquire information about the state of the plants, model predictors are generated from this information by non-trivial processing methods, and informative predictors are automatically selected using regularization techniques.
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