Precision agriculture is a farming system based on the combination of detailed observations, measurement, and rapid response used to optimise energetic input to maximise crop production. Precision agriculture uses a Decision Support System (DSS) for optimising farm management. In this context, ‘EVJA: Observe, Prevent, Improve’ (or just ‘EVJA’) is an intelligent support system used for precision agriculture. A vast set of data (temperature, relative humidity, deficit of vapour pressure, leaf wetness, solar radiation, carbon dioxide concentration, and soil moisture) is continuously collected, submitted to a local control unit, and processed through algorithms specifically developed for different crops. On the other hand, farmers can access EVJA from their PC and mobile devices, and they may monitor complex agronomic data analysis presented in a user-friendly interface. In this article, we show how EVJA works and how its output can be used to assess the health status of plants through a specific set of functions. Moreover, we show the methodology utilised to develop useful predictive models based on this information. Specifically, we describe a predictive algorithm that is capable of predicting the infection risks of downy mildew for baby leaves plantations and for Fusarium ear blight of wheat.
Introducing EVJA: A ‘rugged’ intelligent support system for precision farming / Loret, N.; Affinito, A.; Bonanomi, G.. - In: ACTA IMEKO. - ISSN 0237-028X. - 9:2(2020), pp. 83-88. [10.21014/acta_imeko.v9i2.795]
Introducing EVJA: A ‘rugged’ intelligent support system for precision farming
Bonanomi G.
2020
Abstract
Precision agriculture is a farming system based on the combination of detailed observations, measurement, and rapid response used to optimise energetic input to maximise crop production. Precision agriculture uses a Decision Support System (DSS) for optimising farm management. In this context, ‘EVJA: Observe, Prevent, Improve’ (or just ‘EVJA’) is an intelligent support system used for precision agriculture. A vast set of data (temperature, relative humidity, deficit of vapour pressure, leaf wetness, solar radiation, carbon dioxide concentration, and soil moisture) is continuously collected, submitted to a local control unit, and processed through algorithms specifically developed for different crops. On the other hand, farmers can access EVJA from their PC and mobile devices, and they may monitor complex agronomic data analysis presented in a user-friendly interface. In this article, we show how EVJA works and how its output can be used to assess the health status of plants through a specific set of functions. Moreover, we show the methodology utilised to develop useful predictive models based on this information. Specifically, we describe a predictive algorithm that is capable of predicting the infection risks of downy mildew for baby leaves plantations and for Fusarium ear blight of wheat.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


