Artificial intelligence to manage crops with less water

The new generation of agricultural sensors developed by the PRISMA research group of the Polytechnic of Cartagena allows to know soil parameters that would estimate the energy status of the soil and its water retention capacity to adapt and optimize irrigation, also enabling the automation of deficit irrigation strategies using Artificial Intelligence to accurately predict the water status of crop plots .

Juan Domingo González with the latest developed sensor; behind, Ana Toledo and Roque Torres.

These sensors are the main object of the doctoral thesis that Juan Domingo González has just defended at the UPCT and which has received a European quality mention for his research stays in the United Kingdom and the United States. Directed by the professors of the School of Industrialists Roque Torres and Ana Toledo, the research has generated more sophisticated equipment for measuring water in the soil than those currently used by precision agriculture.

“The conventional thing is to measure the volume of water in the soil, but with these sensors physical properties of interest could be estimated, such as the density or texture of the soil,” explains Roque Torres and gives the different water retention capacities of the land as an example. sandy versus clayey. “Knowing if the soil has a greater or lesser retention capacity allows you to adapt irrigation so as not to waste a drop of water,” he remarks.

“There are no sensors like this on the market”, underlines the author of the thesis, indicating that this technological evolution facilitates the optimization of irrigation and has other potential uses, such as the detection of moisture in loads of grain to avoid food waste.

The thesis also shows the results obtained by the UPCT researchers in trials with cherry crops, managing to reduce up to 45% irrigation in summer using deficit strategies without affecting production, and demonstrates that the use of Artificial Intelligence algorithms together With soil sensors and weather information, it allows automating deficit irrigation strategies.

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