Plantae, a specialist in agricultural sensors, and the University of Carlos III of Madrid have developed new predictive models based on artificial intelligence that help optimize irrigation in crops such as olive groves. Using data from soil moisture, temperature, and other sensors, the system allows for more efficient irrigation planning, saving water and energy.
AI to optimize irrigation in crops
The project integrates a real-world database with more than 10,000 sensors deployed on farms, generating approximately 10 million data points per month.
Artificial intelligence algorithms are used to leverage this agricultural Big Data to anticipate water needs, calculate irrigation, predict adverse weather conditions (frost, hail, drought, heavy rain), and even warn of pests.
Data is collected by low-cost sensors and wireless communication, including:
- Soil moisture sensors: at different depths.
- Temperature sensors (soil and ambient).
- Flowmeters: to measure the volume of water distributed.
- Rain gauges, anemometers and solar radiation.
- In development: macronutrient sensors (NPK) for fertilizer diagnosis.
“Nuestro objetivo es anticiparnos a lo que va a ocurrir, facilitando al agricultor o al técnico recomendaciones específicas sobre cantidades de riego o acciones preventivas en sus cultivos con una antelación de hasta diez días”, ha precisado Samuel López, CEO y cofundador de Plantae.
In addition, improved water use, energy savings, climate and pest control, and the potential for a comprehensive system that even controls fertilization have been achieved. Water savings of up to 30% and 50% have been achieved on intensive farms thanks to irrigation optimization.
“Currently, our predictive models focus on olive trees and tomatoes, but the methodology we developed is applicable to more than 80 types of crops, from field crops to woody crops.”
Predictive models with AI to optimize irrigation
They are machine learning models, which are trained on large volumes of agricultural data.
- Artificial neural networks to recognize patterns in the evolution of humidity, temperature, and irrigation.
- Supervised predictive models, powered by real field data (more than 10 million records per month).
- Hybrid systems that combine meteorological inputs (weather forecasting) with data from soil and plant sensors.
Innovation is captured by digital media
New predictive models developed with AI to optimize irrigation management in crops such as olive groves
“All this data modeling allows us to anticipate the farm's needs, whether it's irrigation or air expulsion. Ultimately, data and AI are there to be used and make our lives easier, not only helping farmers with decision-making but also making agriculture more efficient and sustainable,” concludes Samuel López…+INFO)
Plantae innovates with AI and sensors to anticipate water needs in more than 80 crops.
“Data and artificial intelligence are there to be used and make our lives easier, helping not only farmers but also making agriculture more efficient and environmentally friendly,” concludes Samuel López…+INFO)
New predictive models developed with AI to optimize irrigation and crop management
“Plantae, una start-up apoyada por el Parque Científico C3N-IA de la Universidad Carlos III de Madrid (UC3M) y especializada en agricultura de precisión, ha desarrollado nuevos modelos predictivos mediante inteligencia artificial (IA) para anticipar las necesidades hídricas, optimizar el riego y aumentar la productividad de los cultivos”…(+INFO)
Sources
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