AI for monitoring crop roots

Artificial vision

Q: Do I need any special device to use AI in my crops?

No, el objetivo de esta tecnología es facilitar el acceso a datos relevantes del cultivo, simplemente disponiendo de un teléfono o cámara fotográfica.

Q: Is there a protocol or methodology for taking photos?

Sí, para una correcta estandarización y mejor calidad de los datos recogidos, recomendamos una serie de sencillos consejos a la hora de tomar la fotografía tanto para las raíces como para el área foliar.

Q: What are the advantages and limitations of computer vision in crops?

En comparación con otras alternativas del mercado, se trata de una tecnología que no necesita estar en condiciones controladas ni utilizar dispositivos especiales, lo cual, facilita la adopción de la tecnología a la realidad de la agricultura fuera de la investigación. La principal limitación, es que, la fotos o muestras tomadas sean representativas tanto del resto de plantas como del individuo en sí, por eso, es recomendable interiorizar el protocolo y tomar las imágenes con criterio alineado al dato objetivo.

Q: Which crops is computer vision compatible with? Which ones have we worked with?

Hemos trabajado con gran cantidad de cultivos y variedades. Como por ejemplo, cultivos de alto valor como arándanos, aguacate, uva de mesa, cítricos, cerezas, avellanos…. Y cultivos anuales como maíz, canola, trigo, girasol, poroto, etc.

Q: Is the root analysis method invasive?

En la parte de cuantificación foliar no se trata de un método invasivo sobre la planta, pero en la cuantificación radicular, es necesario visualizar las raíces para ser fotografiadas, por lo que será necesario alguno de los siguientes formatos: • Calicata • Maceta • Rhizotron • Raíces Limpias

Q: Can computer vision be combined with automated irrigation technology?

Efectivamente, toda la tecnología de Agrow está pensada para trabajar junta y aportarse valor entre sí. Para en sistema de recomendación de riego, nuestras herramientas de visión artificial permiten: • Conocer el IAF o área foliar real, lo cual, nos permite ajustar aún más la componente de transpiración de las plantas. • Identificar la distribución de raíces absorbentes a lo largo del perfil del suelo, lo cual, nos permite ajustar objetivamente la cantidad de agua disponible para las plantas y los riegos ideales para aplicar. • Modelizar en base a datos reales el crecimiento radicular en cada etapa fenológica.

Measure crop key-parameters with your phone camera

For producers and biostimulant companies

Quickly and accurately measure essential crop parameters using artificial vision technology with your phone camera. Enhance farming efficiency and productivity with this powerful tool.

Challenges in crop monitoring

Objectively verify and understand the impact of various treatments, such as fertilizers or biostimulants, on plant canopy and root system development.

Our AI-based solution

Accurately and consistently quantify the physiological traits of your plants, simplifying measurements and enabling scalable comparisons.

Objective quantification

New data availability

Homogeneity of the analysis

Leaf and root area quantification

Services associated with our artificial vision technology

Leaf and Root Area Quantification

We accurately measure plant development, analyzing foliage and root growth to optimize crop management.

Treatment Effectiveness Analysis

We evaluate the impact of fertilizers and phytosanitary products through AI, detecting improvements in crop health and yield.

Integration with Smart Irrigation Platforms

We connect our data with advanced irrigation systems, allowing automatic adjustments for more efficient water use.

Frequently asked questions about computer vision

Do I need any special device to use AI in my crops?

No, this technology is designed to provide easy access to relevant data and detailed crop traits using just the camera on your mobile phone or a photographic camera.

Is there a protocol or methodology for taking photos?

Yes, for standardised and higher-quality results, we recommend a straightforward protocol and guidance for taking photos. This protocol should be followed for both leaf area and root systems.

What are the advantages and limitations of computer vision in crops?

Unlike other market solutions, this technology doesn't require controlled conditions or specialized devices. It simplifies adoption, making it practical for real-world agricultural applications beyond research settings.

Additionally, its accuracy makes it ideal for R&D departments seeking to quantify and evaluate results from various treatments and experimental testing with ease.

Which crops is computer vision compatible with? Which ones have we worked with?

We have successfully applied our technology to a wide range of crops, including blackberries, avocados, grapes, citrus, sweet cherries, and hazelnuts. Additionally, we have implemented it in annual crops such as maize, canola, wheat, sunflower, and beans.

Is the root analysis method invasive?

For leaf area analysis, our method is non-invasive. However, quantifying root systems requires visualizing the roots to capture images. Depending on the sample size, the following techniques can be implemented:

• Soil pit
• Pot
• Rhizotron
• Clean roots

Can computer vision be combined with automated irrigation technology?

Indeed, all of Agrow's technology is designed to work together and add value to each other. For the irrigation recommendation system, our artificial vision tools enable:

• Knowing the LAI or actual leaf area, which allows us to further adjust the transpiration component of the plants.

• Identify the distribution of feeder roots throughout the soil profile, enabling us to objectively adjust the available water for plants and optimize irrigation based on water demand.

• Model root growth at each phenological stage using real data.