{"id":1389,"date":"2026-09-26T22:23:47","date_gmt":"2026-09-26T20:23:47","guid":{"rendered":"https:\/\/greatit.it\/demetra\/una-ai-vision-verso-il-controllo-dellagritech\/"},"modified":"2026-09-26T22:28:40","modified_gmt":"2026-09-26T20:28:40","slug":"una-ai-vision-verso-il-controllo-dellagritech","status":"publish","type":"post","link":"https:\/\/greatit.it\/demetra\/en\/una-ai-vision-verso-il-controllo-dellagritech\/","title":{"rendered":"An AI VISION for agritech control"},"content":{"rendered":"

Abstract<\/strong><\/p>\n

Plants change throughout the growing cycle, while many facilities continue to operate with the same setpoints.
\nPhenological stage, radiation, biomass and production targets call for dynamic strategies.
\nWhy treat a plant the same way when it changes every day?<\/p>\n

Think of a newly transplanted lettuce and that same plant a few days before harvest. Its leaf area, root system and biomass are substantially different. Leaves overlap, light penetrates the canopy differently, and air encounters new obstacles. Water and nutrient uptake change, as does the crop\u2019s contribution to the moisture balance of the growing environment.<\/p>\n

Yet the cultivation programme may remain identical: the same light intensity, irrigation intervals, temperature and humidity settings.<\/p>\n

This observation points to a direction we are interested in developing at GREAT IT: identifying indicators that reveal how the crop is progressing and using them to guide management decisions, with support from artificial intelligence.<\/p>\n

How to tell whether the crop is meeting its targets<\/strong><\/p>\n

In business management, we use performance indicators to assess whether results are in line with expectations. The same reasoning can be applied to cultivation.<\/p>\n

Growth rate, uniformity, quality and resource consumption per unit of product allow us to evaluate a cultivation recipe through the results it delivers. Some indicators are measured at harvest; others can be monitored throughout the cycle, while there is still time to intervene.<\/p>\n

We can observe how quickly the canopy expands, how water consumption changes, or how much individual plants within the same batch differ from one another.<\/p>\n

To make this information useful, we need a reference: what development pattern should we expect from that cultivar, at that stage and under those conditions? When is a deviation part of normal physiological variation, and when does it warrant further investigation?<\/p>\n

Research is already developing these tools<\/strong><\/p>\n

Studies are already using images and machine learning algorithms to estimate crop traits without having to rely on destructive sampling for every measurement.<\/p>\n

A study published in 2022 combined colour images, depth images and plant geometric traits to estimate lettuce shoot fresh weight through deep learning. It provides a concrete example of how visual observations can become quantitative measurements for tracking growth. Automatic monitoring of lettuce fresh weight by multi-modal fusion based deep learning<\/em><\/a><\/p>\n

Another study demonstrated the possibility of estimating lettuce biomass by combining a low-cost commercial RGB-D camera with a deep learning model. Non-destructive Plant Biomass Monitoring With High Spatio-Temporal Resolution<\/em><\/a><\/p>\n

These results provide a foundation for developing decision-support systems. Accurately estimating growth, however, is only one step: recommending an intervention requires connecting that growth with environmental conditions, the crop\u2019s history and the production target.<\/p>\n

\"Articolo
Figure 1 \u2014 Monitoring growth through RGB-D imaging. Experimental lettuce cultivation and a colour and depth imaging system used to estimate biomass without destroying the plants. Source: Buxbaum, Lieth and Earles (2022), Figure 1, Frontiers in Plant Science. <\/em>DOI: 10.3389\/fpls.2022.758818<\/em><\/a>. Licensed under <\/em>CC BY 4.0<\/em><\/a>.<\/em><\/figcaption><\/figure>\n

Which sensors can help us?<\/strong><\/p>\n

Sensing technologies offer different tools for observing the environment and plant responses.<\/p>\n

Air temperature and humidity, CO\u2082, photosynthetically active radiation, nutrient solution temperature, pH and EC describe important growing conditions. These measurements can be complemented by tools that observe the crop more directly:<\/p>\n