The relevance of optical sensors in precision agriculture is steadily growing, owing to their ability to furnish comprehensive insights into crop conditions. Technologies like computer vision systems, hyperspectral cameras, and LIDAR sensors offer the means to monitor various crop attributes such as fruit ripening, or the presence of diseases or pests, thereby serving as invaluable aids for farmer decision-making to optimise resource utilisation. Moreover, with ongoing technological advancements, the associated costs are significantly declining, rendering these sensors conducive to widespread adoption of smart farming practices. However, their utilisation still encounters significant challenges that may impede their full potential, particularly concerning the unpredictable and harsh outdoor field conditions, including highly variable ambient lighting.
Artificial intelligence (AI) emerges as a potent tool to surmount these challenges. In this context, NOVATERRA has harnessed these evolving data processing strategies in the successful development of an optical sensor for automatic in-field canopy characterisation in vineyards and olive groves. By amalgamating standard color imaging with depth sensing, alongside AI-driven algorithms, this system has proven its ability to accurately assess vegetation structure at a low cost and with high spatial resolution across plots. This breakthrough underscores the immense potential of combining optical sensors with AI to address current and future crop monitoring requisites.
