We are proud to announce the publication of two studies that mark an important step forward in agricultural research, bringing together advanced technologies such as artificial intelligence and positron emission tomography (PET) with new perspectives for more sustainable and innovative farming.

The first article, published in Physics in Medicine & Biology under the title "A hybrid predictor-corrector network and spatiotemporal classifier method for noisy plant PET image classification" (Weike Chang et al., 2025), introduces a cutting-edge method for analyzing plant PET images. By novel predictor-corrector neural network with a spatiotemporal classifier, our approach delivers highly accurate, time-aware classification--even under challenging imaging conditions. It represents a key advancement for precision agriculture, opening new pathways to monitor crop health, support breeding programs, and develop more resilient and sustainable agricultural practices.

The second study, published in Smart Agricultural Technology with the title "Assessing the effects of biostimulants on spinach through a Field-to-Lab approach for PET imaging", marks the first successful use of PET in a real agricultural setting. In this research, spinach plants were cultivated in the field with the application of biostimulants--natural substances that enhance growth and resilience--and then analyzed in the lab using advanced imaging. This made it possible to track, in real time, the flow of nutrients and energy within the plant, observing its internal functions without interference. The "field-to-lab" approach represents a breakthrough, as it enables the precise and rapid evaluation of sustainable agricultural products, accelerating their development and validation.

Together, these two studies demonstrate how frontier technologies can revolutionize the way we observe and understand plants. The application of PET imaging and artificial intelligence to agriculture is not only a technological challenge but also a concrete opportunity to improve crop productivity, reduce environmental impact, and address the challenges of climate change with innovative tools.