This study presents a protocol for applying Interpretable Machine Learning (IML) to enhance communication within Variety Registration Offices (VROs). Rather than focusing on a model comparison, we illustrate how two IML-compatible models—Random Forests and AMBARTI—can support a clearer interpretation of genotype-by-environment (G×E) interactions and variable importance. Using multi-environment wheat trial data from CREA-DC-Milano across Italian sites, we predicted the yield and protein content while visualizing the performance patterns. Genotype g25 ranked first in protein across both years, while g20 led in yield in Year 1. Tolentino consistently supported higher protein levels; Torino and Tolentino led in yield, varying by year. These insights, made accessible through intuitive IML visualizations, proved valuable in supporting VRO, reinforcing the role of IML as a practical communication tool in regulatory processes, agricultural innovation, and food security.
This study addresses the problem of pre-harvest fruit commodity grade prediction in intelligent orchards. To overcome the limitations of conventional post-harvest grading systems, including delayed quality evaluation, the limited representation capability of single-modality appro…
This review provides a comprehensive bibliometric analysis of the literature on the integration of remote sensing data and machine learning or deep learning algorithms in precision agriculture. The analysis covers 1056 publications, included in the Web of Science Core Collection,…
Gully erosion is a significant threat to the sustainability of soil in Mediterranean basins. Despite its impact, there is a lack of research providing accurate regional-scale cartography of complete gully networks. This study aims to automatically map the gully network in the oli…
Modern combine harvesters can collect real-time geolocated yield data, but it is subject to errors. Various protocols have been proposed to clean this data, each with varying levels of complexity. This data is valuable for precision agriculture to implement site-specific manageme…
Accurately quantifying grazing intensity (GI) is crucial for assessing grassland utilization and supporting sustainable management. Traditional livestock-based approaches cannot capture the spatial heterogeneity of grazing or its dynamic response to climate variability. The objec…
Advanced computer vision (CV) and deep learning (DL) are essential for sustainable agriculture via automated vegetation management. This paper methodically reviews advancements in these technologies for agricultural settings, analyzing their fundamental principles, designs, syste…