Deep learning (DL) based instance segmentation has attracted a growing research interest
in the scientific community to tackle precision agriculture problems over the past few years …

The scientific progress in artificial intelligence and robotics has enabled precision viticulture
to pursue sustainability and improve the final yield. For instance, monitoring the canopy …

Based on the current research on the wine grape variety recognition task, it has been found
that traditional deep learning models relying only on a single feature (eg, fruit or leaf) for …

Pests significantly negatively affect product yield and quality in agricultural production.
Agricultural producers may not accurately identify pests and signs of pest damage. Thus …

Accurately predicting grape yield in vineyards is essential for strategic decision-making in
the wine industry. Current methods are labour-intensive, costly, and lack spatial coverage …

Precision Viticulture (PV) is becoming an active and interdisciplinary research field since it
requires solving interesting research issues to concretely answer the demands of specific …

Fungi can be used as the environmental bioindicators of a given area. Detection and
localization of mushrooms in their natural habitats represent an important task that can help …

Lettuce (Lactuca sativa L.) is highly susceptible to drought and water deficits, resulting in
lower crop yields, unharvested areas, reduced crop health and quality. To address this, we …

Grape harvesting is a crucial process in the wine-making industry, and the accurate
classification of grape aging stages can significantly improve the efficiency and quality of …