Effective crop protection requires early and accurate detection of biotic stress. In recent
years, remarkable results have been achieved in the early detection of weeds, plant …

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the
visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) …

Deep Learning (DL), a type of Machine Learning, has gained significant interest in many
fields, including agriculture. This paper aims to shed light on deep learning techniques used …

Hyperspectral signatures can provide abundant information regarding health status of crops;
however it is difficult to discriminate between biotic and abiotic stress. In this study, the case …

Plant stress is one of the most significant factors affecting plant fitness and, consequently,
food production. However, plant stress may also be profitable since it behaves hormetically; …

Current approaches for accurate identification, classification, and quantification of biotic and
abiotic stresses in crop research and production are predominantly visual and require …

The timely detection of crop diseases is critical for securing crop productivity, lowering
production costs, and minimizing agrochemical use. This study presents a crop disease …

Over the last 20 years, researchers in the field of digital plant pathology have chased the
goal to implement sensors, machine learning and new technologies into knowledge-based …

Crop protection is a key activity for the sustainability and feasibility of agriculture in a current
context of climate change, which is causing the destabilization of agricultural practices and …

Modern agriculture and food production systems are facing increasing pressures from
climate change, land and water availability, and, more recently, a pandemic. These factors …