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 an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.

This work aimed to evaluate the potential of visible-near-infrared (VNIR) and thermal infrared (TIR) imagery, acquired from an unmanned aerial vehicle (UAV), to detect vine water status. Three irrigation treatments were designed to impose weekly evapotranspiration (ET) to K C = 0.2, K C = 0.4 and K C = 0.8 of reference ET. In-situ leaf area index (LAI) and midday leaf (Ψ Leaf ) and stem water potential were collected during seven UAV overpasses. TIR-based temperature correlated highly with the water status variability observed between treatments (Ψ Leaf : r = -0.68). However, VNIR indices were less correlated with Ψ Leaf (r < 0.4), revealing the importance of TIR imaging to capture the vine physiological response to water stress, with foliage differences being less apparent between treatments.