Vegetation indices (VIs) have long been a crucial tool for monitoring plant growth and health, assessing the impact of environmental factors on vegetation, and supporting decision-making processes in agriculture and forestry. Traditionally, these mathematical formulations, leveraging the spectral response of plants to sunlight, have been instrumental in assessing vegetation health. However, emerging research suggests some unconventional applications that extend the scope of VIs. This article surveys the traditional and novel uses of VIs in various fields, including other applications such as climate change studies, disaster management, or microorganism assessment. It underscores the value of VIs, such as the normalized difference vegetation index (NDVI), or the enhanced vegetation index (EVI), in tracking climate-related shifts, mitigating the impact of disasters, assessing microbial terroir, organic production, and even aiding the wine industry. Nonetheless, despite their promise, the practical application of VIs also presents interpretational and technical challenges that need to be addressed. Hence, while the vast potential of VIs is underscored in this article, it also calls for continued technological advancements and research efforts to fully harness their benefits, ultimately aiding in the sustainable management of our environment. The objective of this review is not only to reflect on the current situation, but also to explore innovative and unconventional applications of vegetation indices. This involves anticipating the potential of this dynamic and rapidly advancing scientific tool, rather than solely following mainstream approaches.

Remote Sensing (RS) allows the estimation of some important vineyard parameters. There are several platforms for obtaining RS information. In this context, Sentinel satellites are a valuable tool for RS since they provide free and regular images of the earth’s surface. However, several problems regarding the low-resolution of the imagery arise when using this technology, such as handling mixed pixels that include vegetation, soil and shadows. Under this condition, the Normalized Difference Vegetation Index (NDVI) value in a particular pixel is an indicator of the amount of vegetation (canopy area) rather than the NDVI from the canopy (as a vigour expression), but its reliability varies depending on several factors, such as the presence of mixed pixels or the effect of missing vines (a vineyard, once established, generally loses grapevines each year due to diseases, abiotic stress, etc.). In this study, a vine removal simulation (greenhouse experiment) and an actual vine removal (field experiment) were carried out. In the field experiment, the position of the Sentinel-2 pixels was marked using high-precision GPS. Controlled removal of vines from a block of cv. Cabernet Sauvignon was done in four steps. The removal of the vines was done during the summer of 2019, matching with the start of the maximum vegetative growth. The Total Leaf Area (TLA) of each pixel was calculated using destructive field measurements. The operations were planned to have two satellite images available between each removal step. As a result, a strong linear relationship (R² = 0.986 and R² = 0.72) was obtained between the TLA and NDVI reductions, which quantitatively indicates the effect of the missing vines on the NDVI values.

A few decades ago, farmers could precisely monitor their croplands just by walking over the fields, but this task becomes more difficult as farm size increases. Precision viticulture can help better understand the vineyard and measure some key structural parameters, such as the Leaf Area Index (LAI). Remote Sensing is a typical approach to monitoring vegetation which measures the spectral information directly emitted and reflected from vegetation. This study explores a new method for estimating LAI which measures the projected shadows of plants using UAV (unmanned aerial vehicle) imagery. A flight mission over a vineyard was scheduled in the afternoon (15:30 to 16:00 solar time), which is the optimal time for the projection of vine shadows on the ground. Real LAI was measured destructively by removing all the vegetation from the area. Then, the projected shadows in the image were detected using machine learning methods (k-means and random forest) and analysed at pixel level using a customised R code. A strong linear relationship (R² = 0.76, RMSE = 0.160 m² m-2 and MAE = 0.139 m² m-2) was found between the shaded area and the LAI per vine. This is a quick and simple method, which is non-destructive and gives accurate results; moreover, flights can be scheduled during other periods of the day than solar noon, such as in the morning or afternoon, thus enabling pilots to extend their working day. Therefore, it may be a viable option for determining LAI in vineyards trained on Vertical Shoot Positioned (VSP) systems.

Integrating Artificial Intelligence (AI) and Extended Reality (XR) technologies into agriculture presents a transformative opportunity to modernize education and sustainable food production. Traditional agriculture training remains resource-intensive, time-consuming, and geographically restrictive, limiting scalability. This study explores an AI-driven Digital Twin (DT) system embedded within a gamified XR environment designed to enhance decision-making, resource management, and practical training in viticulture as well as woody crop management. A survey among stakeholders in the viticultural sector revealed that participants are increasingly open to adopting Virtual Reality (VR) combined with AI-enhanced technologies, signaling a readiness for digital learning transformation in the field. The survey revealed a 4.48/7 willingness to adopt XR-based training, a 4.85/7 interest in digital solutions for precision agriculture, and a moderate climate change concern of 4.16/7, indicating a strong readiness for digital learning transformation. Our findings confirm that combining AI-powered virtual educators with DT simulations provides interactive, real-time feedback, allowing users to experiment with vineyard management strategies in a risk-free setting. Unlike previous studies focusing on crop monitoring or AI-based decision support, this study examines the potential of combining Digital Twins (DTs) with AI-driven personal assistants to improve decision-making, resource management, and overall productivity in agriculture. Proof-of-concept implementations in Unity and Oculus Quest 3 demonstrate how AI-driven NPC educators can personalize training, simulate climate adaptation strategies, and enhance stakeholder engagement. The research employs a design-oriented approach, integrating feedback from industry experts and end-users to refine the educational and practical applications of DTs in agriculture. Furthermore, this study highlights proof-of-concept implementations using the Unity cross game engine platform, showcasing virtual environments where students can interact with AI-powered educators in simulated vineyard settings. Digital innovations support students and farmers in enhancing crop yields and play an important role in educating the next generation of digital farmers.