## 1 Introduction  
Given the importance of olive growing, especially in Mediterranean countries, it is crucial that there is a constant process of modernization aimed at both environmental sustainability and the maintenance of high standards of production [1, 2]. Considering the environmental risks caused by climate change with the Mediterranean region as the biggest threat, adopting available tools, best practices, and applying forward-looking policies can adapt agricultural management strategies and processes given the above mentioned changes [3]. The use of remote sensing (RS) allows intervention in a specific and differentiated way in an olive grove, depending on its variability, in managing different agronomic aspects. Remote sensed tree detection is a key factor in on-farm mapping aimed at supporting farmers in planning practices such as pruning, fertilizing, irrigation, phytosanitary treatments, and the estimation of olive productivity [4, 5]. Plant breeding programs have benefited from the possibility of exploiting images from above to collect phenotypic data on plant growth and morphology, geometrical canopy characteristics, and tree vigor. These observable traits are essential in assessing the suitability of selected genotypes for cultivation in olive growing planting systems, such as modern super high-density hedges, requiring adaptability to mechanized pruning and harvesting [6, 7]. One of the crucial issues concerns is the detection of plant diseases by monitoring the symptoms of olive diseases, among which two are particularly harmful as they cause severe problems to olive growing worldwide: Verticillium Wilt and Quick Decline Syndrome. The latter, caused by the now well-known plant pathogenic bacterium _Xylella fastidiosa_ which has proven to cause the death of olive trees, has severe effects on the production, landscape, and cultural heritage of the territory concerned [8]. The contribution of RS in this area is to provide support in monitoring symptoms and identifying diseased trees based on a visual assessment that requires field inspections with a more significant expenditure of resources and time [9]. This review focuses on the role of aerial RS applications in olive growing farm management from the 2000s onwards. The first review dealing with the research of RS applications in olive growing for the preservation of the olive landscape, monitoring of land use, management of irrigation resources, and environmental issues arising from olive oil mill wastes was reported in Messina and Modica [10]. This article, instead, covers purely agronomic matters of interest to olive farms, such as yielding and managing diseases and pests, detection, and counting of olive trees. In addition to these topics, there are other relevant aspects concerning the characterization of the canopy structure of olive trees, particularly interesting for mechanized pruning management and phenotyping. Therefore, the structure of the second part of this review is the following: Section 2 covers research related to methodologies for detecting and counting trees; in Section 3, pruning-related research is treated; Section 4 deals with olive yield forecast; Section 5 concerns phenotyping and monitoring of olive trees' biophysical parameters; the Section 6 focuses on monitoring and management of olive's pests and diseases; finally, Section 7 contains discussions and conclusions on the perspectives and challenges about the future of RS in olive growing. The used sources for reviewed works were the following databases: Scopus (Elsevier) Google Scholar, and Web of Science (Clarivate Analytics). Figure 1 shows the researches found, 72 in all, many of them were published in the last decade, from 2011 onward, while only a tiny fraction, just 12, were published earlier [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Of these, 11, concern tree detection and counting (discussed in Section 2) which is the topic with the highest occurrences, 27.