## 7 Final Remarks and Future Challenges
Other challenges relate to data, particularly the need to standardize procedures for converting images into prescription maps, classification maps, and VIs maps, the latter was used in much of the research examined in this review, as shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Index (VI)** & **Acronym** & **Equation** & **Research** \\ \hline Chlorophyll Vegetation & & & \\ Index [246] & & & \\ \hline Difference Vegetation & & & \\ Index [155] & & & \\ \hline Enhanced Vegetation & & & \\ Index [247] & & & \\ \hline Enhanced Vegetation & & & \\ Index 2 [248] & & & \\ \hline Generalized difference & & & \\ Vegetation Index [249] & & & \\ \hline Green Ratio Vegetation & & & \\ Index [171] & & & \\ \hline Normalized Difference Red & & & \\ Edge Index [250] & & & \\ \hline Green and Red Normalized & & & \\ Difference Vegetation & & & \\ Index [152] & & & \\ \hline Green Normalized Vegetation & & & \\ Index [151] & & & \\ \hline Modified chlorophyll & & & \\ absorption in reflectance & & & \\ index [56] & & & \\ \hline Modified Simple Ratio [251] & MSR & & \\ \hline Modified Soil Adjusted & & & \\ Vegetation Index [54] & & & \\ \hline Modified triangular & & & \\ vegetation index [56] & & & \\ \hline Normalized difference & & & \\ green/red index [154] & & & \\ \hline Normalized Difference & & & \\ Vegetation Index [46] & NDVI & & \\ \hline Optimized Soil Adjusted & & & \\ Vegetation Index [167] & & & \\ \hline Photochemical Reflectance & & & \\ Index (570) [165] & & & \\ \hline Perpendicular Vegetation & & & \\ Index [155] & & & \\ \hline Ratio Vegetation Index [155] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The vegetation indices (VIs) used in remote sensing (RS) researches in olive growing. This leads to using different research methods to achieve the same goal with results that are not always excellent or reliable [239]. The presence of different software for image analysis with different complexity in usage, capacities, and prices constitutes a further uncertainty in the choices of the company or farmer. In this regard, some images can be provided to the farmer pre processed and in a \"form\" that is already usable according to their needs. On the other hand, it is difficult to easily find reliable data, such as time-series images, in the absence of standard procedures for aspects such as radiometric calibration and retrieval of reflectance and temperature data [257]. Despite a large number of studies on RS applications in PA, there is a general lack of established techniques and/or frameworks that are accurate, reproducible, and applicable under a wide variety of climatic, soil, crop, and management conditions. The accuracy of methods using RS platforms data (satellite, airborne, and UAV) depends on a variety of factors, including image resolution (spatial, spectral, and temporal); atmospheric, climatic, and weather conditions; crop and field conditions (e.g., growth stage, land cover); and the analyses technique (e.g., regression-based, machine learning, physically-based modeling). In this regard, a big push in automating remote enterprise data analysis can come from increased interoperability between field sensors that collect real-time data, and proximal and remote sensing sensors. The considerations made so far on RS platforms, such as the need for the standardization of methods of data collection, processing and output production, dissemination and transmission of knowledge, and expertise from the scientific field to companies in the sector and farms, are valid both for the olive sector as for other crops. The resolution of these issues is a necessary step for the modernization of the sector. The olive sector is very complex because, unlike other agricultural realities, it is spread over several continents and different parts of the world with different climatic and environmental conditions that alone are sufficient to generate different needs from the agronomic and technological point of view.