# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 5 Future Perspective  
RS technology is anticipated to maximize the potential of the agricultural sector to feed future generations. Integration of modern science is greatly needed. A few possible future studies, with regard to the 5 th industrial revolution, are hinted at below (Figure 8). * Satellites, RPAs, and sensors seek a vast amount of data in a raw form, which requires tiresome pre-processing, processing, and data evaluation procedures. This is where big data comes in play and ML can be applied to handle this difficult task as an effective tool for managing and processing huge data sets, as has also been previously reported [42]. Although traditional ML methods (i.e., artificial neural networks, random forests, and support vector machines) provide the characteristics of a subject under study, these prove to be subjective and cumbersome processes when dealing with the problems of a complex domain, e.g., yield estimation of a crop or highly dimensional datasets [42]. Therefore, selection of key features is very important to further train models for a higher accuracy. Furthermore, such machine and deep learning models for various crop characteristics (i.e., stress, disease, ripeness, etc.) are urgently required for almost all of crops. For example, postharvest grading processes can be easily regulated by the implementation of quality-based ML fruit classification models. Similarly, an image offers a great deal of information, such as chlorophyll, plant canopy, size, color, fruit number, etc., which can be analyzed using various ML networks with the potential of minimizing input costs and making the crop yield more profitable. * Incorporation of GIS-enabled biosensors into plants cannot only help to reveal plant molecular processes, e.g., nutrients assimilation, production of antioxidants, etc., but also the location of pathogenic attack, drought stress, and other valuable growth parameters. * Novel low-cost sensors are highly in demand and can certainly complement the efficiency of RS technology. For example, novel sensors detecting ethylene gas levels, which is an indicator of fruit ripening [146], can help farmers to make essential management decisions before harvest. Similarly, durable ground-based sensors can be incorporated with cellular and WST technology for real time information gathering, as well as programing for respective measures. Further novel applications of sensor, as well as new sensors measuring a variety of parameters, can certainly take RS technology to another level. * An integrated and complementing system for satellites, automatically guided land vehicles (AGVs), and RPA imagery could be quite beneficial to overcome physical hurdles in data acquisition and improving the efficiency and accuracy of these technologies. Novel algorithms can also fill in this gap and, therefore, research should be focused on this domain. * Given the tremendous potential of LiDAR, it could be used to extract architectural information of crops, as well as of forests. Such morphological information can pave the way to unveil growth processes, phenotypic explorations, structural patterns, establishing biomass-yield relationships, etc. Using AI, this information could be amplified for various other plant species and even to simulate plants developmental behavior in space. * Freely available satellite imagery with coarse temporal and spatial resolutions have not been exploited for scale-appropriate precision agriculture applications [147]. These free resources can be manipulated in a number of ways to extract desired information; for example, historical and current field data can be a game changer for estimatingsoil fertility status, which immensely influences crop yield. Similarly, distanced and under-nourished plants in a field patch indicate inadequate growth conditions, e.g., salinity stress, drought stress, shallow patch, etc. This is quite valuable information for assuring precision.