# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 6 Conclusions: Challenges, Opportunities and Future Prospects  
Technology and data fusion play an important role in increasing the efficiency of crop monitoring. Spatiotemporal data fusion is based on combining data of fine spatial and coarse temporal resolution with fine temporal resolution and coarse spatial resolution to achieve the objective of creating fine spatiotemporal resolution data. The fusion of the sensor technologies efficiently classifies the crop type, status, and parameters for site-specific crop monitoring.  
Optical, thermal infrared, multispectral, hyperspectral, LiDAR, and radar sensors are widely adopted for crop N, chlorophyll, LAI, and AGB estimation.  
Multispectral, hyperspectral, and thermal systems are confirmed to be beneficial in crop monitoring, acquiring precise spectral sensitivity measurements in different stages of crop growth and production. Furthermore, LiDAR may be positively used for data fusion of crop parameters. For different sensors, the vis-NIR spectroscopy sensor provides good to excellent crop monitoring accuracy. This also supports the final conclusion of this study that multi-sensor and data fusion approach is a useful method for high crop production and optimizes the quantity and frequency of crop irrigation.  
Crop monitoring by space-borne and aerial vehicles has experienced significant improvement. Different types of sensors were used to achieve this objective.  
The widely adopted and successful fusion in precision agriculture is the fusion of multi-sensor imagery from UAVs, multiresolution imagery from satellites, and the fusion of UAV and satellite imagery data for improved results. The fusion of this type of data improves the estimation of crop monitoring processes. Therefore, for particular tasks such as yield prediction, the fusion of other data sources (e.g., for field sensors and ground vehicles) may further improve crop growth stage estimation performance.  
Space-borne platforms now have the great ability to deliver suitable resolution for crop mapping and monitoring, weather condition assessment, as well as conservation and documentation. Airborne LiDAR provides the purposes of estimation below the vegetation canopy and to conduct field crop analysis better than spectral estimations, or SAR. AirborneUAVs can be used to acquire centimeter-level crop-related data that are mostly site-specific or site scale to develop the detailed crop monitoring and mapping.  
The major constraint of data fusion is the multimodality of data from different types of sensors, systems, noise, poor resolution, or flaws. However, the assessment of these advanced models for crop monitoring is still in the development stages. Future developments, such as of intelligent and autonomous systems, are a major necessity for the expansion of technology and data fusion to provide precise data over the forecasted periods.  
Conceptualization, U.A., S.M. and A.N.; methodology, U.A., S.M.; validation, U.A., S.M. and A.N.; investigation, U.A.; resources, U.A., S.M.; writing--original draft preparation and development, U.A., S.M.; writing--review and editing, U.A., S.M., A.N. and O.H.; visualization, U.A.; supervision, U.A., S.M., A.N. and O.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.