**5. Conclusions and Recommendations**

Hyperspectral imaging has great potential for applications in agriculture, particularly precision agriculture, owing to ample spectral information sensitive to different plant and soil biophysical and biochemical properties. Multiple platforms, including satellites, airplanes, UAVs, and close-range platforms, have become more widely available in recent years for collecting hyperspectral images with different spatial, temporal, and spectral resolutions. These platforms also have different strengths and limitations in terms of spatial coverage, flight endurance, flexibility, operational complexity, and cost. These factors need to be considered when choosing imaging platform(s) for specific research purposes. Further technological developments are also needed to overcome some of the limitations, such as the short battery endurance in UAV operations and high cost of hyperspectral sensors.

Different analytical methods, such as linear regression, advanced regression, machine learning, deep learning, and RTM, have been explored in previous studies for analyzing the tremendous amount of information in hyperspectral images for investigating different agricultural features. Previous studies have mainly used the regression approach, while more physically based methods, such as RTM, have been less explored. Deep learning and effective big-data analytics are powerful tools for recognizing patterns in remote sensing data. Together with hyperspectral imagery, deep learning models have high potential to support the monitoring of a wide range of agricultural features. Different analytical methods have different advantages and disadvantages, and thus it is critical to compare these methods for specific research (e.g., requirements of accuracy and computing efficiency) and choose an optimal approach. In addition, image spectral information has been commonly used as variables for prediction or classification tasks, while other information, such as texture, has been less explored. Further, some other sources of data, such as weather, irrigation records, and historical yield information, can also be used in some of the analytical methods (e.g., machine learning and deep learning) for better monitoring of crop features. More research in these fields is also warranted.

Hyperspectral imaging has been successfully applied in a wide range of agricultural applications, including estimating crop biochemical and biophysical properties; evaluating crop nutrient and stress status; classifying or detecting crop types, weeds, and diseases; and investigating soil characteristics. Previous studies have focused on discussing one or two of the many factors impacting crop growth performance and productivity, and thus cannot evaluate crop status and growth-limiting factors comprehensively. It is important to integrate these factors to achieve a better understanding of their inter-relationships for optimal crop production and environmental protection. Besides, previous studies using hyperspectral imaging have mainly targeted investigating crop growth, aiming to improve crop yield, while less research has focused on understanding the ecosystem side of crop production (e.g., ecosystem services and biodiversity). Further research in these areas is warranted.

**Author Contributions:** Conceptualization, J.S., J.L., Y.H., B.L. and P.D.D.; methodology, B.L., P.D.D. and Y.H.; investigation, B.L.; writing—original draft preparation, B.L.; writing—review and editing, P.D.D., J.S., J.L. and Y.H.; project administration, J.S., J.L. and Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Discovery Grant RGPIN-386183 to Professor Yuhong He.

**Conflicts of Interest:** The authors declare no conflict of interest.

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