It is crucial to improve the efficiency of plant breeding and crop yield in order to fulfill rising food demands. In plant phenotyping study, the capability to correlate morphological traits of plants plays an important role. However, measuring the plant phenotypes manually is prone to human errors, labor intensive and time-consuming. Hence, it is important to develop techniques for measurement of plant phenotypic data accurately and rapidly. The objective of this study was to find out the feasibility of point cloud data of 3D LiDAR including RGB image for plant phenotyping. The obtained results were then verified through the manually acquired data for sorghum and soybean plant samples. The overall results showed remarkable correlation between point cloud data and manually acquired data for plant phenotyping. This correlation indicates that the 3D Lidar imaging system have potential to measure phenotypes of crops in rapid and accurate way.
Information about NPK content in farm soil is important for further application of the necessary dosage of fertilizer. At present, laboratory-based chemical analysis is being used to assess the status of soil nutrients, but these methods are complex, tedious, costly, and poor in in-situ condition. While, for precise farming, in-situ assessment of soil fertility status is of prime importance. Hyper-spectral remote sensing bears the potential of a detailed investigation of soil through analysis of its spectral absorption features. Therefore, this study aims to estimate quantitatively the content of fertilizer in a sample of soil making use of spectral properties of macro soil nutrients. For addressing the abovementioned issues, this study has used Derivative Analysis for Spectral Unmixing (DASU), approach consisting of spectral unmixing and spectral derivative analysis. The study reveals that the spectral region 993.2nm provides a unique feature. It leads to the development of a model for estimation of NPK fractional abundance in a soil sample. Further, this model has been validated for the good number of soil samples in a laboratory. Thus, the key contribution of this study is to underpin that; the hyper-spectral remote sensing may be used in-situ to estimate soil fertility of farm soil. Although, the fractional abundances of individual components of NPK may be considered as future scope of work.
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