In space, where all resources including space and labor are scarce, it is essential that crop production be constantly and efficiently managed and that the health of the crop is determined quickly and accurately. The evaluation of crop health today depends largely on visual observations, judgments, and periodic destructive sampling. Remote sensing techniques, which are beginning to be used to examine crop growth, have difficulties in distinguishing multiple stresses. In order to use recently available sensors having high spectral resolution to distinguish individual stresses, it is necessary to develop algorithms which characterize individual stresses. This project will utilize existing spectral data from controlled experiments to determine the feasibility of developing algorithms for evaluating crop health. With these algorithms, it will be possible to determine the health status of plants in real time. Knowing health status in real time from spectral algorithms makes efficient management possible with the potential of full automation of crop production.
Potential Commercial Applications:Commercial users could include greenhouse and growth chamber manufacturers and operators, environmental resource and consulting firms, investment and economic analysis companies involved with agricultural products, and farming or other agricultural enterprises.STATUS: Project Proceded to Phase II
