The feasibility of an automated crop assessment method based upon a photo-diode array sensor and a micro-computer analyzer is examined. This crop assessment method exploits the visual damage patterns, from biological infestations, found on the leaves of the crop. Current crop assessment techniques take a leaf sample along a field diagonal for laboratory analysis, after a problem is determined. This method, because of its reliance upon the farmer visually detecting crop damage, gives pathogens a chance to become established. Once established, the extensive use of expensive wide area fungicides, herbicides, and pesticides are required to eradicate the pathogen. We will use the fungus Verticillium Albo-atrum because of its well-documented effects on leaves and its ease-ofhandling. The program objectives are: (1) to determine the ability of an automated sensor to detect Verticillium Albo-atrum, (2) to determine the automated sensors' resolution requirements, and (3) to determine the sensors' performance in relation to human beings. The implications of this research are far-reaching in terms of lowering farm production costs, increasing farm production rates, decreasing the level of pesticide, herbicide, and fungicide use, and decreasing the amount of ground-water pollution.The potential commercial application as described by the awardee: There is a great need for an autonomous crop assessment method, both to increase farm productivity and to decrease farm costs. The method might also be used in a laboratory after isolating and categorizing microorganisms,
