Abstract
Abstract. A normalized difference vegetation index (NDVI) sensor can be used for nondestructively assessing the nitrogen (N) status of crops. When deployed as a mobile ground-based sensor for variable-rate cropping applications, its readings can be influenced by the irregular or sparse canopy coverages of the ground surface. The soil and ground stubble backgrounds that are periodically exposed within the sensor’s view will generate erroneous readings. The objective of this research was to develop an algorithm that improves an NDVI-based instrumentation system’s ability to discriminate between cotton plant biomass and the ground surface, thereby providing a more accurate N status for the crop. The research focused on N concentration assessment as a measure of N status; hence, eliminating the exposed soil background became essential. Algorithms were developed to correct the errors resulting from irregular cotton populations. A statistical analysis of field data taken using manipulated crop populations highlighted two effective algorithms.
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