Crop vegetation anomaly identification from observed patterns found within remote sensing data

摘要

Aerial images and photographs of cropland vegetation and bare soil were collected during the 1997 and 1998 cropping seasons at the Purdue Agronomy Research Center and Davis Purdue Agriculture Center for remote sensing research purposes. During these seasons the weather was such that many field crop anomalies developed and have been identified in corn (Zea mays), soybeans (Glycine max), wheat (Triticum), and miscellaneous hay crops such as alfalfa (Medicago sativa). Through identification of these anomalies and describing specific characteristics associated with each, it is possible to group them according to common causes such as water, nutrition, weeds, insects, disease, and management decisions. A Cropland Anomaly Classification System was developed and has been beneficial in understanding each specific anomaly. Through this understanding the possibility exists to make sound economic management decisions on smaller areas than whole fields (management zones), The more intensive management of fields may reduce some inputs or increase production or both.