In-season N management of irrigated corn requires frequent acquisition of plant N estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study compared ground-based Exotech and QuickBird satellite multispectral data using the normalized GNDVI to produce N status maps of a study site on three days during the corn vegetative growth period. Scale factors to represent N sufficient and N deficient corn were determined for both systems from relationships between the normalized GNDVI and the NSI. A third classification was required for this study to classify areas that exhibited leaf chlorosis that was not caused by N deficiency, i.e., not N related. N status maps generated from normalized GNDVI values showed similar patterns between the two systems for the three corn growth stages (V10, V12, and V15) investigated. However, the extent of the pattern varied between systems. On 2 July (V10), six of six sample sites were correctly classified using leaf N content to indicate plant N status. On the other two days (7 July and 15 July), four of six sites were correctly classified. Two not N related areas were classified as N deficient when leaf N content was adequate.
Development of a machine vision device to automatically identify different weed species within a field is needed to design a successful spatially variable herbicide applicator. This study was conducted to develop a computer vision algorithm that can successfully identify a broadleaf weed (velvetleaf, Abutilon theophrasti), a grassy weed (wild proso millet, Panicum miliacem), and corn (Zea mays, L.). Digital images were collected in laboratory and field conditions for all three plant species. Image analysis techniques were used to analyze the possibility of using a combination of size and shape features to produce a classification scheme. Two separate approaches were used to classify the velvetleaf from the wild proso millet and corn, and the wild proso millet from the corn. The first and second invariant central moment of inertia measurements along with plant perimeter were used to separate the velvetleaf from the monocot species. Due to the similar shapes of wild proso millet and corn, we were unable to classify the two species by only using size and shape features. Consequently, a two step approach was utilized. This involved using projected perimeter to determine the age (number of days after emergence) of the plant. By knowing the possible age of the plant, the wild proso millet and corn were classified using a combination of length and circularity. Future research will involve the evaluation of several other image features to determine the best classification scheme. Further data will also be collected from a library of laboratory and field images in order to increase the confidence interval of the classification scheme.