Many upland pastures and forest meadows in the western United States contain significant infestations of yellow hawkweed and oxeye daisy. Documentation of infestations is necessary in order to plan and assess control tactics. Previous work with an airborne charge coupled device (CCD) with spectral filters indicated that flowering yellow hawkweed with at least 30% cover was detectable at 1 m resolution. A single image of a large area may not capture all plants in the flowering phase and multiple images are costly. The objective of this paper was to assess the accuracy of images recorded at different phenological stages. We compared three methods of classification: unsupervised classification of a three principal component analysis image, supervised classification of a three principal component analysis image, and supervised classification of a composited image consisting of four bands and normalized difference near infrared (NIR)/red band. Regardless of the classification method, images of yellow hawkweed and oxeye daisy in full bloom had lower classification error than at early bloom or post bloom. The percent error for yellow hawkweed classification was about twice as high at post bloom as at full bloom, but varied slightly depending on the method of classification and cover class. The ability to detect discrete colonies of yellow hawkweed was not affected by phenological stage, but the ability to measure the area of each cluster differed among stages. Less than one-third fo the pixels classified as yellow hawkweed or oxeye daisy in the early bloom image remained in the same class in the full bloom image. About half the pixels in the full bloom image remained in the 90 to 100% cover class at the post bloom image. Seasonal growth of the grasses masked some yellow hawkweed and oxeye daisy plants, and accounted for differences in classification among phenological stages.

Field experiments were conducted in 1999 at Stoneville, MS, to determine the potential of multispectral imagery for late-season discrimination of weed-infested and weed-free soybean. Plant canopy composition for soybean and weeds was estimated after soybean or weed canopy closure. Weed canopy estimates ranged from 30 to 36% for all weed-infested soybean plots, and weeds present were browntop millet, barnyardgrass, and large crabgrass. In each experiment, data were collected for the green, red, and near-infrared (NIR) spectrums four times after canopy closure. The red and NIR bands were used to develop a normalized difference vegetation index (NDVI) for each plot, and all spectral bands and NDVI were used as classification features to discriminate between weed-infested and weed-free soybean. Spectral response for all bands and NDVI were often higher in weed-infested soybean than in weed-free soybean. Weed infestations were discriminated from weed-free soybean with at least 90% accuracy. Discriminant analysis models formed from one image were 78 to 90% accurate in discriminating weed infestations for other images obtained from the same and other experiments. Multispectral imagery has the potential for discriminating late-season weed infestations across a range of crop growth stages by using discriminant models developed from other imagery data sets.

This study assessed the tolerance of ‘Midlawn’ (Cynodon dactylon × C. transvaalensis) and ‘OKS 91-11’ (C. dactylon) bermudagrass to commonly used postemergence herbicides and compared visual assessment with vehicle-mounted optical sensing (V-MOS) for evaluating herbicide phytotoxicity. Two postemergence herbicides were applied to mature stands of Midlawn and OKS 91-11 at two and four times label rates, and seven postemergence herbicides were applied at standard and two times label rates. Visual evaluation and spectral assessments were made for turf color 2, 7, 14, and 21 d after treatment (DAT). Triclopyr and triclopyr plus clopyralid at 2× and 4× label rates caused significant damage on OKS 91-11 and Midlawn bermudagrass in both July and September experiments. MSMA at 2× rate and MSMA + metribuzin at 1× and 2× rate caused up to 73% color reductions that disappeared within 21 DAT in both cultivars. During July, 2,4-D plus mecoprop plus dicamba at the 2× rate caused at least 18% injury to Midlawn bermudagrass for 21 d. Metribuzin was safe at the 1× rate but caused significant injury for up to 7 d at the 2× rate. Imazaquin and halosulfuron-methyl each caused significant damage on one rating date. Pronamide caused no change in color regardless of rate or time of application. OKS 91-11 tolerated 2× rates of 2,4-D plus mecoprop plus dicamba better than Midlawn, but cultivar responses to other herbicide treatments were similar. V-MOS was effective for measuring green color reduction on bermudagrass turf. V-MOS and visual evaluation were linearly related (P < 0.01) at a strength of r = 0.58. Statistical results obtained using visual rating and V-MOS were the same in 86% of all cases.

Weed-detecting reflectance sensors were modified to allow selective interrogation of the near infrared–red ratio to estimate differences in plant biomass. Sampling was programmed to correspond to the forward movement of the field of view of the sensors. There was a linear relationship (r 2 > 0.80) between actual biomass and crop canopy analyzer (CCA) values up to 2,000 kg/ha for winter wheat sequentially thinned to create different amounts of biomass and up to 1,000 kg/ha for spring wheat sampled at different stages of development. At higher amounts of biomass the sensors underestimated the actual biomass. A linear relationship (r 2 = 0.73) was obtained with the CCA for the biomass of 76 chickpea cultivars at 500 growing degree days (GDD500). The reflectance sensors were used to determine differences in the herbicide response of soybean cultivars sprayed with increasing rates of herbicides. The CCA data resulted in better dose–response relationships than did biomass data for bromoxynil at 0.8 kg ai/ha and glyphosate at 1.35 kg ai/ha. There was no phytotoxicity to soybean with imazethapyr at 1.44 kg ai/ha. The method offers a quick and nondestructive means to measure differences in early-season crop growth. It also has potential in selecting crop cultivars with greater seedling vigor, as an indicator of crop nutrient status, in plant disease assessment, in determining crop cultivar responses to increasing herbicide dose rates, in weed mapping, and in studying temporal changes in crop or weed biomass.