The objective of this research was to determine the potential use of commercially available multispectral images to detect weeds at low densities during the critical period of weed control. Common lambsquarters seedlings were transplanted into plots of glyphosate-resistant corn at 0, 1, 2, and 4 plants/m2 at two sites, Agronomy Center for Research and Extension (ACRE) and Meig's Horticultural Research Farm at the Throckmorton–Purdue Agricultural Center (TPAC), in Indiana. Aerial multispectral images (12 to 16 cm pixel resolution) were taken 18 and 32 days after planting (DAP) at ACRE and 19 and 32 DAP at TPAC. Corn and common lambsquarters could not be reliably detected and differentiated at either site when weeds were 9 cm or less in height. However, economic threshold densities (2 and 4 plants/m2) of common lambsquarters could be distinguished from weed-free plots at TPAC when weeds were 17 cm in height. At this height, common lambsquarters plants were beyond the optimal height for glyphosate application, but could still be readily controlled with higher rates. Results from this study indicate that commercially available multispectral aerial imagery at current spatial resolutions does not provide consistently reliable data for detection of early season weeds in glyphosate-resistant corn cropping systems. Additional refinement in sensor spatial and spectral resolution is necessary to increase our ability to successfully detect early season weed infestations.

The feasibility of visual detection of weeds for map-based patch spraying systems needs to be assessed for use in large-scale cropping systems. The main objective of this research was to evaluate the reliability and profitability of using maps of Johnsongrass patches constructed at harvest to predict spatial distribution of weeds during the next cropping season. Johnsongrass patches visually were assessed from the cabin of a combine harvester in three corn fields and were compared with maps obtained in the subsequent year prior to postemergence herbicide application. There was a good correlation (71% on average) between the position of Johnsongrass patches on the two maps (fall vs. spring). The highest correlation (82%) was obtained with relatively large infestations, whereas the lowest (58%) was obtained when the infested area was smaller. Although the relative positions of the patches remained almost unchanged from 1 yr to the next, the infested area increased in all fields during the 4-yr experimental period. According to our estimates, using a strategy based on spraying full rates of herbicides to patches recorded in the map generated in the previous fall resulted in higher net returns than spraying the whole field, either at full or half rate. This site-specific strategy resulted in an average 65% reduction in the volume of herbicide applied to control this weed.

The objective of this research was to assess the accuracy of remote sensing for detecting weed species in soybean based on two primary criteria: the presence or absence of weeds and the identification of individual weed species. Treatments included weeds (giant foxtail and velvetleaf) grown in monoculture or interseeded with soybean, bare ground, and weed-free soybean. Aerial multispectral digital images were collected at or near soybean canopy closure from two field sites in 2001. Weedy pixels (1.3 m2) were separated from weed-free soybean and bare ground with no more than 11% error, depending on the site. However, the classification of weed species varied between sites. At one site, velvetleaf and giant foxtail were classified with no more than 17% error, when monoculture and interseeded plots were combined. However, classification errors were as high as 39% for velvetleaf and 17% for giant foxtail at the other site. Our results support the idea that remote sensing has potential for weed detection in soybean, particularly when weed management systems do not require differentiation among weed species. Additional research is needed to characterize the effect of weed density or cover and crop–weed phenology on classification accuracies.