Spots available at the WebGro Training Workshops

The genetic yield potential of soybeans in the Midwestern United States is estimated to be approximately 100 bushels per acre, based on results from small-plot studies. However, field and statewide average yields are much lower. Soybean yield is the result of complex interactions between genetics, management, environment, fertility, and stresses. Water stress is often viewed as the biggest underlying factor resulting in yield loss. However, other factors such as soybean cyst nematodes, Rhizoctonia root rot, and hail damage can also cause significant injury to soybean yields

Probabilistic Neural Networks for Segmentation of Features in Corn Kernel Images

A method is presented for clustering of pixel color information to segment features within corn kernel images. Features for blue–eye mold, germ damage, sound germ, shadow in sound germ, hard starch, and soft starch were identified by red, green, and blue (RGB) pixel value inputs to a probabilistic neural network. A data grouping method to obtain an exemplar set for adjustment of the Probabilistic Neural Network (PNN) weights and optimization of a universal smoothing factor is described. Of the 14,427 available exemplars (RGB pixel values sampled from previously collected images), 778 were used for adjustment of the network weights, 737 were used for optimization of the PNN smoothing parameter, and 12,912 were reserved for network validation. Based on a universal PNN smoothing factor of 0.05, the network was able to provide an overall pixel classification accuracy of 86% on calibration data and 75% on unseen data. Much of the misclassification was due to overlap of pixel values among classes. When an additional network layer was added to combine similar classes (blue–eye mold and germ damage, sound germ and shadow in sound germ, and hard and soft starch), network results were significantly enhanced so that accuracy on validation data was 94.7%. Image quality was shown to be important to the success of this algorithm as lighting and camera depth of field effects caused artifacts in the segmented images

Methodology for the use of DSSAT models for precision agriculture decision support

A prototype decision support system (DSS) called Apollo was developed to assist researchers in using the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth models to analyze precision farming datasets. Because the DSSAT models are written to simulate crop growth and development within a homogenous unit of land, the Apollo DSS has specialized functions to manage running the DSSAT models to simulate and analyze spatially variable land and management. The DSS has modules that allow the user to build model input files for spatial simulations across predefined management zones, calibrate the models to simulate historic spatial yield variability, validate the models for seasons not used for calibration, and estimate the crop response and environmental impacts of nitrogen, plant population, cultivar, and irrigation prescriptions. This paper details the functionality of Apollo, and presents the results of an example application

Evaluation of interactions within a shelterbelt agroecosystem

Yield data for corn (eight years) and soybeans (six years) were collected and analyzed to determine the impacts of a hybrid poplar shelterbelt on crop production on a central Iowa farm

Using Aerial Hyperspectral Remote Sensing Imagery to Estimate Corn Plant Stand Density

Since corn plant stand density is important for optimizing crop yield, several researchers have recently developed ground-based systems for automatic measurement of this crop growth parameter. Our objective was to use data from such a system to assess the potential for estimation of corn plant stand density using remote sensing images. Aerial hyperspectral remote sensing imagery was collected on three dates over three plots of corn in central Iowa during the 2004 growing season. The imagery had a spatial resolution of 1 m and a spectral resolution of 3 nm between 498 nm and 855 nm. A machine vision system for early-season measurement of corn plant stand density was also used to map every row of corn within the three plots, and a complete inventory of corn plants was generated as a rich ground reference dataset. A principal component regression analysis was used to assess relationships between plant stand density measurements and principal components of hyperspectral reflectance for each plot, on each image collection date, and at three different spatial resolutions (2, 6, and 10 m). The maximum R2 for regressions was 0.79. Estimates of corn plant stand density were best when using imagery collected at the later vegetative and early reproductive corn growth stages. Quantization effects due to row width complicated corn plant stand density estimates at 2 m spatial resolution, and better estimations were typically seen at resolutions of 6 m and 10 m. Among the different cases of plot, image date, and spatial resolution, the principal components of reflectance most highly correlated with plant stand density were able to be classified into four distinct types, denoted as types A, B, C, and D. Type A principal components contrasted all available visible red wavelengths with all available near-infrared wavelengths. Type B principal components contrasted green wavelengths (531 to 552 nm) plus shorter wave near-infrared (759 nm) with red wavelengths (675 to 693 nm) plus longer wave near-infrared (852 nm). Type C principal components summed green wavelengths (528 to 546 nm) and near-infrared wavelengths (717 to 855 nm). Type D principal components contrasted blue/green wavelengths (498 to 507 nm) with the red edge (717 nm). Remote sensing can be best used to estimate corn plant stand density at mid-season as long as plant stand variability exists and variability due to other factors is minimal

Using Aerial Hyperspectral Remote Sensing Imagery to Estimate Corn Plant Stand Density

Aerial hyperspectral remote sensing imagery was collected on three dates over three plots of corn. The imagery had a spatial resolution of 1 m and a spectral resolution of 3 nm between 471 nm and 828 nm. A machine vision corn plant population sensing system was also used to map every row of corn within the three plots, and a complete inventory of corn plants was generated as a rich ground reference dataset for remote sensing image analysis. A multiple linear regression analysis was performed to estimate corn plant stand density using reflectance in combinations of three wavebands, and R 2 s of up to 0.82 were found. Estimates of corn plant stand density were best when using imagery collected at the later vegetative growth stage. Quantization effects due to row width complicated corn plant stand density estimates at 2 m spatial resolution, and better estimations were typically seen at resolutions of 6 m and 10 m. For the best-case scenarios, the first predictor variable in the regression model typically fell in the blue reflectance region (473 to 492 nm). The second predictor variable was typically in the longer green and shorter red wavelengths (584 to 635 nm), and reflectance for the third predictor variable was typically at the red edge (729 nm) or in the near-infrared region. Because results for the second and third predictor variables tended to straddle between important regions of typical vegetative reflectance spectra, it is expected that multiple linear regressions using a greater number of bands would improve the distinction between important spectral ranges for estimating corn plant stand density

Soybean composition variance in fields

Much attention has been given to determining the causes of soybean yield variability across fields. However, little attention has been given to whether seed composition may contribute to the variability. In 1998, researchers at Iowa State University measured soybean protein and oil variability across a 50-acre field in central Iowa. The field contained a single variety, and approximately 10 soybean plants were collected from 50 points uniformly distributed across the field. The seeds were sampled for oil and protein content. Protein ranged from 34.4 to 37.9 percent, whereas oil ranged from 18.1 to 19.8 percent

Using Crop Growth Models for Soybean and Corn Management

Corn and soybeans are the two primary row-crops grown in Iowa. In 1993, 11 million acres of corn was planted, with an average yield of 149 bu/ ac. In the same year, 8.1 million acres of soybean was planted, with an average yield of 44 bu/ ac (Iowa Crop Report, 1994). Growers are under increasing pressure to produce crops with minimum effects to the environment. This must be done without compromising the economic sustainability of the farm. It is becoming more difficult to determine the optimum crop production strategy because the system constraints continue to increase. In the future, farmers will rely more heavily upon the use of computers to aid in decision making to determine the optimum crop production strategy including variety selection, planting date, irrigation, pesticide applications, fertilizer strategies, and manure applications

Dynamical age of solar wind turbulence in the outer heliosphere

In an evolving turbulent medium, a natural timescale can be defined in terms of the energy decay time. The time evolution may be complicated by other effects such as energy supply due to driving, and spatial inhomogeneity. In the solar wind the turbulence appears not to be simply engaging in free decay, but rather the energy level observed at a particular position in the heliosphere is affected by expansion, “mixing,” and driving by stream shear. Here we discuss a new approach for estimating the “age” of solar wind turbulence as a function of heliocentric distance, using the local turbulent decay rate as the natural clock, but taking into account expansion and driving effects. The simplified formalism presented here is appropriate to low cross helicity (non-Alfvénic) turbulence in the outer heliosphere especially at low helio-latitudes. We employ Voyager data to illustrate our method, which improves upon the familiar estimates in terms of local eddy turnover times