Kenneth Von Bargen

Plant species identification, size, and enumeration using machine vision techniques on near-binary images

Shape parameters such as aspect, roundness, and the ratio of thickness to perimeter were used to describe plant shape and are different according to the species that they represent. Color slide images of several species of plants were digitized for computer analysis. Three optical methods were tested to separate target plants from the soil and residue background. The separation method that provided the best contrast was the normalized difference index. Subtracting the blue or the red raster from the green raster also provided good separation on soils with little residue. Once the plant image had been isolated from the background, leaf edges were automatically traced using a commercial software package. Analysis of the shape of the plant outline was then performed, resulting in the plant shape parameters. Grasses and broadleaf plants had similar values for each shape parameter during the first ten days after emergence. After this period, differences occurred between grasses and broadleaf plants. The parameter that best discriminated grasses from broadleaf plants was the aspect (major axis length/minor axis length). However, when a grass sends out more than one shoot radially from the stem, the aspect will be similar to broadleaf plants. This study contributes to the design of a system that can determine weed populations and identify plant species without the use of human intervention.

Optical reflectance sensor for detecting plants

The reflectance ratio meter, an optical device for detecting weeds by measuring the ratio of reflected red and near-infrared light, is described. Experiments were conducted to evaluate the accuracy of the sensor in detecting weeds in the inter-row of growing soybeans and to characterize its sensitivity. Several methods for interpreting the signal were evaluated. The reflectance ratio meter is shown to have potential for estimating local weed populations.

Red/near-infrared reflectance sensor system for detecting plants

Growing plants, soil types, and surfaces and residues on a soil surface have distinct natural light reflectances. These reflectance characteristics have been determined using current spectroradiometry technology. Detection of plants is possible based upon the distinct reflectance characteristics of plants, soil, and residues. An optical plant reflectance sensor was developed which utilizes a pair of red and near infrared sensitive photodetectors to measure the radiancy from the plant and soil. Another pair of sensors measures radiancy from a highly radiant reference surface to accommodate varying intensities of the natural light. The ratio of the target and reference radiancies is the target reflectance. Optical filters were used to select the spectral bandwidth sensitivities for the red and NIR photodetectors. The reflectance values were digitized for incorporation into a normalized difference index in order to provide a stronger indication that a live plant is present within the field of view of the sensor. This sensor system was combined with a microcontroller for activating a solenoid controlled spray nozzle on a single unit prototype spot agricultural sprayer.

Performance Comparisons Between Duals and Singles on the Rear Axle of a Front Wheel Assist Tractor

ABSTRACT DYNAMIC traction ratio and tractive efficiency of a tractor, equipped with dual or single tires on the rear drive axle, and a mechanical front-wheel drive assist, were evaluated on four different surfaces; con-crete, disked sandy soil, disked wheat stubble and plow-ed wheat stubble. When operating on concrete, a higher dynamic traction ratio was found for the duals than singles. However, when the tractor was operated in the field on the different soil surfaces, no discernible perfor-mance difference in the dynamic traction ratio or tractive efficiency could be detected between single or dual tires on the rear drive axle.

Equipment for milkweed floss-fiber recovery

Abstract The floss-fiber of milkweed ( Asclepias syriaca and Asclepias speciosa ) is being commercialized for use as a loose fill insulation. Successful production of milkweed floss-fiber requires a reliable and economic system for harvesting, handling, drying and processing the floss-fiber. A system consisting of a pod harvester, a pod conditioner, two drying stages and a spike tooth cylinder processor is discussed. Most of the equipment units are commonly available, but require modification to meet the special operational requirements of milkweed production. This prototype system has seen limited use and quantification of performance and system refinement await cultural developments in order to produce milkweed on a commercial scale. The harvest rate is 0.4–0.5 ha/h and 90% of the pods were harvested. The pod conditioner opened 95% of the pod sutures. The drying process removed 50% of the moisture in the pod within 4 h during the first drying stage, and further dried the pods to 10% moisture content in 48–60 h in the second stage of drying. The floss processor recovered 60% of the floss with the centrifugal separator prior to spike-tooth cylinder processing.

Optical plant sensor field-of-view determination

Optical plant sensors constructed from red and near-infrared (NIR) filtered photodetector pairs were used in conjunction with the normalized difference index (NDI) to detect plants. Plants must occupy a minimum of 8% of the photodetector pair field-of-view (FOV) to be detected. Thus, knowing the size and location of the FOV is crucial. Since the NDI requires red and NIR reflectance measurements from coincident areas, it is equally important to know the coincident area of a red-NIR detector pair and its location. Reflectance measurements taken every 1 cm from a 60 cm X 60 cm surface can be graphically viewed to determine the size and location of the FOV of a plant sensor. The surface has low reflectance and contains a 20 cm X 20 cm highly reflective checkerboard pattern in the center. From individual FOVs of red-NIR pairs, the coincident area can be found.

Spatial statistical measures of crop temperature variability using infrared thermography in radiant heated greenhouse crops

Crop surface temperature under a radiant heated greenhouse was measured using a portable infrared thermometer. Plants were arranged so that each plant occupied a grid cell of 30 cm X 30 cm (1 ft X 1 ft). Data collected were analyzed for their spatial distribution. Geostatistical software was used to characterize the spatial variability of the plant surface temperature. The shape of the empirical semi-variogram suggested that a spherical model was best fitted to the empirical semi-variogram. This model indicated that the nugget effect was estimated at 1.2, the sill at 3.3 and the range at 1.65 m. This model was used in block kriging to estimate plant surface temperature for unsampled locations.