To reach the goals laid out by the U.S. Government for displacing fossil fuels with biofuels, high-biomass sorghum is well-suited to achieving this goal because it requires less water per unit dry biomass and can produce very high biomass yields. In order to make biofuels economically competitive with fossil fuels it is essential to maximize production efficiency throughout the system. The goal of this study was to use remote sensing technologies to optimize the yield and harvest logistics of high-biomass sorghum with respect to production costs based on spatial variability within and among fields. Specific objectives were to compare yield to aerial multispectral imagery and develop predictive relationships. A 19.2-ha high-biomass sorghum field was selected as a study site and aerial multispectral images were acquired with a four-camera imaging system on July 17, 2009. Sorghum plant samples were collected at predetermined geographic coordinates to determine biomass yield. Aerial images were processed to find relationships between image reflectance and yield of the biomass sorghum. Results showed that sorghum biomass yield in early August was closely related (R2 = 0.76) to spectral reflectance. However, in the late season the correlations between the biomass yield and spectral reflectance were not as positive as in the early season. The eventual outcome of this work could lead to predicted-yield maps based on remotely sensed images, which could be used in developing field management practices to optimize yield and harvest logistics.
External-interface computer code has been written for the cotton growth model, Gossym, such that it can be operated from the ArcView GIS interface. Remote-sensing data have been incorporated as an estimator of plant height for feedback to the model. Management zones are delineated automatically based on available spatially variable data, and Gossym subsequently calculates outputs for each management zone, and uses current remote-sensing data in the calculations. This advanced Gossym model system also gathers local weather data automatically over the internet. Development and use of the model system are also described. Site-specific field data and remotely sensed images have been collected extensively on two agricultural fields in Mississippi from 1998 through 2003. Evaluation of yield prediction based on the Gossym model system indicated that current remote-sensing data can enhance accuracy. Details of experimentation and data analysis are presented.
A plant health sensing system was developed for determining nitrogen status in plants. The system consists of a
multi-spectral optical sensor and a data-acquisition and processing unit. The optical sensor’s light source provides
modulated panchromatic illumination of a plant canopy with light-emitting diodes, and the sensor measures spectral
reflectance through optical filters that partition the energy into blue, green, red, and near-infrared wavebands.
Spectral reflectance of plants is detected in situ, at the four wavebands, in real time. The data-acquisition and
processing unit is based on a single board computer that collects data from the multi-spectral sensor and spatial
information from a global positioning system receiver. Spectral reflectance at the selected wavebands is analyzed,
with algorithms developed during preliminary work, to determine nitrogen status in plants. The plant health sensing
system has been tested primarily in the laboratory and field so far, and promising results have been obtained. This
article describes the development, theory of operation, and test results of the plant health sensing system.
If farmers could predict yield on a spatially variable basis, they could better understand risks and returns in applying costly inputs such as fertilizers, etc. To this end, several remotely sensed images of a cotton field were collected during the 2002 growing season, along with daily high and low temperatures. Image data were converted to normalized-difference vegetation index (NDVI), and temperature data were used to normalize NDVI changes over periods between image collections. Remote-sensing and weather data were overlaid in a geographic information system (GIS) with data from the field: topography, soil texture, and historical cotton yield. All these data were used to develop relationships with yield data collected at the end of the 2002 season. Stepwise regression was conducted at grid-cell sizes from 10 m square (100 m2) to 100 m square (10,000 m2) in 10-m increments. Relationships at each cell size were calculated with data available at the beginning of the season, at the first image date, at the second image date, and so on. Stepwise linear regression was used to select variables at each date that would constitute an appropriate model to predict yield. Results indicated that, at most dates, model accuracy was highest at the 100-m cell size. Remotely sensed data combined with weather data contributed much information to the models, particularly with data collected within 2.5 months of planting. The most appropriate model had an R2 value of 0.63, and its average prediction error was about 0.5 bale/ha (0.2 bale/ac, or roughly 100 lb/ac).