Fresh market fruit crops such as apples have not employed precision agriculture tools, partially due to the labor intensive nature of the cropping systems. In this paper we describe new research in the development of precision agriculture tools for tree fruit, including the ability to track spatially variable orchard data before harvest through to the packing plant. Remote sensing is a key component of this system, and remote sensing products are being evaluated for their usefulness in guiding orchard management.
We describe a unique approach to image resolution enhancement, inverse kriging (IK), which takes advantage of the spatial relationship between high- and low-resolution images within an area of overlap. Once established, this mathematical relationship then can be applied across the entire low-resolution image to significantly sharpen the image. The mathematical relationship uses the spatial correlations within the low-resolution image and between the low and high spatial-resolution imagery. Two of the most important requirements of the technique are that the images be co-registered well within the resolution of the larger pixels and that the spatial structure of the training area (where the spatial correlation statistics are computed) is similar to the structure of the remaining image area where it will be applied.
Testing was performed using same-sensor and multi-sensor imagery. We show results that indicate that the method does improve the low spatial-resolution imagery. The selection of a training area spatial structure similar to the area being processed is important, as areas with different spatial structure (e.g., vegetation versus buildings and roads) will produce poor results. Comparisons with bilinear interpolation demonstrate that IK could be used as an improved interpolation tool, for example, in the image-registration process.
Multispectral digital imagery from aircraft or satellite is presently being used to derive basic assessments of crop health for growers and others involved in the agricultural industry. Research indicates that narrow band stress indices derived from hyperspectral imagery should have improved sensitivity to provide more specific information on the type and cause of crop stress. Under funding from the NASA Earth Observation Commercial Applications Program we are identifying and evaluating scientific and commercial applications of hyperspectral imagery for the remote characterization of agricultural crop stress. During the summer of 1999 a field experiment was conducted with varying nitrogen treatments on a production corn-field in eastern Nebraska. The AVIRIS hyperspectral imager was flown at two critical dates during crop development, at two different altitudes, providing images with approximately 18m pixels and 3m pixels. Simultaneous supporting soil sampling, and aerial photography. In this paper we describe the experiments and results.
Center pivot (CP) irrigation systems account for more than half of all sprinkler irrigated lands in the U.S. In the Pacific Northwest, CP systems allow production of potatoes and other crops on sandy soils with variable topography. Prescription CP systems are being developed to reduce recharge to aquifers while maintaining or increasing crop yields. The hardware, software, and communication components for CP systems has been developed, installed and field tested. What is needed is the prescription for site specific applications of water, nitrogen, and pesticides. This paper discusses the role that remote sensing may have in providing some of the necessary spatial inputs to drive the irrigation models, and describes a CP prescription irrigation system that has been developed.