2011 Annual Report
1a.Objectives (from AD-416)
This research seeks to provide improved management tools and information technologies to aid commercial cotton production in the Mid South through the application of precision agriculture practices. It seeks to reduce costs of cotton production through the acquisition, storage, processing, and application of geo-referenced information. Combines remotely sensed data with insect sampling methods to improve decisions regarding control for thrips and stink bugs in cotton. Develops appropriate statistical anslysis concepts and models for precision agriculture. Develops local area networking software and decision aids for digital data collection and use in precision agriculture.
1b.Approach (from AD-416)
Statistical models will be developed for geo-referenced measurement of precision agriculture cotton production practices. Improvements to mixed model analysis methodology will be developed using archived data as well as new data from remotely sensed images and field measurements. Statistical models to analyze spatial application decisions from data that involves a disproportionate number of zero counts and has small sample sizes will be developed and improved. Light Detection and Ranging (LiDAR) and Normalized Difference Vegetation Index (NDVI) data will be combined to facilitate site-specific management of thrips and stink bugs in cotton. Concepts and software will be built upon a wireless network to manage large quantities of geo-referenced data.
There remains a need to develop better methods for the spatial processing of remote sensing images for site-specific pest management applications in row crops. A new direction is being explored that involves an older image processing concept known as bit-planes. Bit-planes break out the different levels of digital information in each spectral band of an image according to the powers of 2 (2n, where n = 1, 2, 3, …, 8). Next, using geostatistical software that models spatial structure, each bit-plane is examined. Locations where spatial structure arises in the bit-plane of a specific spectral band or bands may potentially pinpoint where environmental or management effects have different spatial correlations in comparison to a background correlation pattern of randomness. This analysis process seeks to build a map where the spatial correlation between geo-referenced attributes differs among geographical sub-regions. Such structure, if resolved from the collections of the bit-planes, could indicate zones where different spatial management decisions are necessary.
McKinion, J.M., Willers, J.L., Jenkins, J.N. 2010. Comparing high density LIDAR and medium resolution GPS generated elevation data for predicting yield stability. Computers and Electronics in Agriculture. 74:244-249.
Milliken, G., Willers, J.L., McCarter, K., Jenkins, J.N. 2010. Designing experiments to evaluate the effectiveness of precision agricultural practices on research fields. Part 1. Concepts for their formulation. Operational Research: An International Journal (ORIJ). 10:329-348.
Willers, J.L., Riggins, J.J. 2010. Geographical approaches for integrated pest management of arthropods in forestry and row crops. In: Oerke, E.C., Gerhards, R., Menz, G., Sikora, R.A., editors. Precision Crop Protection - the Challenge and Use of Heterogeneity. New York, NY: Springer. p. 183-202.