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.
3. Progress Report:
Several meetings and discussions with potential users, collaborators, and ARS scientists at Mississippi State, MS, resulted in a better common understanding of what is needed to make significant progress in research and application of site-specific technology in cotton production. One consulting firm asked ARS for information regarding image processing of a 12 channel camera using ERDAS Imagine and other related software. This consulting firm now provides images for use by cotton, other row crop growers, and agricultural businesses in the Texas High Plains. This consulting firm is evaluating the acquisition of other sensors for its operation in order to provide more services for its clients. One invited presentation was made to the High Plains Association of Crop Consultants on information related to image based pest management, pest sampling, and relationship to the integrated control concept from the perspective of precision agriculture practices. About 135 crop consultants, extension specialists, university researchers, and agro-chemical field representatives were in attendance at this meeting. Feedback from the group at this meeting has led to changes in ARS field research so that it will be more useful and relevant to crop production systems in areas other than the Midsouth. One meeting was held with a potential collaborator regarding how to speed up interactions between software systems on farm implements which have different operating systems.
1. Categorical fusion of elevation and imagery information. An analysis methodology was developed by scientists in the Genetics and Precision Agriculture Research Unit at Mississippi State, MS, in cooperation with scientists at Mississippi State University that combines two maps, the digital elevation model (DEM) of field elevation relief and the arctangent of the Normalized Difference Vegetation Index (NDVI), into a single map which categorically describes the phenological variability of the crop. This map can be used to direct scouts to different regions of the field to assess insect pests or evaluate other crop conditions. The map can be used to improve the timing of various management decisions or to prepare the prescription for a variable-rate application. Other impacts are (1) the expansion of understanding of spatial autocorrelation structures of agricultural fields; (2) the development of additional processing methods for remote sensing products if a third geographical layer is available; (3) a better understanding of temporal changes that arise to the crop within and between seasons; and (4) leads to better analyses of factors that affect agricultural production to reduce cost and manage risk.
Willers, J.L., Wu, J., O'Hara, C., Jenkins, J.N. 2012. A categorical, improper probability method for combining NDVI and LiDAR elevation information for potential cotton precision agricultural applications. Computers and Electronics in Agriculture. 82:15-22.