• K. Raja Reddy - Department of Plant and Soil Sciences, Mississippi State University
• Lee Tarpley - Department of Plant and Soil Sciences, Mississippi State University
• Frank Whisler - Department of Plant and Soil Sciences, Mississippi State University

• John Read - USDA-ARS
• James McKinion - USDA-ARS
• Jeff Willers - USDA-ARS
• Roger King - Engineering Research Center, Mississippi State University
• Mike Cox - Department of Plant and Soil Sciences, Mississippi State University
• Alex Thomasson - Department of Agricultural and Biological Engineering, Mississippi State University

Develop reliable remote-sensing and crop production relationships.

We are relating variations in leaf and canopy reflectance spectra to growth, development, leaf composition, and physiology of cotton plants grown under prescribed conditions. In addition, we are determining the bases of the observed reflectance spectra. This will allow for future physiological interpretation of the vegetative component of remote-sensing images, and assist in development of early and sensitive crop stress indicators. We are using a three-stage approach to ensure that the developed algorithms are reliable, precise and accurate.

1. Algorithms are initially developed using plants grown in large, sunlit plant growth chambers (SPAR units) with controlled temperature, nutrient and water supply, and atmospheric composition. Canopy-level photosynthesis, respiration, and transpiration are monitored during development, as are growth, leaf composition and physiology, and leaf reflectance spectra from 360 to 2500 nm. Current treatments include Nitrogen (N), Potassium (K), carbon (C), and water deficiencies.
2. Algorithms are tested and refined using the 1600 plants grown in a controlled-nutrient-release pot-culture with field-typical row spacing and computerized fertigation. Canopy- and leaf reflectance spectra are collected at a 2-day to weekly interval, and related to leaf composition, plus a number of growth, developmental and physiological parameters. Treatments include various levels of N, K and water deficiencies, three levels of a commonly used growth regulator (PIX), and seven varieties.
3. The models or algorithms will be validated in field for commercial applications.

We are ground truthing remote sensing images for soil and plant moisture status in two fields at Perthshire Farms in Bolivar County, MS. In a cotton field, 27 sites, representing six different soil series, are being monitored on a biweekly basis for soil moisture to a depth of 1 meter and for plant water potential. The same variables are being monitored in a soybean field of more uniform soil at eight different sites. These results will be compared to multispectral and other images taken at nearly the same dates by aerial cameras and sensors. In the future, the remotely sensed data, which can be collected over a wider area and more quickly than ground data, can be used to indicate irrigation needs as well as pest problems, and to separate these different plant stresses.

We are using specific spectral properties to:

1. predict how changes in plant physiology influence insect pest behavior and efficacy of defoliation of the crop,
2. provide expertise on field conditions conducive to insect pests, and
3. link remotely sensed imagery with crop health indices to assist integrated pest management practices.

The Engineering Research Center COMPUTATIONAL SIMULATION group is assisting us to:

1. develop hyperspectral diagnostic signatures,
2. interpret and verify the hyperspectral images by novel image analysis visualization techniques, and
3. develop value-added ready-to-use end-products for resource management in precision agriculture.

We are currently working with cotton and soybean, and will be refining the remote sensing methodologies in these crops. We will be extending the information to other major crops.