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Crop Management Abstract - Crop Management Research

Using Multispectral Aerial Imagery to Evaluate Crop Productivity

 

This article in CM

  1. Vol. 4 No. 1
     
    Accepted: Dec 7, 2004


    * Corresponding author(s): wbaker@astate.edu
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doi:10.1094/CM-2005-0205-01-RS
  1. Clinton W. Jayroe,
  2. William H. Baker * and
  3. Amy B. Greenwalta
  1. a Arkansas State University, Jonesboro 72401

Abstract

Remote sensing offers the potential to provide quantitative and timely information on agricultural crops and could be utilized for creating variable rate prescriptions for fertilizer and chemical applications. This would be particularly useful for mid-season management of crop inputs by allowing producers to better allocate needed inputs, thereby increasing efficiency and hopefully overall profitability. The objectives of this study were: (i) to verify a correlation between data gathered by a yield monitor and multispectral imagery, (ii) to evaluate the relationship between soil electrical conductivity and vegetative growth patterns, and (iii) to establish a method of producing a vegetation map from images that could be used for directed scouting and mid-season chemical application decisions. Multispectral aerial imagery was used to observe crop canopy spectral reflectance in a study for rice (Oryza sativa), cotton (Gossypium hirsutum L.), and soybean (Glycine max) production fields in the Delta region of Arkansas. The images were acquired at various stages of crop growth and compared with ground reference observations consisting of soil electrical conductivity (ECa) and yield monitor data. Patterns were distinguishable with the use of the multispectral imagery along with some enhancements in a geographical information system (GIS). A classified, normalized difference vegetation index (NDVI) of the images was performed to further enhance the vegetative differences. The multispectral imagery proved to be a useful tool in assessing field variation through plant canopy reflectance. In the cotton and soybean production fields studied, yield variances due to soil characteristics were visible. Strong correlations, ranging from r2 = 0.26 to 0.83, were seen in classified images, yield maps, and ECa maps. However in the rice production field, final yield had little correlation with the images acquired throughout the season (r2 = 15), but the soil ECa map was related to the mid-season classified NDVI (r2 = 0.48).

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