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A Special Vegetation Index for the Weed Detection in Sensor Based Precision Agriculture

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Abstract

Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture.

The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF).

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Abbreviations

DIRT:

difference index with red threshold

LAI:

leaf area index

NDVI:

normalized difference vegetation index

NIR:

near infrared

WDRVI:

wide dynamic range vegetation index

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Authors and Affiliations

  1. Dept. Eng. for Crop Production, Institute of Agricultural Engineering Bornim (ATB), Max-Eyth-Allee 100, 14469, Potsdam, Germany

    Hans-R. Langner, Hartmut Böttger & Helmut Schmidt

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  1. Hans-R. Langner
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  2. Hartmut Böttger
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  3. Helmut Schmidt
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Correspondence to Hans-R. Langner.

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Langner, HR., Böttger, H. & Schmidt, H. A Special Vegetation Index for the Weed Detection in Sensor Based Precision Agriculture. Environ Monit Assess 117, 505–518 (2006). https://doi.org/10.1007/s10661-006-0768-3

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  • DOI: https://doi.org/10.1007/s10661-006-0768-3

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