A Special Vegetation Index for the Weed Detection in Sensor Based Precision Agriculture |
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Authors: | Hans-R Langner Hartmut Böttger Helmut Schmidt |
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Affiliation: | (1) Dept. Eng. for Crop Production, Institute of Agricultural Engineering Bornim (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany |
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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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Keywords: | decision criterion image processing mulched cropland red threshold signum function spectral sensing vegetation index weed detection |
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