Neural networks using broadband spectral discriminators reduces illumination required for broccoli identification in weedy fields

Federico Hahn

doi:10.1117/12.235929

22 March 1996 Neural networks using broadband spectral discriminators reduces illumination required for broccoli identification in weedy fields

Federico Hahn

Author Affiliations +

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235929
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

Statistical discriminative analysis and neural networks were used to prove that crop/weed/soil discrimination by optical reflectance was feasible. The wavelengths selected as inputs on those neural networks were ten nanometers width, reducing the total collected radiation for the sensor. Spectral data collected from several farms having different weed populations were introduced to discriminant analysis. The best discriminant wavelengths were used to build a wavelength histogram which selected the three best spectral broadbands for broccoli/weed/soil discrimination. The broadbands were analyzed using a new single broadband discriminator index named the discriminative integration index, DII, and the DII values obtained were used to train a neural network. This paper introduces the index concept, its results and its use for minimizing artificial lightning requirements with broadband spectral measurements for broccoli/weed/soil discrimination.

Citation Download Citation

Federico Hahn "Neural networks using broadband spectral discriminators reduces illumination required for broccoli identification in weedy fields", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235929

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