Broccoli/weed/soil discrimination by optical reflectance using neural networks

Federico Hahn

doi:10.1117/12.205126

6 April 1995 Broccoli/weed/soil discrimination by optical reflectance using neural networks

Federico Hahn

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205126
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

Broccoli is grown extensively in Scotland, and has become one of the main vegetables cropped, due to its high yields and profits. Broccoli, weed and soil samples from 6 different farms were collected and their spectra obtained and analyzed using discriminant analysis. High crop/weed/soil discrimination success rates were encountered in each farm, but the selected wavelengths varied in each farm due to differences in broccoli variety, weed species incidence and soil type. In order to use only three wavelengths, neural networks were introduced and high crop/weed/soil discrimination accuracies for each farm were achieved.

Citation Download Citation

Federico Hahn "Broccoli/weed/soil discrimination by optical reflectance using neural networks", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205126

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