Applications of deep learning in precision weed management: A review
The review analyzed 60 research papers on weed detection and discussed the potential of deep learning techniques for improving weed detection.
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Multiclass Classification on Soybean and Weed Species Using a Novel Customized Greenhouse Robotic and Hyperspectral Combination System
Hyperspectral imaging (HSI) effectively differentiates soybeans from five weed species. A robotic platform captured HSI data, which was analyzed using partial least squares regression (PLSR). The best model achieved 86.2% accuracy in classification, identifying key wavelengths linked to plant chemicals. This research shows promise for automated weed control in soybean fields.
Palmer amaranth (Amaranthus Palmeri S. Watson) and soybean (glycine max l.) classification in greenhouse using hyperspectral imaging and chemometrics methods
Hyperspectral imaging can effectively differentiate Palmer amaranth from soybeans. Chemometrics methods (PLS-DA, SIMCA) were used to analyze spectral data. Preliminary results show promise for using this technology to improve weed control in agriculture.