Proceedings
Authors
| Filter results3 paper(s) found. |
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1. Characterizing Cross-Crop Stink Bug Spectral Signatures from Hyperspectral DataEffective crop protection in agricultural production systems requires the ability to detect pest-induced stress in a timely and reliable manner. In large-scale farming systems, stink bugs attack multiple crop species, making cross-crop pest detection a critical capability for scalable monitoring solutions. Rather than developing crop-specific models that require retraining for each species, identifying crop-independent spectral signatures of stink bug infestation enables transferable detection... A.O. Françani, L. Zhao, J. Ferreira , J. Yan, E.J. Ferreira, L.A. Jorge |
2. Guiding Spot Sprayer Decisions: Toward Species-Selective Weed ControlSite-specific weed management is a key approach in precision crop protection, enabling spatially targeted herbicide application based on within-field variability in weed distribution. However, most operational spot-spraying systems rely on uniform nozzle activation rules, implicitly treating all detected weeds equally despite differences in competitive ability and ecological function. This limits the potential of precision systems to exploit species-level differentiation in practice. This... M. Gentili, M.S. Madsen, V.A. Nichols, R.N. Jørgensen, J.R. Jørgensen, |
3. Enhancing Pest Detection Through Spectral Signature Extraction in Hyperspectral DataDetecting insect infestation is essential for effective crop protection, particularly in large-scale systems. Caterpillars and stink bugs induce physiological and structural alterations in plant tissues that can be captured through hyperspectral reflectance sensing, which is a non-destructive technique that measures plant responses across hundreds of wavelengths. However, raw spectral signatures are characterized by high dimensionality, strong inter-band correlation, and they often exhibit baseline... A.O. Françani, J. Ferreira , L. Zhao, L.A. Jorge, K.M. De Oliveira, J.C. Felipe |