Proceedings
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| Filter results5 paper(s) found. |
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1. Influence of Spectral Pre-processing and Signal-to-noise Ratio on Soil Fertility Prediction ModelsSoil and crop sensing through Vis–NIR spectroscopy is key to expanding the spatial and temporal coverage of precision agriculture initiatives. In this scenario spectral preprocessing and signal-to-noise ratio (SNR) significantly influence the accuracy and stability of soil fertility predictions based on spectroscopy. However, their impact is often underestimated, despite their effect on spectral quality and model performance. This study evaluated the influence of different spectral preprocessing... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva |
2. Geostatistical Comparison of Soil Fertility Maps Derived from Laboratory Soil Analyses and Spectral Model PredictionsSpatial mapping of soil fertility attributes is a key tool for precision agriculture and efficient management of agricultural fields. Soil spectroscopy is lately being presented as an efficient alternative to soil wet chemistry analysis; however, the spatial reliability of spectrally predicted data must be carefully evaluated. In this study, spatially interpolated maps generated from observed laboratory measurements and spectral predictions, of three soil attributes related to primary soil fertility... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva |
3. Recalibration of Spectral Models Using Spiking Techniques for Predicting Primary Nutrient AttributesSoil spectral libraries are an important strategy for rapid prediction of soil fertility atributes in digital agriculture projects. However, their predictive performance may decline when models are applied outside the specific conditions for which they were calibrated. Even in regions with similar pedoclimatic characteristics, management practices can limit model accuracy. In this context, spiking-based recalibration has been proposed as a practical strategy to improve model performance, although... A. Ten Caten, V. Ormeño, J.A. Henriques, M.M. Reva, M.S. Silva |
4. Evaluation of Transfer Learning in Semantic Segmentation Models for Soybean SeedlingsSeed vigor evaluation is fundamental in the quality control of commercial lots, as it is directly associated with the rapid and uniform emergence of seedlings and the initial performance of crops in the field. Traditional methods, although widely used, present limitations such as long execution time, dependence on the evaluator’s experience, and subjectivity. In this context, systems based on Computer Vision emerge as promising alternatives for automating vigor assessment, as they enable... E. Freitas, J. Martins Neto, P. Dos Santos E Silva, H.F. Abud, D.G. Gomes, V.C. Secundino |
5. Evaluation of Lettuce Image Classification with CNNs under Different NPK Nutritional ConditionsThe growing global demand for food has driven the development of technologies aimed at increasing productive efficiency in sustainable agricultural systems, such as hydroponics. In this context, proper monitoring of nutrient solutions is essential, particularly for the early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, which directly affect lettuce growth, yield, and quality. Traditional nutritional diagnostic methods often rely on destructive laboratory analyses,... E.L. Silva, E. Freitas, V.C. Secundino, D.G. Gomes |