Proceedings
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| Filter results8 paper(s) found. |
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1. A Simplified Optical Sensor-Based Approach for Variable Rate Nitrogen Recommendation Using Vegetation Indices and Yield PotentialVariable rate nitrogen (VRN) management using active optical sensors has been widely studied as a strategy to improve nitrogen use efficiency and reduce environmental impacts. Most current methodologies are based on complex multi-step models that estimate in-season yield from vegetation indices, crop stage and multiple equations, which often results in unrealistic yield predictions and limits adoption by farmers and consultants. This study presents a simplified and robust approach for VRN... F. Povh, L.M. Gimenez, L.S. Flugel |
2. 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 |
3. Stability-driven Framework for Robust Plant Spectral Signature IdentificationAccurate identification of agricultural crops based on spectral signatures remains a critical challenge for large-scale phytosanitary monitoring. This study proposes a stability-based structure for the robust identification of plant spectral signatures, applied to the discrimination of soybean (Glycine max) from maize (Zea mays) and cotton (Gossypium hirsutum) under biotic stress caused by the pest Spodoptera frugiperda and stink bugs. The proposed method follows a flow of proposed steps that... J. Ferreira , A.O. Françani, E. . Ferreira, L.A. Jorge, J.C. Felipe, L. Zhao |
4. A Hybrid Non-Destructive Approach Combining Image Processing and Spectral Feature Selection for Grapevine Leaf Water Content EstimationReliable and continuous estimation of leaf water content (LWC) is essential for viticulture, as it enables assessment of spatiotemporal variability in vine water demand and supports improved irrigation management efficiency within Precision Agriculture (PA) practices. Although the gravimetric method based on fresh weight (FW) and dry weight (DW) measurements provides accurate LWC estimates, it is time-consuming, destructive, and exhibits limited scalability for large sample sizes. In contrast,... L.H. Bassoi, B.S. Costa, E.J. Ferreira, H. Oldoni, L.A. Jorge |
5. A Statistical Approach to Defining Coffee Management Zones: Integrating Apparent Soil Electrical Conductivity, Altimetry and Satelitte Indices for Moisture MonitoringCharacterizing the spatial and temporal behavior of soil and plant attributes represents the elementary step toward adoption precision agriculture. The expanding availability of multi-temporal remote sensing imagery with enhanced spatial resolution has rendered the delineation of management zones (MZ) an increasingly feasible strategy, especially when the intention is to carry out spatially differentiated interventions considering the vegetative vigor throughout the crop cycle or the plant yield.... E.A. Speranza, E.J. Ferreira, L.H. Bassoi, L.M. Rabello, C.M. Vaz, A. Torre-neto |
6. Weed mapping: advantages of RGB CNN-based approaches vs multispectral pixel-based methodsWeed detection remains a major challenge in modern agriculture, and accurate weed mapping is crucial to support rapid and efficient management interventions, ensuring crop productivity and economic viability. In this context, geotechnologies such as remote sensing and computer vision, together with the widespread adoption of drones, enable the acquisition of ultra–high spatial resolution imagery, allowing more detailed analyses in complex agricultural environments. Although multispectral... |
7. Optimization of Flight Parameters for High-Throughput Phenotyping of Guineagrass Using UASA fenotipagem de alto rendimento utilizando sistemas aéreos não tripulados (UAS) tornou-se uma ferramenta estratégica na agricultura de precisão aplicada a pastagens, permitindo a coleta rápida e não destrutiva de dados em larga escala. No entanto, a definição adequada dos parâmetros de voo, especialmente a distância de amostragem do solo (GSD) e a sobreposição de imagens, permanece um desafio, visto que configurações... |
8. Comparison of Real and Simulated GSD in the Estimation of Canopy Height in Maize Using RPAHigh-throughput phenotyping using remotely piloted aircraft (RPA) has become a strategic tool in precision agriculture, enabling rapid and non-destructive estimation of crop traits such as canopy height. Ground Sampling Distance (GSD) is a critical flight parameter in this process. Resolutions derived from smaller GSD values improve accuracy but increase flight time, image volume, and processing cost, whereas the opposite reduces these demands at a possible cost to accuracy. Simulating coarser... C. Ragalzi, M.J. Lima, L. Felipe, N. Guimarães, M.F. Santos |