DSpace Communidade: Embrapa Agricultura Digital (CNPTIA)

Calibration and evaluation of a carbon net primary productivity module based on the SIMPLE model.

Thu, 01 Jan 2026 00:00:00 GMT

Título: Calibration and evaluation of a carbon net primary productivity module based on the SIMPLE model. Autoria: COLMANETTI, M. A. A.; OLDONI, H.; ANNIBAL, L.; WOLFF, W.; REJAILI, R. P. A.; BARIONI, L. G.; ALMEIDA, I. R. de; EWING, R. P.; CUADRA, S. V. Conteúdo: This study presents the carbon net primary productivity (CNPP) module, an imple-mentation of the simple generic crop model (SIMPLE model) that we have extendedto predict aboveground and belowground biomass production and carbon assim-ilation. The goal for this module is to predict biomass inputs at and below thesoil surface, during and at the end of crop cycles, while integrated into a broaderenvironmental carbon simulation framework, ProCarbon-Soil. CNPP was initiallyparameterized for soybean [Glycine max (L.) Merr.] and maize (Zea mays L.) usingmicrometeorological data, and for wheat (Triticum aestivum L.), bean (Phaseolusvulgaris L.), and perennial forage [Urochloa (syn. Brachiaria) brizantha (Hochstex A. Rich.) Stapf cv. Marandu] using agrometeorological experimental data and/ordata from the literature. Subsequently, calibrations for reference cultivars were per-formed, grouping cultivars by crop phenological characteristics and edaphoclimaticregions using farm-level data. CNPP accurately simulated leaf area index, evapotran-spiration, and biomass dry matter production and allocation for soybean and maizewhen evaluated at the sites with micrometeorological data (R2 > 0.76, Nash–Sutcliffeefficiency > 0.56, and relative root mean square error < 38% for all variables).Simulations for wheat, bean, and perennial forage exhibited lower performanceowing to lower availability of yield data. Nonetheless, the resulting statistics supportthis module’s efficacy in predicting crop productivity in major Brazilian agricul-tural areas. By employing a reduced and efficient parameter set, the CNPP moduleachieves enhanced performance and enables robust calibration across diverse crops,genotypes, and management schemes in multiple regions.

Produtividade em cana-de-açucar em variedades convencionais e tipo cana energia sob diferentes doses de potassio.

Sun, 01 Jan 2023 00:00:00 GMT

Título: Produtividade em cana-de-açucar em variedades convencionais e tipo cana energia sob diferentes doses de potassio. Autoria: SILVA, F. C. da; RAIZER, A. J.; CARVALHO, M. L.; DIAS, V. G.; CRISTOFOLETI, D.; CASTRO, A. de Conteúdo: A cana-de-açúcar é cultura de destaque no Brasil, pela importância socioeconômica e como fonte bioenergética, ocupando área de colheita de 8,5 milhões de hectares e produção de 655 milhões de toneladas, em 2021. O potássio se destaca dentre os nutrientes utilizados na cultura, sendo o nutriente exportado em maior quantidade, além de influenciar na sua qualidade. O objetivo do estudo foi avaliar, em vários ciclos, doses crescentes de potássio aplicadas na cana-de-açúcar, bem como o comportamento de variedades convencionais e de cana energia.

Identification of Leucaena leucocephala in urban landscapes using Street-level images and deep learning.

Thu, 01 Jan 2026 00:00:00 GMT

Título: Identification of Leucaena leucocephala in urban landscapes using Street-level images and deep learning. Autoria: FURUYA, D. E. G.; MARRAFON, G.; LEMOS, E. L. de; FURUYA, M. T. G.; GONÇALVES, R. D. S.; GONÇALVES, W. N.; MARCATO JUNIOR, J.; BOLFE, E. L.; LIESENBERG, V.; OSCO, L. P.; RAMOS, A. P. M. Conteúdo: Mapping urban tree species supports green infrastructure planning. An essential issue refers to the monitoring of exotic species that may become invasive. Street-level imagery provides a complementary perspective to aerial images for species identification that are difficult to distinguish from above. In this context, our study aimed to evaluate deep learning-based object detection and image segmentation approaches to identify a potentially invasive tree species known as Leucaena leucocephala in an urban environment in Brazil, using 422 street-level images acquired from Google Street View (SV) and mobile phones (MPs). Object detection models (YOLOv8 and DETR) and a foundation segmentation model (SAM, zero-shot) were applied to assess how deep learning paradigms perform under heterogeneous urban imaging conditions. YOLOv8 achieved detection performance with mAP50 above 0.83 and recall up to 0.76. DETR showed domain sensitivity, with mAP50 of 0.45 in SV images and 0.84 in MP imagery. For segmentation, SAM zero-shot achieved 0.92 accuracy and 0.93 F1-score in SV images, decreasing to 0.63 accuracy and 0.66 F1-score in MP images. Overall, this study demonstrates that combining detection and segmentation approaches provides complementary information for urban vegetation monitoring, supporting decision-making related to invasive species management and sustainable urban landscape planning.

Geo-referenced factor-graph SLAM for orchard-scale 3D apple reconstruction and yield estimation.

Thu, 01 Jan 2026 00:00:00 GMT

Título: Geo-referenced factor-graph SLAM for orchard-scale 3D apple reconstruction and yield estimation. Autoria: BHARTI, D.; FARIA, L. N. de; KOENIGKAN, L. V.; GEBLER, L.; RUFATO, A. de R.; SANTOS, T. T. Conteúdo: Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture.