DSpace Communidade: Embrapa Agricultura Digital (CNPTIA)

Center for science for development of digital agriculture: Semear Digital, Brazil.

2026-01-01T00:00:00Z

Título: Center for science for development of digital agriculture: Semear Digital, Brazil. Autoria: BOLFE, E. L.; ROMANI, L. A. S.; BARBEDO, J. G. A.; MASSRUHÁ, S. M. F. S. Conteúdo: Projections of world population show that there will be a greater demand for food, fiber and energy, requiring an increase in agricultural productivity, cost reduction and sustainable use of natural resources. Thus, it is essential to consider the process of digital transformation in the countryside, with the introduction of equipment and sensors to collect and generate data in amounts that easily surpass the human processing capacity. In this context, “Semear Digital” is a Brazilian research center designed to enhance agricultural productivity and sustainability through digital technologies and connectivity solutions. The center prioritizes research in inclusive digital technologies in six axis, integrating small and medium scale producers, and operates under the coordination of the Brazilian Agricultural Research Corporation (Embrapa), funded by the São Paulo Research Foundation (FAPESP), and with a collaborative consortium of Research, Development, and Innovation (RD&I) institutions.

From satellites to smartphones: opportunities for digital agriculture in apple orchards.

2026-01-01T00:00:00Z

Título: From satellites to smartphones: opportunities for digital agriculture in apple orchards. Autoria: FURUYA, D. E. G.; BOLFE, E. L.; PARREIRAS, T. C. Conteúdo: Mobile phones images of Fuji and Gala apples are being used to evaluate the performance of neural networks for fruit detection, which can serve as the basis for developing future mobile applications not only for fruit detection but also for the assessment of different characteristics and parameters, such as size, color, or quality. Such applications may support farmers and technicians by offering practical and low-cost tools for monitoring orchard conditions.

Advancing coffee management mapping through multisensor data and multistep ensemble learning.

2026-01-01T00:00:00Z

Título: Advancing coffee management mapping through multisensor data and multistep ensemble learning. Autoria: PARREIRAS, T. C.; BOLFE, E. L.; FURUYA, D. E. G. Conteúdo: Despite the advances, accurately identifying recently renovated and skeletonized coffee areas remains a challenge, as their altered canopy structure and reduced vigor produce spectral signatures similar to those of fallow or non-coffee areas. To address these limitations, upcoming research will focus on leveraging a space-time hybrid approach with deep learning and surface phenology modeling. Specifically, we plan to implement a workflow combining the spatial detail of Sentinel-2 with the temporal continuity of HLS.

Estimating Arabica coffee productivity using Planetscope images and artificial neural networks.

2026-01-01T00:00:00Z

Título: Estimating Arabica coffee productivity using Planetscope images and artificial neural networks. Autoria: FAGUNDES, R. B.; KAYSER, L. P.; BENEDETTI, A. C. P.; AMARAL, L. de P.; BOLFE, E. L.; LEMOS, O. L. Conteúdo: Research on orbital remote sensing and artificial neural networks (ANN) for estimating coffee productivity has advanced in Brazil, but applications remain scarce in states such as Bahia. This study aimed to develop and validate a model to estimate Arabica coffee (Coffea arabica L.) yield using PlanetScope images and ANN, based on two commercial areas with contrasting production systems (rainfed and irrigated). Georeferenced productivity samples (2 per hectare) were collected during the 2024 harvest, and PlanetScope images were analyzed from August 2023 to May 2024. In R software, vegetation indices related to vigor, nutritional status, and water stress were calculated, and their minimum, mean, and maximum values were extracted. Most variables followed a normal distribution (Shapiro–Wilk test, p < 0.05), enabling Pearson’s correlation analysis. The best correlation was observed in Santa Vera, where the CCCI index of 08/20/2023 showed a moderate positive correlation with yield (r = 0.66; p = 0.001). After ANN training in SPSS software, the model achieved very high predictive performance in both areas, with R² and adjusted R² above 0.99, low errors (MAE = 0.031–0.072; RMSE = 0.042–0.087; relative RMSE = 0.029–0.043), and almost no bias (BIAS = 0.001; relative BIAS = 0). Among the vegetation indices, CCCI was the most relevant, followed by NDRE, NDVI, NDWI, and EVI. Overall, the findings demonstrate that combining PlanetScope imagery with ANN provides a highly accurate approach for estimating Arabica coffee productivity under different production systems in Bahia.