Proceedings
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| Filter results6 paper(s) found. |
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1. Development and Field Validation of a Scalable UAV-Based Framework for Automated Cattle Counting and Herd Management in Extensive Production SystemsBrazil holds the largest commercial cattle herd in the world, with more than 230 million head, representing approximately 20% of the global population. In this context, technologies capable of optimizing herd monitoring are strategic for increasing production efficiency, reducing operational costs, and promoting sustainability in livestock systems. Among these technologies, computer vision–based systems have emerged as a promising alternative for automated animal detection and counting in... F. H. S. Sousa , T. S. Maciel, M. M. Dos Reis, R.D. Santos, A. M. Santos, A. M. S. De Souza, A. K. F. Veras, G. G. Ferreira, M. P. M. Nunes, M. C. R. Seruffo, L. C. C. Daher, A. G.m. Silva |
2. Estimation of Soybean Yield Using Remote Sensing and Soil Physical Attributes in Subsoiled AreasPrecision agriculture has incorporated these sensing and computational modeling technologies as strategic tools for monitoring crop development and estimating yield. In this context, the present study aimed to estimate soybean yield through vegetation indices obtained from satellite images, integrated with soil and plant variables, using artificial intelligence techniques. The experiment was conducted in a commercial field in the municipality of Brejo, Maranhão, in a region with a subhumid... W. Garreto, S. De Almeida, W. Da Silva Sousa, J. Costa Souza |
3. Spatial Analysis of Physical and Sensory Attributes of Coffee BeansArabica coffee (Coffea arabica L.) is one of the crops with the greatest economic and social relevance in Brazil, with beverage quality being a differential of broad commercial value. This study aimed to evaluate the spatial variability of the physical and sensory attributes of coffee beans. The study was conducted during the 2023-24 crop season in a 27-hectare plot belonging to Fazenda Mandaguari, in Indianópolis, Minas Gerais, cultivated with the Topázio cultivar under... V.M. Nunes, S.M. Hurtado, I. Almeida, A. , W.G. Siquieroli, G.P. Cândido, L.V. Lazzarini |
4. Integration of LiDAR-Derived TWI and UAV Multispectral Data for Waterlogging Susceptibility Mapping in Precision AgricultureTopography directly controls water redistribution across the landscape, shaping the spatial variability of soil moisture in agricultural areas. The Topographic Wetness Index (TWI), derived from digital elevation models, is widely used to estimate the potential for water accumulation; however, its field-scale validation supported by high-resolution multispectral drone imagery remains limited. In agricultural systems, recurrent waterlogging can reduce productivity by impairing germination, promoting... |
5. Comparative Evaluation of Ground Point Classifiers in LiDAR Point Clouds for DEM Generation in Pasture AreasThe classification of ground points in LiDAR point clouds is an essential step for generating reliable Digital Terrain Models (DTMs), particularly in livestock production systems based on pastures. Despite methodological advances in forested and urban environments, studies specifically addressing ground classification in pasture areas remain limited, where the proximity between the forage canopy and the ground surface makes altimetric distinction between classes challenging. The heterogeneous... |
6. YOLOv10x-based deep learning for automated detection and counting of seeds per soybean podAccurate quantification of the number of seeds per soybean pod is a fundamental step for reliable yield estimation. However, this measurement still relies on manual procedures, which are subject to observational variability and limited scalability. In the context of digital agriculture, deep learning–based techniques have shown promise for automating the detection and counting of reproductive structures. Nevertheless, there is still limited application of models specifically aimed... |