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| Filter results16 paper(s) found. |
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1. Application of Machine Learning Algorithms and Remote Sensing for Predicting Losses in Peanut HarvestingPeanut (Arachis hypogaea L.) is a crop of substantial economic and social relevance in Brazil, particularly in the state of São Paulo, which accounts for the majority of national production and consistently attains high productivity levels. Despite significant advances in agricultural mechanization, harvesting remains one of the most critical phases of peanut production, especially during mechanical digging, a stage in which considerable yield losses frequently occur. These losses are classified... G. Pereira Costa, A.L. Brito Filho, T.C. Oliveira, J. , R.P. Silva |
2. Can Management Zones Be Useful in Guiding Mechanized Peanut Harvesting?Mechanized peanut harvesting presents challenges due to the crop’s indeterminate growth habit, which results in non-uniform maturation across the field; the ideal harvest point is reached when the maturity index exceeds 0.7. Consequently, analyzing the spatial variability of maturation is essential for identifying homogeneous areas and guiding harvest at the optimal time. In this context, Management Zones (MZs), as a Precision Agriculture tool, enable the subdivision of fields into more... A. Andrade Da Silva, T.C. Moura Oliveira, E. Sales, S. Luns, E.M. Perussi, R.H. De Souza Silva, S. Luns, R.P. Silva, J. , A.L. De Brito Filho , R.P. Silva |
3. How Does Yield Data Filtering in Grain Harvesters Influence the Quality of Interpolated Maps?Yield maps generated from grain harvester data are effective tools for characterizing the spatial variability of crop yields. However, several embedded errors are inherent in these datasets, requiring removal methods to ensure the fidelity of actual field yield values and the reliability of the resulting maps. Therefore, this study aimed to determine the optimal combination of parameters for the global and local filtering of grain harvester yield maps to improve the quality of interpolated maps.... A. Andrade Da Silva, E. Sales, T. Moura Oliveira, R. De Souza Silva, S. Luns, R.P. Silva, E. Perussi |
4. High-resolution Orbital Imagery and Neural Networks to Predict Brix and Purity in SugarcaneIntegrating artificial neural networks with high-resolution satellite remote sensing data can provide non-destructive indicators for assessing sugarcane quality at field scale. Conventional laboratory methods for sucrose-related quality assessment are costly, labor-intensive, and operationally demanding, particularly when applied continuously over large commercial areas. This study evaluated the potential of multispectral imagery from the PlanetScope CubeSat platform, vegetation indices, and accumulated... P. Cardoso, R.P. Silva, T.R. Da Silva, M.F. De Oliveira, J.B. Souza, S.L. De Almeida |
5. Spatial Variability in Mechanized Coffee Harvesting at Two Travel SpeedsWithin the scope of Precision Agriculture (PA), monitoring the spatial variability of mechanized operations is a strategy for improving operational efficiency in coffee plantations. The evaluation of mechanized harvesting should consider, in addition to the harvested volume, the proportion of coffee effectively detached in relation to fallen coffee (FC) and remaining coffee (RC), variables directly linked to operational efficiency. Harvesting was carried out at Mariano Farm, in Poços de... N.A. Zimermam, R.P. Pereira Da Silva, L.E. Zonfrilli, A. Andrade Da Silva, T.M. De Oliveira |
6. Integrating Management Zones, Artificial Neural Networks and Remote Sensing for Smart Peanut HarvestingThe integration of technologies contributes significantly to agricultural development, especially regarding the rational and more sustainable use of soil. Thus, the use of remote sensing and artificial intelligence techniques combined with precision agriculture can maximize smart harvesting for peanut crops, which face several challenges such as limited harvesting technology, indeterminate growth, and the development of pods below the soil surface. Therefore, this study aimed to develop a peanut... |
7. Alternative Method for Measuring Fuel Consumption in Agricultural Machinery Using Arduino and Flow SensorsMonitoring fuel consumption in agricultural machinery is a strategic component of precision agriculture, as it is directly associated with operational efficiency, cost reduction, and the mitigation of CO₂ emissions. Despite technological advances in agricultural tractors, most machines, including recent models, do not feature dedicated sensors for direct fuel flow measurement, relying instead on visual fuel level indicators or estimates based on engine parameters, which limits the accuracy of... R. De Souza Silva, L.E. Zonfrilli, A. Andrade Da Silva, R.P. Silva, A.D. Carreira |
8. Evaluation of Horizontal Distribution in Spraying with RPA in Static and Dynamic ModesThe application of plant protection products (PPPs) using remotely piloted aircraft (RPA) represents a significant innovation in the context of modern agriculture, especially regarding the pursuit of greater operational efficiency and the reduction of environmental impacts. The use of this technology has stood out for the possibility of carrying out more precise applications, with better control of droplet deposition and potential reduction in in input consumption. However, despite its promising... G. Gomes Mesquita , J.B. Costa Souza, I. De Oliveira Vieira, S.L. Hatum De Almeida, V.D. Carreira, R.P. Silva, A. Felipe Dos Santos |
9. Agroclimatic and Topographic Zoning for the Sustainable Expansion of Peanut Production in the State of Georgia, USASustainable agricultural production depends on a detailed analysis of environmental conditions to support decision-making. This study aimed to develop a topoclimatic zoning for peanut production in Georgia, USA, using climatic data from the PRISM Climate Group and topographic data from OpenTopography. The water deficit was calculated using the Thornthwaite and Mather methodology. The methodology included the reclassification of variables into three suitability classes for cultivation, based on... I. De Oliveira Vieira, S. Luns, R.C. Mendes, L. Bastos, R.P. Silva |
10. A Decentralized Digital Twin Architecture for Interoperable Digital Agriculture SystemsThe increasing digitalization of agriculture has driven the widespread adoption of heterogeneous sensing systems, autonomous platforms, and data-driven decision-support tools. Despite these advances, interoperability limitations at both the syntactic and semantic levels remain a major challenge, hindering the scalable integration and coordinated operation of agricultural assets. Current agricultural systems are often developed as vertical, vendor-specific solutions, resulting in fragmented... P.H. Morgan Pereira, G. Costa Piazza, K. Usman, L. Santos Comelli Da Silveira, V. Cella Ceriotti, Y. De Souza Pazin, E. Pignaton De Freitas |
11. Sun-view Geometry Causes Hotspot Effect in UAV Imagery During Summer in Tropical RegionsHigh-resolution imagery acquired by Unmanned Aerial Vehicles (UAVs) is essential for remote sensing applications. However, the high solar elevation around noon during summer in tropical regions produces a hotspot effect when the camera is mapping at nadir. The sun-view alignment generates a bright spot in each image acquired during the flight, which significantly changes the digital number and, consequently, the estimated surface reflectance. The objective of this research was to analyze the presence... W. Maes, L. Rodrigues , A.M. Tommaselli, R.P. Silva, V. Carreira |
12. Evaluation of the Similarity between Management Zones Based on Spectral Indices and Soil Electrical ConductivityThe delineation of management zones is one of the main strategies in precision agriculture, as it enables site-specific interventions and improves input-use efficiency. Soil electrical conductivity (EC) is widely used to represent the spatial variability of soil physical and chemical attributes and is often considered a reliable indicator. However, acquiring EC data requires specific equipment and field operations, which may increase operational costs. In contrast, spectral indices derived from... |
13. Estimating Peanut Losses Using Machine Learning with Soil and Weather DataMechanized peanut harvesting is an important phase of the production system, directly affecting both production costs and crop yield. However, due to interactions among soil conditions, plant characteristics, and machine performance, this operation is carried out under challenging conditions that may result in high levels of loss. These losses are classified as visible when pods remain on the soil surface after digging and as invisible when they are incorporated into the soil profile, making them... A. Lopes De Brito Filho, F. Morlin Carneiro, G. Pereira Costa, B. Dos Santos Silva, P.H. Nogueira Gusmão, R.P. Pereira Da Silva |
14. Climatic Zoning of the Peanut Cercosporiosis Complex in São Paulo Under Climate Change ScenariosThe cercosporiosis complex is an important foliar disease of peanut, caused by the fungi Cercospora arachidicola and Nothopassalora personata, impacting grain yield and quality. Another relevant aspect is the symptoms of defoliation and vegetative weakening caused by these fungi, which may lead to significant losses during the digging and harvesting stages of peanut, a crop intrinsically associated with mechanization. The objective of this study was to develop a climatic zoning of the cercosporiosis... R. Mendes, I. De Oliveira Vieira, R.P. Silva |
15. Integration of Spectral Phenological Markers and Artificial Neural Networks for Modeling the Yield of Potato CultivarsThe growing demand for food underscores the importance of essential crops such as potato. In this context, understanding yield dynamics is critical, and digital agriculture emerges as a key tool, enabling more efficient estimation of this variable without the need for destructive sampling. Accordingly, this study aimed to use orbital remote sensing combined with artificial intelligence algorithms to develop more accurate and precise models for potato yield prediction. Field data collection was... S. Luns, J.B. Souza, B. , L. Conceicao Da Silva, R.P. Silva, V. Carreira |
16. Latin America and the Caribbean Regional Meeting... R.P. Pereira Da Silva |