Proceedings
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| Filter results19 paper(s) found. |
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1. 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 |
2. Estimating Grape Bunch Yield Using Convolutional Neural Networks and Proximal RGB Imaging in the Brazilian PampaViticulture of fine wines has become an increasingly important economic activity in the Pampa biome of southern Brazil, a relatively recent production frontier with approximately two decades of commercial development. In this emerging region, accurate prediction of grapewine productivity represents one of the most relevant challenges for growers, as reliable early estimates directly support decision-making related to harvest planning, logistics, labor allocation, and market... S. Camargo, E.M. Da Silveira, F.I. Nogueira, A.F. Campos, V.Z. Mércio |
3. Carbon Stock Assessment in Macaúba (Acrocomia Aculeata) Crops Based on Aerial Digital ImagesIn the current context of climate change, a palm tree named Macaúba, native to the Brazilian Cerrado, has gained prominence as a more sustainable alternative to oil palm, standing out for its high capacity to fix atmospheric carbon throughout its cycle. However, there is a lack of methodologies capable of quantifying carbon stocks in large-scale plantations in a cost-effective way, and manual sampling is still common. In this context, the main objective was to evaluate the effectiveness... P.M. De Sousa, B.C. De Albuquerque, V.A. Galvan, L.D. Corrêdo, L.D. Pimentel, J. Souza |
4. Deep Learning Models Applied to Drone Imagery for Counting, Biometry, and Carbon Stock Estimation in Large-scale Macaw Palm (Acrocomia Aculeata) PlantationsMacaw palm is a native Brazilian species with significant productive potential, emerging as a premier candidate for the sustainable replacement of oil palm and as a strategic feedstock for sustainable aviation fuel (SAF) and carbon credit markets. However, as the crop is still in the process of domestication and commercial expansion, there is an urgent need for efficient monitoring technologies that enable the identification of superior individuals and the rigorous auditing of carbon stocks across... P.M. De Sousa, V.A. Galvan, J. Souza, R. . De Oliveira , L.D. Corrêdo, L.D. Pimentel, B.C. Albuquerque |
5. Estimation of Broiler Chicken Mass using Computer Vision with Convolutional Neural NetworkIn poultry farming, monitoring bird mass during rearing is crucial, as it enables farmers to adjust parameters such as feed supply and lighting to better control weight gain. However, the methods currently used in poultry houses, i.e. manual weighing or poultry scales, present drawbacks, including the inability to weigh a representative number of birds or the frequent maintenance required to keep the equipment clean. The present work aims to validate an alternative method for estimating the mass... I.D. Azevedo, A.T. Salton, R.D. Castro, L.V. Erthal |
6. Quantifying Prediction Uncertainty in Field-scale Soil Maps Generated by Machine Learning.Field-scale maps of soil properties are a key component of precision agriculture, as they are routinely used as inputs for variable-rate fertilization, zone delineation, and site-specific management. While machine learning models have substantially improved the accuracy of spatial predictions, uncertainty associated with these predictions is often ignored, limiting the reliability of soil maps as decision-support tools. Quantifying prediction uncertainty is essential not only to assess map quality,... F. García Seleme, P. Paccioretti, M. Balzarini, M. Córdoba |
7. Soil-Sensing-Based Irrigation Decision Modeling for Greenhouse Tomato Crops Using Machine LearningGlobal agriculture faces increasing pressure to optimize water-use efficiency, particularly for high-demand crops like tomato (Solanum lycopersicum). Tomato is among the most widely consumed vegetables worldwide, playing a central role in global food systems. From an agronomic perspective, tomato crops are highly sensitive to water availability and distribution, requiring precise irrigation management to ensure sustainable production and high-quality yields. In controlled environments such as... P. Guerra, A.R. Raucci, S.A. Gutierrez , J.F. Botero, C. Kamienski, F.M. Campos De Oliveira |
8. Management Zone Delineation for the Optimization of Nitrogen Use Efficiency in Arabica Coffee CropsPrecision coffee farming requires efficient methods for Nitrogen (N) management—an input of high cost and environmental impact, whose optimization faces challenges due to the topographical characteristics of regions such as the Zona da Mata in Minas Gerais, Brazil. This study evaluates and compares different dimensionality reduction models for agricultural management zone (MZ) delineation, aiming to maximize nitrogen fertilizer use efficiency in Arabica coffee plantations. A dataset comprising... D.N. Nunes, R.P. Oliveira, L.D. Corrêdo, L. Peternelli, A.W. Pedrosa, V.H. Galvan, J. Souza |
9. An Online Decision Support Tool for Homogeneous Zone Delineation in Precision AgricultureManagement zone delineation is a key component of site-specific management in precision agriculture, enabling the spatial optimization of inputs and an improved understanding of within-field variability. Traditionally, homogeneous zones have been derived from historical yield maps or soil-related variables obtained through proximal sensing. More recently, the increasing availability of multispectral satellite imagery and derived vegetation indices has expanded the range of data sources available... |
10. Temporal Stability of Management Zones Derived from Vegetation Indices and Yield Data in Contrasting Production SystemsThe delineation of management zones is a central component of site-specific crop management in precision agriculture. However, the temporal stability of zones derived from different data sources remains a key challenge, particularly when vegetation indices and yield data are combined across multiple seasons. This study evaluates the temporal stability of management zones delineated using vegetation indices and yield data derived from long-term commercial field datasets. The proposed methodology... |
11. Statistical Mean Comparisons in Unreplicated Yield Trials with Georeferenced DataPrecision agriculture technologies have enabled the collection of large volumes of georeferenced yield data within experimental fields. In practice, many on-farm experiments (OFE) are implemented as large unreplicated strips or field zones containing numerous observations within each zone. The lack of replication prevents the use of classical statistical models for comparing zone means. Although many yield observations are available per zone, spatial autocorrelation violates independence assumptions... M. Córdoba, P. Paccioretti, M. Balzarini |
12. Application of CNNs in Cattle Counting using RPAsThe increasing demand for productive efficiency and sustainability in the agricultural sector has driven the adoption of technologies focused on Precision Livestock Farming. Among the main operational challenges in extensive systems, the counting and monitoring of cattle herds stand out. Historically performed manually, these activities are time-consuming, increase labor costs, and are highly susceptible to human error, especially across vast territorial expanses. However, the parallel advancement... E. De Souza Salles, C. Souza, R. Clemente Thom De Souza |
13. Detection of Banana Bunches and Peduncles in the Prata Catarina Cultivar Using Faster R-CNN With Transfer LearningBanana is one of the most produced and consumed fruits worldwide, being strategic for precision agriculture, especially in applications aimed at intelligent management and automated harvesting. Its economic and social relevance in tropical countries reinforces the need for technological solutions that increase productive efficiency and reduce losses in the field. In this context, the automatic detection of bunches and stalks in a natural environment represents a relevant challenge due to occlusion,... Y. Costa G. Da Silva, E. Freitas, P.S. Costa, D.V. Beserra, D.G. Gomes |
14. Automatic Detection of White Shrimp (Litopenaeus Vannamei) Feeding Activity Using Acoustic SignalsIn the cultivation of white shrimp (Litopenaeus vannamei), feeding management is one of the main challenges, accounting for approximately 40% to 60% of operational costs. Inaccurate feed management not only increases production costs but also compromises water quality, leading to environmental impacts. Shrimp produce acoustic events known as clicks, which makes it possible to use these signals as indicators of feeding activity. This study analyzes acoustic data collected over... F. Costa Filho, L. Affonso Guedes, S. Peixoto, I. Sánchez-gendriz |
15. Spatial Distribution of Coffee Leaf Miner Infestation and Its Impact on Coffee Fruit Maturation, Yield, and Beverage QualityDifferences in the maturation rate of coffee fruits can be associated with plant stress. The incidence of pests, such as the coffee leaf miner (Leucoptera coffeella), compromises the photosynthetically active area, which can reduce yield and beverage quality. Computer vision can assist in damage reduction by identifying the pest's spatial and temporal behavior. This study aimed to verify, spatially and temporally, the impact of damage caused by the coffee leaf miner on fruit... L.V. Lazzarini, A. , G.P. Cândido, V.M. Nunes, S.M. Hurtado, F.H. Leandro, I.D. Gonçalves |
16. Monitoring the Invasive Grass Eragrostis plana with Artificial Intelligence: A Comparative Study of Hyperspectral Data and Drone-Based Object DetectionThe invasion of exotic plant species is recognized as one of the major threats to biodiversity and ecosystem stability worldwide. In the Brazilian Pampa biome, Eragrostis plana Nees (commonly known as Annoni grass) has become one of the most aggressive invasive species since its introduction in the 1950s. Currently occupying approximately 20% of the native grassland vegetation in the state of Rio Grande do Sul, this species exhibits high adaptive capacity, rapid propagation, and the absence of... S. Camargo, N. Perez, T.S. Lopes, A.R. Silveira |
17. 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 |
18. NDVI Index and Its Correlation with Biennial Coffee YieldPrecision agriculture (PA) has consolidated itself as an important tool in agricultural management, allowing greater productive efficiency, cost reduction, and environmental sustainability. Among PA tools, the use of vegetation indices stands out, as it allows inferences about crop biomass both spatially and temporally. In this context, this study aimed to evaluate the relationship between the normalized difference vegetation index (NDVI) and the yield of Arabica coffee (Coffea arabica... L.V. Lazzarini, S.M. Hurtado, I.D. Gonçalves, M.F. Carneiro Filho, A. , G.P. Cândido |
19. Data Analytics in Precision Agriculture: Statistical Modelling and Machine Learning... M. Córdoba, P. Paccioretti |