Proceedings
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| Filter results13 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. 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 |
3. 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 |
4. 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... |
5. 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... |
6. 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 |
7. 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 |
8. 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 |
9. 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 |
10. 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 |
11. 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 |
12. 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 |
13. Data Analytics in Precision Agriculture: Statistical Modelling and Machine Learning... M. Córdoba, P. Paccioretti |