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| Filter results16 paper(s) found. |
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1. 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 |
2. 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 |
3. 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 |
4. Towards Precision Agriculture with Electrochemical Sensors for Detecting Dopamine in Plants and FruitsCathecolamines are essential neurotransmitters that regulate the central nervous system of animals, while also acting as direct modulators in plants, coordinating antioxidant responses and ionic balance regulation. Dopamine (DA) is an essential catecholamine not only for animals but also plays a critical regulatory role in plants, acting as a potent antioxidant and growth modulator under abiotic stresses such as drought and salinity or pathogen attacks and helps neutralize free radicals and... L.M. Gonçalves, B. Vasconcellos Lopes, B.B. Gallo, A. La Rosa, D.A. Fruchtenicht, C. Miler, P. Silveira, N.L. Carreno |
5. Optimization of Electrochemical Device Development: Laser-Induced Graphene Electrode as an Alternative for Agricultural Monitoring.The agroindustrial sector has driven the technological development of electrochemical devices aimed at field applications. In this context, laser-induced graphene (LIG) electrodes stand out for enabling electrode miniaturization, favoring in situ analyses and equipment portability. These devices exhibit high sensitivity, selectivity, rapid response, and low cost, characteristics that expand their application potential in different scenarios. However, the growing demand for these devices highlights... C. Miler, L. Gonçalves, B. Vasconcellos Lopes, B.B. Gallo, A. La Rosa, D. Fruchtenicht, P. Silveira, F. , N. Carreño, L. Machado |
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. Comparison of Machine Learning Models for Within-field Cotton Yield Prediction: Assessing the Value of Temporal Data and GeneralizabilityCotton (Gossypium hirsitum L.) yield prediction is challenging due to the complex interactions between static environmental factors, such as soil properties and topography, and dynamic variables, including weather and crop management. These factors generate significant spatial and temporal variability within and across fields. While remote sensing technologies, particularly satellite-derived vegetation indices, provide spatially explicit data to evaluate crop health, translating... L. Bastos, L. Fuhrer, W. Porter, G.J. Scarpin, A. Kaur Dhaliwal, A. Bhattarai, A. Jakhar |
10. 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... |
11. 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... |
12. 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 |
13. Relationship Between Temporal Variability of Soybean Yield and Stable Soil AttributesManagement zones are widely used in precision agriculture and can be defined by different factors; however, uncertainties remain regarding their temporal stability when based on a single soil attribute. This study aimed to analyze the relationship between a temporal series of yield from five agricultural fields and four stable soil attributes—clay content, soil organic matter (SOM), Topographic Wetness Index (TWI), and apparent electrical conductivity (ECa)—using multiple linear regression... G. Kaefer Seganfredo, L.G. Kern, L. Silveira Pavão, A. Müllich, I. Maldaner, J. Sgarbossa, L. , E. Rolim Farias Da Silva, M.S. Farias |
14. 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 |
15. Development and Field Validation of SMART-C: A Geostatistics and PCA-Based Decision Framework for Site-Specific Cocoa Management in the Brazilian AmazonCocoa production plays a major socioeconomic role in Pará State, Brazil’s largest producing region, with annual output exceeding 140 thousand tons. Although Brazil ranks among the world’s leading cocoa producers, most production systems are still managed using field-average approaches that disregard within-field spatial variability of soil attributes and crop performance. This limitation restricts input efficiency and long-term system sustainability in perennial tropical systems.This... |
16. Data Analytics in Precision Agriculture: Statistical Modelling and Machine Learning... M. Córdoba, P. Paccioretti |