A5041921573- Remote Sensing in Agriculture
- Banana Cultivation and Research
- Soil Geostatistics and Mapping
- Smart Agriculture and AI
- Sugarcane Cultivation and Processing
- Agricultural and Food Sciences
- Spectroscopy and Chemometric Analyses
- Rural Development and Agriculture
- Soil and Land Suitability Analysis
- Geography and Environmental Studies
- Remote Sensing and LiDAR Applications
- Leaf Properties and Growth Measurement
- Growth and nutrition in plants
- Land Use and Ecosystem Services
- Remote Sensing and Land Use
- Soil Management and Crop Yield
- 3D Modeling in Geospatial Applications
- Data Mining Algorithms and Applications
- Sustainable Agricultural Systems Analysis
- Coastal and Marine Management
- Research in Cotton Cultivation
- Semantic Web and Ontologies
- Crop Yield and Soil Fertility
- Irrigation Practices and Water Management
- Soil Carbon and Nitrogen Dynamics
Brazilian Agricultural Research Corporation
2011-2025
Cotton (United States)
2023
University of Nebraska–Lincoln
2023
Currently, Brazil is the leading producer of sugarcane in world, with self-sufficiency use ethanol as a biofuel, well being one largest suppliers sugar to world. This study aimed develop predictive model for production based on data extracted from aerial imagery obtained drones or satellites, allowing precise tracking plant development field. A semiparametric approach associated inverse Gaussian distribution applied vegetation indices (VIs), such Normalized Difference Vegetation Index (NDVI)...
This research explores the estimation of sugarcane (Saccharum officinarum L.) productivity at harvest by leveraging average satellite image time series collected during growth phase. The study aims to evaluate effectiveness diverse modeling approaches, including a Heteroskedastic Gamma Regression model, Random Forest, and Neural Networks, in predicting yield using satellite-derived vegetation indices environmental variables. Key covariates such as varieties, production cycle, accumulated...
This research investigates how to estimate sugarcane (Saccharum officinarum L.) yield at harvest by using an average satellite image time-series collected during the growth phase. study aims evaluate effectiveness of various modeling approaches, including a heteroskedastic gamma regression model, Random Forest, and Artificial Neural Networks, in predicting based on satellite-derived vegetation indices environmental variables. Key covariates analyzed include varieties, production cycles,...
Homogeneous management zones (HMZs) delineation is important for the application of precision agriculture because farm decisions are based on it. Diverse soil chemical characteristics HMZs delineation. However, summarizing several variables into homogeneous zoning while taking account spatial distribution pattern a challenge. Addressing this challenge to produce oriented practical use farmers. In work, 17 were jointly analyzed HMZ by using indicator kriging (IK) interpolate fertility index...
Satellite remote sensing data expedite crop yield estimation, offering valuable insights for farmers’ decision making. Recent forecasting methods, particularly those utilizing machine learning algorithms like Random Forest and Artificial Neural Networks, show promise. However, challenges such as validation performances, large volume of data, the inherent complexity inexplicability these models hinder their widespread adoption. This paper presents a simpler approach, employing linear...
Este artigo apresenta e discorre sobre os atributos da agricultura de precisão (ap) digital (ad), expondo as particularidades sinergias cada uma delas. Explica como a ap vem sendo empregada nos sistemas produção vegetal animal em vários países desde década 1990, com intensidade abrangência relação à área aos tipos que adotam evoluem gradualmente. A compreende o uso procedimentos equipamentos, implementos e/ou sensores avaliam variabilidade espacial temporal do solo, planta, ou clima, intuito...
Agriculture is directly related to the Sustainable Development Goals (SDGs) proposed by United Nations. Digital transformation in agriculture an important trend for sustainable practices and governance. In recent years, various sensors have been used collect large volumes of data that allows faster more complex analysis. This paradigm shift has led data-driven agricultural management, which aids farmers decision-making process. platforms storage, organization, management are tools companies...
The delineation of management zones is one the ways to enable spatially differentiated plots using precision agriculture tools. Over years, spatial variability data collected from soil and plant sampling started be replaced by proximal orbital sensors. As a result, variety volume have increased considerably, making it necessary use advanced computational tools, such as machine learning, for analysis decision-making support. This paper presents methodology used establish (MZ) in analyzing...
In this paper we propose a cluster-based approach for the delineation of management zones in precision agriculture. The proposed was built following steps data mining clustering task, resulting computer application that generates maps and yield areas, allowing to compare them using known statistical indexes. basis implementation model previously published literature uses only historical productivity, soil electrical conductivity relief generate maps. main difference our work with respect...
Variable-rate application has great potential to reduce variability and increase yield by spatially optimizing agricultural inputs. In cotton, plant growth regulators (PGRs) control excessive provide suitable height for harvest operations. This study evaluates the effect of variable-rate PGR compared constant-rate spatial yield. The approach was carried out in 2020 based on zonal applications defined clustering analysis using soil electrical conductivity, vegetation indexes, maps....
Crop yield estimation supported by satellite remote sensing data can provide expeditious and strategic information for farmers’ decision-making. Most recent forecasting methods have indicated a promising pathway based on machine learning algorithms. However, validation performances, demand big their inherent inexplicability not yet consolidated substantial differential to replace simpler more understandable models. This paper proposes an approach simple linear models fitted from vegetation...
Sugarcane plays a pivotal role in the Brazilian economy as primary crop. This semi-perennial crop allows for multiple harvests throughout its life cycle. Given longevity, farmers need to be mindful of avoiding gaps sugarcane fields, these interruptions planting lines negatively impact overall productivity over years. Recognizing and mapping failures becomes essential replanting operations estimation. Due scale cultivation, manual identification prove impractical. Consequently, solutions...