{"id":2390,"date":"2024-07-25T15:21:56","date_gmt":"2024-07-25T18:21:56","guid":{"rendered":"https:\/\/www2.uepg.br\/computacaoaplicada\/?page_id=2390"},"modified":"2024-08-01T16:05:32","modified_gmt":"2024-08-01T19:05:32","slug":"dissertations","status":"publish","type":"page","link":"https:\/\/www2.uepg.br\/computacaoaplicada\/en\/dissertations\/","title":{"rendered":"Dissertations"},"content":{"rendered":"
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101th – <\/strong>Few-shot learning evaluation for classifying soybean productivity images obtained by remotely piloted aircraft<\/a><\/span>
\n100th – <\/strong>Classical and machine learning methods for predicting fresh tomato prices<\/a><\/span>
\n99th – <\/strong>Development of a tool to identify ribosomal proteins in MALDI-TOF mass spectra<\/a><\/span>
\n98th – <\/strong>Electronic nose system applied to the analysis of flowering and thinning in peach trees<\/a><\/span>
\n97th – <\/strong>Integration of functionalities for the traceability of organic products in Rastrogr\u00e3o<\/a><\/span>
\n96th – <\/strong>Ontology applied to soybean cultivation system<\/a><\/span>
\n95th – <\/strong>Analysis of Siamese neural network architectures for plant species classification<\/a><\/span>
\n94rd – <\/strong>Identification of plant species using convolutional autoencoder and unsupervised learning<\/a><\/span>
\n93nd – <\/strong>Plant species classification based on images of leaf and flower components<\/a><\/span>
\n92st – <\/strong>Evaluation of the positional quality of control points for generating digital agricultural models<\/a><\/span>
\n91th – <\/strong>Analysis of corn development using image processing from remotely piloted aircraft<\/a><\/span>
\n90th – <\/strong>Classification of igneous rock aggregates based on their alteration using digital image processing<\/a><\/span>
\n89th – <\/strong>Convolutional neural network and metadata pattern in soybean grain classification<\/a><\/span>
\n88th – <\/strong>Convolutional neural networks and data augmentation for detecting anthracnose in bean leaves<\/a><\/span>
\n87th – <\/strong>Bacterial classification based on ribosomal proteins derived from genomic data<\/a><\/span>
\n86th – <\/strong>Influence of segmentation threshold variation on root parameter measurement systems<\/a><\/span>
\n85th – <\/strong>Irrigation system for curly lettuce crops based on fuzzy logic<\/a><\/span>
\n84rd – <\/strong>Wheat productivity prediction using spectral data and estimated plant height obtained via remotely piloted aircraft<\/a><\/span>
\n83nd – <\/strong>Dynamic selection schemes for identifying bacteria from virtual m\/z data of ribosomal proteins<\/a><\/span>
\n82st – <\/strong>Learning classifiers for bacterial identification: relationship between data complexity measures and classifier performance<\/a><\/span>
\n81th – <\/strong>Agricultural product traceability method using blockchain<\/a><\/span>
\n80th – <\/strong>Hortiprice: Domain framework for horticultural sales price formation<\/a><\/span>
\n79th – <\/strong>Metataxonomic analysis of microbial communities in maize rhizosphere after nitrogen application and bacterial inoculation<\/a><\/span>
\n78th – <\/strong>Identification of ribosomal proteins in MALDI-TOF mass spectra<\/a><\/span>
\n77th – <\/strong>Data quality and Internet of Things in the context of grain traceability<\/a><\/span>
\n76th – <\/strong>K-means algorithm in a manycore environment to reduce data mining response time<\/a><\/span>
\n75th – <\/strong>Generation of digital models and indices for diagnosing erosion in agricultural areas<\/a><\/span>
\n74rd – <\/strong>Analysis of curly lettuce cultivation in protected environments based on physical quantities and digital image processing<\/a><\/span>
\n73nd – <\/strong>Development of an Electronic Nose for determining the ripeness stage in peach trees<\/a><\/span>
\n72st – <\/strong>Pest classification method using a deep convolutional neural network<\/a><\/span>
\n71th – <\/strong>kNN algorithm in MALDI-TOF mass spectra data imputation: An analysis of the influence of kNN imputation on logistic classifiers’ performance for bacterial identification<\/a><\/span>
\n70th – <\/strong>Spectral band selection supported by PSO metaheuristic to predict exchangeable aluminum content in soil samples<\/a><\/span>
\n69th – <\/strong>Diagnosis of erosion processes in agricultural soils using terrain numerical models analysis<\/a><\/span>
\n68th – <\/strong>Development of a mobile helical capacitive sensor for analyzing the distribution of solid mineral fertilizers<\/a><\/span>
\n67th – <\/strong>Evaluation of image mosaic methods applied to agricultural images obtained by UAV<\/a><\/span>
\n66th – <\/strong>Ontology in agricultural data traceability<\/a><\/span>
\n65th – <\/strong>Evaluation of the efficiency of classical and spatial data mining in estimating grain productivity from images obtained via remotely piloted aircraft<\/a><\/span>
\n64rd – <\/strong>Evaluation of nitrogen nutritional status and estimation of wheat biomass productivity using remote sensing data extraction<\/a><\/span>
\n63nd – <\/strong>Usageo: Method for usability evaluation in agricultural systems<\/a><\/span>
\n62st – <\/strong>Use of machine learning to classify bacteria using ribosomal proteins<\/a><\/span>
\n61th – <\/strong>Integration of geoinformation in the framework of grain traceability<\/a><\/span>
\n60th – <\/strong>Model-Based Development Methodology and Its Application in Agricultural Machinery<\/a><\/span>
\n59th – <\/strong>Computational Method for Identifying Cercospora Kikuchii Fungus in Soybean Seeds<\/a><\/span>
\n58th – <\/strong>Classification of Rapideye Orbital Images Using NOSQL Database and GEOBIA Method<\/a><\/span>
\n57th – <\/strong>Dimensionality Reduction for Estimating Nutrient Content in Soybean Leaves and Grains Using Infrared Spectroscopy<\/a><\/span>
\n56th – <\/strong>Software Architecture for Optimizing the Use of Remotely Piloted Aircraft in Precision Agriculture Using Case-Based Reasoning<\/a><\/span>
\n55th – <\/strong>An Architecture to Integrate Data Storage, Spatial, and Precision Agriculture Environments<\/a><\/span>
\n54rd – <\/strong>Data Mining of Images Obtained with Remotely Piloted Aircraft for Wheat Productivity Estimation<\/a><\/span>
\n53nd – <\/strong>Estimation of Asian Rust Severity Index in Soybean Crop Using Images Obtained with Remotely Piloted Aircraft<\/a><\/span>
\n52st – <\/strong>Automated Agro-Meteorological Experimental Station Integrated with Software for Dormancy Estimation in Peach and Grape Vines<\/a><\/span>
\n51th – <\/strong>Evaluation of Root Length and Diameter in Corn Using Digital Image Processing Techniques<\/a><\/span>
\n50th – <\/strong>Metagenomic Analysis of Diazotrophic Bacteria in Organic Soils of Paran\u00e1 State<\/a><\/span>
\n49th – <\/strong> Plant Species Identification Using a Combination of Classifiers<\/a><\/span>
\n48th – <\/strong>Development of a Temporary Immersion Bioreactor with Software and Hardware System for Data Management and Handling<\/a><\/span>
\n47th – <\/strong>Mathematical and Computational Models for Evaluating Nutritional Status of Soybean Through Leaf and Grain Diagnostics<\/a><\/span>
\n46th – <\/strong>Architecture and Implementation of Mobile GIS Based on Internet of Things Concepts<\/a><\/span>
\n45th – <\/strong>Tracking of Agrobots in Agricultural Greenhouses Using Hidden Markov Models: Comparison of Viterbi and Sliding Window Viterbi Algorithms Performance and Correction<\/a><\/span>
\n44rd – <\/strong>Digital Image Processing for Estimating Leaf Nitrogen Content in Corn Crop<\/a><\/span>
\n43nd – <\/strong>Procedure for Robot Self-Localization in Greenhouses Using SURF Descriptors: Sequential and Parallel Implementation<\/a><\/span>
\n42st – <\/strong>Trajectory Calculation Method for Agricultural Machinery Using Image Processing on Smartphones<\/a><\/span>
\n41th – <\/strong>Use of Digital Image Processing and Artificial Neural Networks for Recognizing Soybean Asian Rust Severity Indexes<\/a><\/span>
\n40th – <\/strong>Parallel k-Means Algorithm Based on Hadoop-MapReduce for Agricultural Data Mining<\/a><\/span>
\n39th – <\/strong>Meta-Analysis for Validation of Diagrammatic Scales in Plant Diseases and Proposal of an Assistant Software System<\/a><\/span>
\n38th – <\/strong>Hierarchical Visualization Applied to Meta-Analysis of Fluquinconazole Fungicide Efficacy in Soybean Seeds for Asian Rust Control<\/a><\/span>
\n37th – <\/strong>Feature Extraction from Images Applied to Detection of Burnt Corn Grains<\/a><\/span>
\n36th – <\/strong>Interest of Regression Models in Agricultural Data Mining<\/a><\/span>
\n35th – <\/strong>Computational Methodology for Defining Planting Periods of Agricultural Crops<\/a><\/span>
\n34rd – <\/strong>Analysis of a High-Resolution Orbital Image for Land Use and Cover Classification in the Pitangui Basin Area – PR<\/a><\/span>
\n33nd – <\/strong>Bayesian Probabilistic Model to Simulate Expert Knowledge in Controlling Asian Soybean Rust in Paran\u00e1 State<\/a><\/span>
\n32st – <\/strong>Determination of Carbon Content Estimation Model in Soils Using Support Vector Machine and Spectral Reflectance<\/a><\/span>
\n31th – <\/strong>Specification of a Framework for Grain Production Chain Traceability<\/a><\/span>
\n30th – <\/strong>Evaluation of Image Acquisition Methodologies for Corn Seed Quality Analysis<\/a><\/span>
\n29th – <\/strong>Computational Method for Evaluating Soybean Root Growth<\/a><\/span>
\n28th – <\/strong>Specification of a Framework for Grain Production Chain Traceability<\/a><\/span>
\n27th – <\/strong>Evaluation of Image Acquisition Methodologies for Corn Seed Quality Analysis<\/a><\/span>
\n26th – <\/strong>Computational Method for Evaluating Soybean Root Growth<\/a><\/span>
\n25th – <\/strong>Attribute Selection in Images Collected Under Uncontrolled Lighting Conditions and Its Influence on Naive Bayes Classifiers’ Performance for Object Identification in Agricultural Greenhouses<\/a><\/span>
\n24rd – <\/strong>Use of Digital Image Processing for Developing an Automated Environment for Testing Corn Seed Plantability<\/a><\/span>
\n23nd – <\/strong>Development of a Soil Moisture Sensor for Determining the Wetting Front<\/a><\/span>
\n22st – <\/strong>Evaluation of Soil Sampling Spacings for Granulometry Modeling with Application in Precision Agriculture<\/a><\/span>
\n21th – <\/strong>NAVIGO \u2013 Autonomous Robot for Agricultural Navigation<\/a><\/span>
\n20th – <\/strong>Impact of Sharpness Analysis on Wood Image Classification Methods<\/a><\/span>
\n19th – <\/strong>Application of Multiple Linear Regression in Analyzing the Dynamics of Exchangeable Cations in a Soil-Plant System Irrigated with Wastewater<\/a><\/span>
\n18th – <\/strong>Parallel Computing to Reduce Response Time in Agricultural Data Mining<\/a><\/span>
\n17th –<\/strong> Spectral Reflectance Sensors and Wheat Crop Performance in Respect to Nitrogen Fertilizer in Direct Planting<\/span>
\n16th – <\/strong>Use of Data Mining for Extracting Agronomic Knowledge on the Use of Agricultural Gypsum<\/a><\/span>15th – <\/strong>Evaluation of Vegetation Indices in Orbital Images<\/a><\/span>
\n14th – <\/strong>Analysis of Thresholding Techniques in Hydrosensitive Card Analysis Programs<\/a><\/span>
\n13th – <\/strong>Use of the Kinect Sensor to Measure Regularity in Solid Fertilizer Distribution<\/a><\/span>
\n12th – <\/strong>Application of Artificial Neural Networks in Processing Agro-Meteorological Data for Time Series Correction<\/a><\/span>
\n11th – <\/strong>Use of Multiple Regression in Studying Carbon, Nitrogen, and Phosphorus Dynamics in a Soil-Plant System Irrigated with Wastewater<\/a><\/span>
\n10th – <\/strong>Dager: A Computational Tool for Clustering in Georeferenced Agricultural Data Mining<\/a><\/span>
\n9th – <\/strong>Variable Selection in Agricultural Data Mining: A Principal Component Analysis-Based Approach<\/a><\/span>
\n8th – <\/strong>Microcontrolled Electronic Solution for Determining Longitudinal Distribution of Corn Seeds<\/a><\/span>
\n7th – <\/strong>Correction of Agro-Meteorological Data Using Statistical Methods<\/a><\/span>
\n6th – <\/strong>Variable Selection in Agricultural Data Mining: A Principal Component Analysis-Based Approach<\/a><\/span>
\n5th – <\/strong>Microcontrolled Electronic Solution for Determining Longitudinal Distribution of Corn Seeds<\/a><\/span>
\n4th – <\/strong>Filling Missing Data in Climate Time Series Using P2P Networks<\/a><\/span>
\n3rd – <\/strong>Georeferencing-Based Navigation System with Path Correction Using Fuzzy Logic Applied to Agricultural Robotics<\/a><\/span>
\n2nd – <\/strong>Georeferencing-Based Navigation System with Path Correction Using Fuzzy Logic Applied to Agricultural Robotics<\/a><\/span>
\n1st – <\/strong>Evaluation of Parallel Computing Using a P2P Network in Climate Data Simulation: Wind Speed and Direction<\/a><\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

101th – Few-shot learning evaluation for classifying soybean productivity images obtained by remotely piloted aircraft 100th – Classical and machine learning methods for predicting fresh tomato prices 99th – Development of a tool to identify ribosomal proteins in MALDI-TOF mass spectra 98th – Electronic nose system applied to the analysis of flowering and thinning in …<\/p>\n","protected":false},"author":321,"featured_media":1519,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www2.uepg.br\/computacaoaplicada\/wp-json\/wp\/v2\/pages\/2390"}],"collection":[{"href":"https:\/\/www2.uepg.br\/computacaoaplicada\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www2.uepg.br\/computacaoaplicada\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www2.uepg.br\/computacaoaplicada\/wp-json\/wp\/v2\/users\/321"}],"replies":[{"embeddable":true,"href":"https:\/\/www2.uepg.br\/computacaoaplicada\/wp-json\/wp\/v2\/comments?post=2390"}],"version-history":[{"count":3,"href":"https:\/\/www2.uepg.br\/computacaoaplicada\/wp-json\/wp\/v2\/pages\/2390\/revisions"}],"predecessor-version":[{"id":2597,"href":"https:\/\/www2.uepg.br\/computacaoaplicada\/wp-json\/wp\/v2\/pages\/2390\/revisions\/2597"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www2.uepg.br\/computacaoaplicada\/wp-json\/wp\/v2\/media\/1519"}],"wp:attachment":[{"href":"https:\/\/www2.uepg.br\/computacaoaplicada\/wp-json\/wp\/v2\/media?parent=2390"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}