Lucas Prado Osco
University of Western Ontario
66 Papers
32 Citations
Lucas Prado Osco is an academic researcher from University of Western Ontario. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 9, co-authored 46 publications. Previous affiliations of Lucas Prado Osco include Federal University of Mato Grosso do Sul.
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Papers
A Review on Deep Learning in UAV Remote Sensing
Lucas Prado Osco,José Marcato Junior,Ana Paula Marques Ramos,Lúcio André de Castro Jorge,Sarah Narges Fatholahi,Jonathan de Andrade Silva,Edson Takashi Matsubara,Hemerson Pistori,Hemerson Pistori,Wesley Nunes Gonçalves,Jonathan Li +10 more
TL;DR: In this paper, the authors present a comprehensive review of the fundamentals of deep learning applied in UAV-based imagery, focusing mainly on describing the classification and regression techniques used in recent applications with UAV acquired data.
304
A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices
Ana Paula Marques Ramos,Lucas Prado Osco,Danielle Elis Garcia Furuya,Wesley Nunes Gonçalves,Dthenifer Cordeiro Santana,Larissa Pereira Ribeiro Teodoro,Carlos Antonio da Silva Junior,Guilherme Fernando Capristo-Silva,Jonathan Li,Fabio Henrique Rojo Baio,José Marcato Junior,Paulo Eduardo Teodoro,Hemerson Pistori,Hemerson Pistori +13 more
TL;DR: It is demonstrated that the ranking-based approach to potentialize the RF method for maize yield prediction reduces the number of VIs needed to determine a high accuracy and relative low MAE, and the approach may contribute to decision-making actions, resulting in accurate management of maize fields.
217
A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery
Lucas Prado Osco,Mauro dos Santos de Arruda,José Marcato Junior,Neemias Buceli da Silva,Ana Paula Marques Ramos,Érika Akemi Saito Moryia,Nilton Nobuhiro Imai,Danillo Roberto Pereira,José Eduardo Creste,Edson Takashi Matsubara,Jonathan Li,Wesley Nunes Gonçalves +11 more
TL;DR: The convolutional neural network approach developed to estimate the number and geolocation of citrus trees in high-density orchards is satisfactory and is an effective strategy to replace the traditional visual inspection method to determine the number of plants in orchard trees.
175
Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery
Lucas Prado Osco,Ana Paula Marques Ramos,Danillo Roberto Pereira,Érika Akemi Saito Moriya,Nilton Nobuhiro Imai,Edson Takashi Matsubara,Nayara Vasconcelos Estrabis,Maurício de Souza,José Marcato Junior,Wesley Nunes Gonçalves,Jonathan Li,Veraldo Liesenberg,José Eduardo Creste +12 more
TL;DR: This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm and demonstrates that the approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.
117
A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements
Lucas Prado Osco,Ana Paula Marques Ramos,Mayara Maezano Faita Pinheiro,Érika Akemi Saito Moriya,Nilton Nobuhiro Imai,Nayara Vasconcelos Estrabis,Felipe Ianczyk,Fabio Fernando de Araujo,Veraldo Liesenberg,Lúcio André de Castro Jorge,Jonathan Li,Lingfei Ma,Wesley Nunes Gonçalves,José Marcato Junior,José Eduardo Creste +14 more
TL;DR: The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrient content, while first-derivative spectra is better linked to micronutrients.
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