Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing.

Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits. The main goal of this study was to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. For this, an experiment composed of 330 plots of full-sib families and checks conducted at Embrapa Beef Cattle, Brazil, was used. The image dataset was composed of images obtained with an RGB sensor embedded in a Phantom 4 PRO. The traits leaf dry matter yield (LDMY) and total dry matter yield (TDMY) were obtained by conventional agronomic methodology and considered as the ground-truth data. Different CNN architectures were analyzed, such as AlexNet, ResNeXt50, DarkNet53, and two networks proposed recently for related tasks named MaCNN and LF-CNN. Pretrained AlexNet and ResNeXt50 architectures were also studied. Ten-fold cross-validation was used for training and testing the model. Estimates of DMY traits by each CNN architecture were considered as new HTP traits to compare with real traits. Pearson correlation coefficient r between real and HTP traits ranged from 0.62 to 0.79 for LDMY and from 0.60 to 0.76 for TDMY; root square mean error (RSME) ranged from 286.24 to 366.93 kg·ha-1 for LDMY and from 413.07 to 506.56 kg·ha-1 for TDMY. All the CNNs generated heritable HTP traits, except LF-CNN for LDMY and AlexNet for TDMY. Genetic correlations between real and HTP traits were high but varied according to the CNN architecture. HTP trait from ResNeXt50 pretrained achieved the best results for indirect selection regardless of the dry matter trait. This demonstrates that CNNs with remote sensing data are highly promising for HTP for dry matter yield traits in forage breeding programs.

Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs.

Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.

Soroepidemiologia da sífilis em mulheres submetidas a tratamento específico durante puerpério anterior / Seraepidemiology of syphilis in womwn submitted to specific treatment in an anterior puerperium

Objetivo: interpretar a sorologia anti-sifilítica e aspectos epidemiológicos de mulheres já submetidas a tratamento específico durante puerpério ocorrido há um ano. Método: doze mulheres diagnosticadas e tratadas para sífilis durante o puerpério na Fundação Santa Casa de Misericórdia do Pará (FSCMPA), no período de janeiro a agosto de 1999, foram, após um ano, novamente avaliadas para reinfecção pelo Treponema pallidum (Tp). Após obtenção do Termo de Consentimento Livre e Esclarecido (TCLE), procedeu-se a entrevista e coleta de 5 ml de sangue periférico. Testes (VDRL) realizados no Laboratório de Análises Clínicas do Departamento de Pediatria da Universidade Federal do Pará (UFPA). Dados analisados através do softwere Bioestat 3.0 e submetidos ao teste do qui-quadrado com valor de p<0,05. Resultados: 66,7por cento das pacientes se re-infectaram; 75por cento tinham de 21 a 29 anos; 87,5por cento confessaram relacionamento sexual com mais de um parceiro; metade das pacientes informou ter tido um episódio de aborto; 75por cento delas não exerciam atividade remunerada; 12,5por cento confessaram consumir drogas ilícitas (cocaína); 37,5por cento das pacientes apresentaram títulos de VDRL iguais a 1:32; 25por cento dos companheiros das mulheres reinfectadas tiveram sorologia não reagente para Tp. Conclusão: o elevado percentual de mulheres re-infectadas, em faixa etária propícia à procriação, sinaliza para um risco potencial do nascimento de crianças com sífilis. Fatores relacionados à transmissão da doença como, promiscuidade sexual (87,5por cento) e drogadição (12,5por cento), estiveram presentes de forma expressiva na amostra estudada