Percorrer por autor "Paula Filho, Pedro L. de"
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- Assessment of honey bee cells using deep learningPublication . Alves, Thiago da Silva; Ventura, Paulo J.C.; Neves, Cátia J.; Candido Junior, Arnaldo; Paula Filho, Pedro L. de; Pinto, M. Alice; Rodrigues, Pedro JoãoTemporal assessment of honey bee colony strength is required for different applications in many research projects. This task often requires counting the number of cells with brood and food reserves multiple times a year from images taken in the apiary. There are thousands of cells in each frame, which makes manual counting a time-consuming and tedious activity. Thus, the assessment of frames has been frequently been performed in the apiary in an approximate way by using methods such as the Liebefeld. The automation of this process using modern imaging processing techniques represents a major advance. The objective of this work was to develop a software capable of extracting each cell from frame images, classify its content and display the results to the researcher in a simple way. The cells’ contents display a high variation of patterns which added to light variation make their classification by software a challenging endeavor. To address this challenge, we used Deep Neural Networks (DNNs) for image processing. DNNs are known by achieving the state-of-art in many fields of study including image classification, because they can learn features that best describe the content being classified, such as the interior of frame cells. Our DNN model was trained with over 60,000 manually labeled images whose cells were classified into seven classes: egg, larvae, capped larvae, honey, nectar, pollen, and empty. Our contribution is an end-to-end software capable of doing automatic background removal, cell detection, and classification of its content based on an input image. With this software the researcher is able to achieve an average accuracy of 94% over all classes and get better results compared with approximation methods and previous techniques that used handmade features like color and texture.
- Automatic detection and classification of honey bee comb cells using deep learningPublication . Alves, Thiago da Silva; Pinto, M. Alice; Ventura, Paulo J.C.; Neves, Cátia J.; Biron, David G.; Candido Junior, Arnaldo; Paula Filho, Pedro L. de; Rodrigues, Pedro JoãoIn a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3; MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed to be the best compromise between training cost, with ~9 s for processing all cells in a comb image, and accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBeesource).
- Classificação do conteúdo de favos em quadros de colmeias usando Deep LearningPublication . Alves, Thiago da Silva; Ventura, Paulo J.C.; Neves, Cátia J.; Pinto, M. Alice; Candido Junior, Arnaldo; Paula Filho, Pedro L. de; Rodrigues, Pedro JoãoNo âmbito de várias tarefas da investigação apícola, existe uma que obriga o investigador a classificar e contar o conteúdo de cada favo em cada quadro da colmeia. Esta tarefa tem por objetivo analisar e controlar a progressão da criação, de abelhas, e de reservas, o que implica repeti-la múltiplas vezes a cada ano. Cada quadro contém milhares de favos o que leva a que a contagem, na maior parte dos casos, seja feita de forma aproximada. Os favos podem conter: pupas (criação fechada), larvas em diferentes fases de maturação, mel, néctar, pólen, ovos, ou então podem estar vazios. A automatização deste processo, com o auxílio de um sistema computacional, representa uma importante evolução na referida tarefa. O presente trabalho aborda a classificação automática do conteúdo de favos a partir de imagens digitais. Arquiteturas neuronais de Deep Learning têm mostrado um bom potencial a classificar padrões que exibem elevada variabilidade visual. Assim, a utilização deste método de aprendizagem máquina adequa-se à complexidade e variabilidade visual dos padrões apresentados pelas imagens dos favos. No modelo desenvolvido neste trabalho foi utilizada a arquitetura neuronal GoogleNet. Esta foi treinada utilizando 63344 imagens anotadas e separadas nas sete classes referidas. A taxa média de acerto do modelo sobre o conjunto de validação foi de 94% o que melhora substancialmente o resultado obtido com técnicas clássicas (SVM - 76%). Este estudo foi financiado pelo projeto BEEHOPE através do concurso conjunto 2013-2014 BiodivErsA/FACCE-JPI pela FCT (Portugal), CNRS (França) e MEC (Espanha).