Counting of shoots of Eucalyptus sp. clones with convolutional neural network

O objetivo deste trabalho foi investigar o uso do modelo de rede neural convolucional You Only Look Once (YOLO) para detecção e contagem eficiente de brotos de Eucalyptus sp. em plantações, por meio de fotografias aéreas capturadas por veículos aéreos não tripulados. Para isso, avaliou-se a importância da organização dos dados durante o processo de treinamento do sistema. Foram utilizados dois conjunto de dados para treinar a rede neural convolucional: um consistindo em imagens com um único broto e o outro com pelo menos dez brotos por imagem. Os resultados mostraram altas taxas de precisão e recall para ambos os conjuntos de dados. A rede neural convolucional treinada com imagens contendo dez brotos por imagem apresentou desempenho superior quando aplicada a dados não utilizados durante o treinamento. Portanto, a rede neural convolucional YOLO pode ser usada para detecção e contagem de brotos de clones de Eucalyptus sp. a partir de imagens aéreas capturadas por veículos aéreos não tripulados em áreas florestais. Recomenda-se o uso de imagens contendo dez brotos para compor o conjunto de dados de treinamento para o detector de objetos.The objective of this work was to investigate the use of the You Only Look Once (YOLO) convolutional neural network model for the detection and efficient counting of Eucalyptus sp. shoots in stands through aerial photographs captured by unmanned aerial vehicles. For this, the significance of data organization was evaluated during the system-training process. Two datasets were used to train the convolutional neural network: one consisting of images with a single shoot and another with at least ten shoots per image. The results showed high precision and recall rates for both datasets. The convolutional neural network trained with images containing ten shoots per image showed a superior performance when applied to data not used during training. Therefore, the YOLO convolutional neural network can be used for the detection and counting of shoots of Eucalyptus sp. clones from aerial images captured by unmanned aerial vehicles in forest stands. The use of images containing ten shoots is recommended to compose the training dataset for the object detector

Counting of shoots of Eucalyptus sp. clones with convolutional neural network

Abstract The objective of this work was to investigate the use of the You Only Look Once (YOLO) convolutional neural network model for the detection and efficient counting of Eucalyptus sp. shoots in stands through aerial photographs captured by unmanned aerial vehicles. For this, the significance of data organization was evaluated during the system-training process. Two datasets were used to train the convolutional neural network: one consisting of images with a single shoot and another with at least ten shoots per image. The results showed high precision and recall rates for both datasets. The convolutional neural network trained with images containing ten shoots per image showed a superior performance when applied to data not used during training. Therefore, the YOLO convolutional neural network can be used for the detection and counting of shoots of Eucalyptus sp. clones from aerial images captured by unmanned aerial vehicles in forest stands. The use of images containing ten shoots is recommended to compose the training dataset for the object detector