Automatic Tobacco Plant Detection in UAV Images via Deep Neural Networks

Abstract

Tobacco plant detection plays an important role in the management of tobacco planting. In this paper, a new algorithm based on deep neural networks is proposed to detect tobacco plants in images captured by unmanned aerial vehicles (UAVs) (called UAV images). These UAV images are characterized by a very high spatial resolution (35 $\text{mm}$ ), and consequently contain an extremely high level of detail for the development of automatic detection algorithms. The proposed algorithm consists of three stages. In the first stage, a number of candidate tobacco plant regions are extracted from UAV images with the morphological operations and watershed segmentation. Each candidate region contains a tobacco plant or a nontobacco plant. In the second stage, a deep convolutional neural network is built and trained with the purpose of classifying the candidate regions as tobacco plant regions or nontobacco plant regions. In the third stage, postprocessing is performed to further remove the nontobacco plant regions. The proposed algorithm is evaluated on a UAV image dataset. The experimental results show that the proposed algorithm performs well on the detection of tobacco plants in UAV images.