Abstract
This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.
Highlights
The latest advances in sensing technologies dedicated to agricultural systems have led to the emergence and development of a modern management concept, namely precision agriculture, which focuses on efficient management of the temporal and spatial variability of field and crop properties using information and communication technology (ICT) [1]
The potential uses of tree segmentation cover a variety of applications, such as, for example, mapping orchard environments in order to identify the coordinates of tree trunks for autonomous ground vehicle navigation
These operations are crucial for the age of precision agriculture, in which on-field visual inspection by experts will be less frequent, or extensive and less time-consuming
Summary
The latest advances in sensing technologies dedicated to agricultural systems have led to the emergence and development of a modern management concept, namely precision agriculture, which focuses on efficient management of the temporal and spatial variability of field and crop properties using information and communication technology (ICT) [1]. Recent technological advances have made unmanned aerial systems (UASs), i.e., sensing systems mounted on unmanned aerial vehicles (UAVs), commercially available. These systems provide high spatial resolution images and, in combination with their ease of use, quick acquisition times, and low operational cost, they have become popular for monitoring agricultural fields [3]. Several studies have utilized UASs for crop management purposes, such as yield prediction and site-specific fertilization [4] by capturing multispectral images, irrigation using thermal imaging [5], or for field scouting using RGB (Red-Green-Blue) orthomosaics [6]
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