ArticleSensors (Basel, Switzerland)2021
Orchard Mapping with Deep Learning Semantic Segmentation.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Application of Image Computing in Non-Destructive Detection of Chinese Cuisine.Foods (Basel, Switzerland) · 2025Review
- An improved 3D-UNet-based brain hippocampus segmentation model based on MR images.BMC medical imaging · 2024Article
- An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture.Nature communications · 2024Article
- Sensors and Robotics for Digital Agriculture.Sensors (Basel, Switzerland) · 2023Article
- Sensor-Driven Human-Robot Synergy: A Systems Engineering Approach.Sensors (Basel, Switzerland) · 2022Article
- Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments.Sensors (Basel, Switzerland) · 2022Article
- An Advanced Noise Reduction and Edge Enhancement Algorithm.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.