ArticleSensors (Basel, Switzerland)2021
3D Reconstruction of Non-Rigid Plants and Sensor Data Fusion for Agriculture Phenotyping.
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 13 papers.
What it found
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Who cites it
13 citing papers in PubMed, 31 citations in OpenAlex.
- Embedding of ripening topology into one-stage detection for tomato cluster phenotyping.Journal of Zhejiang University. Science. B · 2026Article
- 2D-to-3D Image Reconstruction in Agriculture: A Review of Methods, Challenges, and AI-Driven Opportunities.Sensors (Basel, Switzerland) · 2026Review
- Advancements in 3D field-crop phenotyping using point clouds: a comparative review of sensor technology, target traits, and challenges under controlled and field conditions.Frontiers in plant science · 2026Review
- Smart Image-Based Deep Learning System for Automated Quality Grading of Phalaenopsis Seedlings in Outsourced Production.Sensors (Basel, Switzerland) · 2025Article
- 3DPotatoTwin: a paired potato tuber dataset for 3D multi-sensory fusion.Plant phenomics (Washington, D.C.) · 2025Article
- Comparative analysis of adaptive and general labeling methods for soybean leaf detection.Frontiers in plant science · 2025Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping.Plants (Basel, Switzerland) · 2023Review
- Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps.Sensors (Basel, Switzerland) · 2022Review
- A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots.Plant phenomics (Washington, D.C.) · 2022Review
- Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics.Journal of imaging · 2021Article
- Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Technology has been promoting a great transformation in farming. The introduction of robotics; the use of sensors in the field; and the advances in computer vision; allow new systems to be developed to assist processes, such as phenotyping, of crop's life cycle monitoring. This work presents, which we believe to be the first time, a system capable of generating 3D models of non-rigid corn plants, which can be used as a tool in the phenotyping process. The system is composed by two modules: an terrestrial acquisition module and a processing module. The terrestrial acquisition module is composed by a robot, equipped with an RGB-D camera and three sets of temperature, humidity, and luminosity sensors, that collects data in the field. The processing module conducts the non-rigid 3D plants reconstruction and merges the sensor data into these models. The work presented here also shows a novel technique for background removal in depth images, as well as efficient techniques for processing these images and the sensor data. Experiments have shown that from the models generated and the data collected, plant structural measurements can be performed accurately and the plant's environment can be mapped, allowing the plant's health to be evaluated and providing greater crop efficiency.
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What OpenQuestion holds
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.