ArticleHorticulture research2019
Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production.
Article in Horticulture research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 154 citations in OpenAlex.
- Recent Advancements and Challenges of AIoT Application in Smart Agriculture: A Review.Sensors (Basel, Switzerland) · 2023Pooled it
- PhenoRob-P: An autonomous robotic system for high-throughput phenotyping of potted plants.Plant phenomics (Washington, D.C.) · 2026Article
- Image-Based Assessment of External Quality Traits in Chinese Bayberry (Foods (Basel, Switzerland) · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas.Scientific reports · 2025Article
- Article
- IHUP: An Integrated High-Throughput Universal Phenotyping Software Platform to Accelerate Unmanned-Aerial-Vehicle-Based Field Plant Phenotypic Data Extraction and Analysis.Plant phenomics (Washington, D.C.) · 2024Article
- Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.Plant phenomics (Washington, D.C.) · 2024Article
- From Images to Loci: Applying 3D Deep Learning to Enable Multivariate and Multitemporal Digital Phenotyping and Mapping the Genetics Underlying Nitrogen Use Efficiency in Wheat.Plant phenomics (Washington, D.C.) · 2024Article
- GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat.Frontiers in plant science · 2024Article
- GSP-AI: An AI-Powered Platform for Identifying Key Growth Stages and the Vegetative-to-Reproductive Transition in Wheat Using Trilateral Drone Imagery and Meteorological Data.Plant phenomics (Washington, D.C.) · 2024Article
- An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture.Biology · 2023Review
- Lettuce Production in Intelligent Greenhouses-3D Imaging and Computer Vision for Plant Spacing Decisions.Sensors (Basel, Switzerland) · 2023Article
- The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat.Plant phenomics (Washington, D.C.) · 2023Article
- CropQuant-Air: an AI-powered system to enable phenotypic analysis of yield- and performance-related traits using wheat canopy imagery collected by low-cost drones.Frontiers in plant science · 2023Article
- Explainable deep learning in plant phenotyping.Frontiers in artificial intelligence · 2023Review
- Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income.Plant phenomics (Washington, D.C.) · 2023Article
- Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation.Sensors (Basel, Switzerland) · 2022Article
- AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice.The New phytologist · 2022Article
- Online recognition and yield estimation of tomato in plant factory based on YOLOv3.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Aerial imagery is regularly used by crop researchers, growers and farmers to monitor crops during the growing season. To extract meaningful information from large-scale aerial images collected from the field, high-throughput phenotypic analysis solutions are required, which not only produce high-quality measures of key crop traits, but also support professionals to make prompt and reliable crop management decisions. Here, we report AirSurf, an automated and open-source analytic platform that combines modern computer vision, up-to-date machine learning, and modular software engineering in order to measure yield-related phenotypes from ultra-large aerial imagery. To quantify millions of in-field lettuces acquired by fixed-wing light aircrafts equipped with normalised difference vegetation index (NDVI) sensors, we customised AirSurf by combining computer vision algorithms and a deep-learning classifier trained with over 100,000 labelled lettuce signals. The tailored platform, AirSurf-
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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.