ArticleFrontiers in artificial intelligence2020
Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest.
Article in Frontiers in artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The trial behind it
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Who cites it
26 citing papers in PubMed.
- Grafting on resistant rootstocks alleviates salt stress on tomato by enhancing morpho-physiological and biochemical traits.Protoplasma · 2026Article
- Proactive Irrigation Timing Decision-Making for Greenhouse Tomatoes via STL-LSTM Deep Learning and Plant-Soil Dual-Threshold Sensing.Sensors (Basel, Switzerland) · 2026Article
- Digitalised phenotyping of pepper (Capsicum spp.) using effective RGB imaging and optimised camera positioning.Scientific reports · 2026Article
- Article
- Genotypic difference in yield response to far-red light in dwarf tomato: explained by fruit sink and source strength and predicted by seedlings' height plasticity.Frontiers in plant science · 2026Article
- A digital twin-driven deep learning framework for online quality inspection in tobacco transplanting.Frontiers in plant science · 2026Article
- Photosynthetic performance index (PIabs) and malondialdehyde (MDA) content determine rice biomass under combined salt stress and prohexadione-calcium treatment.BMC plant biology · 2025Article
- Water status and plant traits of dry bean assessment using integrated spectral reflectance and RGB image indices with artificial intelligence.Scientific reports · 2025Article
- A Portable High-Resolution Snapshot Multispectral Imaging Device Leveraging Spatial and Spectral Features for Non-Invasive Corn Nitrogen Treatment Classification.Sensors (Basel, Switzerland) · 2025Article
- Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network.Frontiers in plant science · 2025Article
- Cluster segmentation and stereo vision-based apple localization algorithm for robotic harvesting.Frontiers in plant science · 2025Article
- Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Frontiers in plant science · 2025Article
- Quantifying mangrove carbon assimilation rates using UAV imagery.Scientific reports · 2024Article
- Revitalizing agriculture: next-generation genotyping and -omics technologies enabling molecular prediction of resilient traits in the Solanaceae family.Frontiers in plant science · 2024Review
- Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images.Frontiers in plant science · 2024Article
- Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data.Plant methods · 2023Article
- Image-Based High-Throughput Phenotyping in Horticultural Crops.Plants (Basel, Switzerland) · 2023Review
- Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods.Frontiers in plant science · 2023Review
- A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomass and yield are key variables for assessing the production and performance of agricultural systems. Modeling and predicting the biomass and yield of individual plants at the farm scale represents a major challenge in precision agriculture, particularly when salinity and other abiotic stresses may play a role. Here, we evaluate a diversity panel of the wild tomato species (
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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.