ArticleFrontiers in plant science2020
Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 231 citations in OpenAlex.
- Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects.Sensors (Basel, Switzerland) · 2022Pooled it
- Plant counting: origins, development, and future paths.Plant phenomics (Washington, D.C.) · 2026Review
- VT-SCC: Visual-text semantic alignment via multimodal guidance for unified staple crop organ counting.aBIOTECH · 2026Article
- Leveraging generative adversarial networks and SE-UNet for high-precision tobacco leaf image segmentation and blend uniformity detection.Scientific reports · 2026Article
- Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability.Scientific reports · 2025Article
- Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8.Scientific reports · 2025Article
- Fruit size prediction of tomato cultivars using machine learning algorithms.Frontiers in plant science · 2025Article
- Optimizing polymorphic tomato picking detection: improved YOLOv8n architecture to tackle data under complex environments.Frontiers in plant science · 2025Article
- Article
- DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.Frontiers in plant science · 2025Article
- GAE-YOLO: a lightweight multimodal detection framework for tomato smart agriculture with edge computing.Frontiers in plant science · 2025Article
- Deep learning for accurate classification of conifer pollen grains: enhancing species identification in palynology.Frontiers in big data · 2025Article
- BerryPortraits: Phenotyping Of Ripening Traits cranberry (Vaccinium macrocarpon Ait.) with YOLOv8.Plant methods · 2024Article
- Application of amodal segmentation for shape reconstruction and occlusion recovery in occluded tomatoes.Frontiers in plant science · 2024Article
- Deep learning-based elaiosome detection in milk thistle seed for efficient high-throughput phenotyping.Frontiers in plant science · 2024Article
- Lightweight tomato ripeness detection algorithm based on the improved RT-DETR.Frontiers in plant science · 2024Article
- Toward Real Scenery: A Lightweight Tomato Growth Inspection Algorithm for Leaf Disease Detection and Fruit Counting.Plant phenomics (Washington, D.C.) · 2024Article
- Tomato maturity recognition with convolutional transformers.Scientific reports · 2023Article
- Feature enhancement guided network for yield estimation of high-density jujube.Plant methods · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately detecting and counting fruits during plant growth using imaging and computer vision is of importance not only from the point of view of reducing labor intensive manual measurements of phenotypic information, but also because it is a critical step toward automating processes such as harvesting. Deep learning based methods have emerged as the state-of-the-art techniques in many problems in image segmentation and classification, and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. This paper reports results on the detection of tomatoes in images taken in a greenhouse, using the MaskRCNN algorithm, which detects objects and also the pixels corresponding to each object. Our experimental results on the detection of tomatoes from images taken in greenhouses using a RealSense camera are comparable to or better than the metrics reported by earlier work, even though those were obtained in laboratory conditions or using higher resolution images. Our results also show that MaskRCNN can implicitly learn object depth, which is necessary for background elimination.
Indexed as
Identifiers
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.