ArticleSensors (Basel, Switzerland)2020
YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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60 citing papers in PubMed, 403 citations in OpenAlex.
- A real-time ripeness detection model for tomatoes in complex greenhouse environments.Frontiers in plant science · 2026Article
- Artificial Intelligence in Edible Mushroom Cultivation, Breeding, and Classification: A Comprehensive Review.Journal of fungi (Basel, Switzerland) · 2025Review
- Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability.Scientific reports · 2025Article
- YOLO-RGDD: A Novel Method for the Online Detection of Tomato Surface Defects.Foods (Basel, Switzerland) · 2025Article
- An improved chilli pepper flower detection approach based on YOLOv8.Plant methods · 2025Article
- Circle Detection with Adaptive Parameterization: A Bottom-Up Approach.Sensors (Basel, Switzerland) · 2025Article
- Design of tomato picking robot detection and localization system based on deep learning neural networks algorithm of Yolov5.Scientific reports · 2025Article
- Evaluating deep learning techniques for optimal neurons counting and characterization in complex neuronal cultures.Medical & biological engineering & computing · 2025Article
- Tomato ripeness and stem recognition based on improved YOLOX.Scientific reports · 2025Article
- Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.Frontiers in plant science · 2025Article
- MDE-DETR: multi-domain enhanced feature fusion algorithm for bayberry detection and counting in complex orchards.Frontiers in plant science · 2025Article
- An anchor-based YOLO fruit detector developed on YOLOv5.PloS one · 2025Article
- Ta-YOLO: overcoming target blocked challenges in greenhouse tomato detection and counting.Frontiers in plant science · 2025Article
- AITP-YOLO: improved tomato ripeness detection model based on multiple strategies.Frontiers in plant science · 2025Article
- A review of visual perception technology for intelligent fruit harvesting robots.Frontiers in plant science · 2025Review
- A novel approach combining YOLO and DeepSORT for detecting and counting live fish in natural environments through video.PloS one · 2025Article
- GAE-YOLO: a lightweight multimodal detection framework for tomato smart agriculture with edge computing.Frontiers in plant science · 2025Article
- Comparative analysis of YOLO models for green coffee bean detection and defect classification.Scientific reports · 2024Article
- FF3D: A Rapid and Accurate 3D Fruit Detector for Robotic Harvesting.Sensors (Basel, Switzerland) · 2024Article
- YOLO_Bolt: a lightweight network model for bolt detection.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic fruit detection is a very important benefit of harvesting robots. However, complicated environment conditions, such as illumination variation, branch, and leaf occlusion as well as tomato overlap, have made fruit detection very challenging. In this study, an improved tomato detection model called YOLO-Tomato is proposed for dealing with these problems, based on YOLOv3. A dense architecture is incorporated into YOLOv3 to facilitate the reuse of features and help to learn a more compact and accurate model. Moreover, the model replaces the traditional rectangular bounding box (R-Bbox) with a circular bounding box (C-Bbox) for tomato localization. The new bounding boxes can then match the tomatoes more precisely, and thus improve the Intersection-over-Union (IoU) calculation for the Non-Maximum Suppression (NMS). They also reduce prediction coordinates. An ablation study demonstrated the efficacy of these modifications. The YOLO-Tomato was compared to several state-of-the-art detection methods and it had the best detection performance.
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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.