ArticleSensors (Basel, Switzerland)2020
Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques.
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 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 113 citations in OpenAlex.
- Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects.Sensors (Basel, Switzerland) · 2022Pooled it
- TMAF-YOLO: A Lightweight Model for In Situ Detection of Tomato Maturity and Defective Fruits in Greenhouses.Sensors (Basel, Switzerland) · 2026Article
- A real-time ripeness detection model for tomatoes in complex greenhouse environments.Frontiers in plant science · 2026Article
- A real-time detection method for classification and grading of multi-variety shiitake mushrooms in natural growth environments.Frontiers in plant science · 2026Article
- Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability.Scientific reports · 2025Article
- Optimizing RetinaNet anchors using differential evolution for improved object detection.Scientific reports · 2025Article
- Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS.Sensors (Basel, Switzerland) · 2025Article
- Article
- YOLOv8-CML: a lightweight target detection method for color-changing melon ripening in intelligent agriculture.Scientific reports · 2024Article
- DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling.Plant phenomics (Washington, D.C.) · 2024Article
- A lightweight Color-changing melon ripeness detection algorithm based on model pruning and knowledge distillation: leveraging dilated residual and multi-screening path aggregation.Frontiers in plant science · 2024Article
- Multi-stage tomato fruit recognition method based on improved YOLOv8.Frontiers in plant science · 2024Article
- EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph.Plant phenomics (Washington, D.C.) · 2023Article
- An efficient tomato-detection method based on improved YOLOv4-tiny model in complex environment.Frontiers in plant science · 2023Article
- An accurate green fruits detection method based on optimized YOLOX-m.Frontiers in plant science · 2023Article
- Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series Tracking.Plant phenomics (Washington, D.C.) · 2023Article
- Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting.Foods (Basel, Switzerland) · 2022Article
- PDC: Pearl Detection with a Counter Based on Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Intelligent yield estimation for tomato crop using SegNet with VGG19 architecture.Scientific reports · 2022Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Automatic detection of intact tomatoes on plants is highly expected for low-cost and optimal management in tomato farming. Mature tomato detection has been wildly studied, while immature tomato detection, especially when occluded with leaves, is difficult to perform using traditional image analysis, which is more important for long-term yield prediction. Therefore, tomato detection that can generalize well in real tomato cultivation scenes and is robust to issues such as fruit occlusion and variable lighting conditions is highly desired. In this study, we build a tomato detection model to automatically detect intact green tomatoes regardless of occlusions or fruit growth stage using deep learning approaches. The tomato detection model used faster region-based convolutional neural network (R-CNN) with Resnet-101 and transfer learned from the Common Objects in Context (COCO) dataset. The detection on test dataset achieved high average precision of 87.83% (intersection over union ≥ 0.5) and showed a high accuracy of tomato counting (R
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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.