ArticleHorticulture research2021
Easy domain adaptation method for filling the species gap in deep learning-based fruit detection.
Article in Horticulture research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 37 citations in OpenAlex.
- Postharvest Fruit Grading Technologies and Equipment: A Review.Foods (Basel, Switzerland) · 2026Review
- A knowledge-driven unified framework for plant disease classification and severity grading via domain adaptation.Plant phenomics (Washington, D.C.) · 2026Article
- Integrating Kolmogorov-Arnold networks and sparse attention for robust visual plant disease symptom identification across diverse agricultural crops.Frontiers in plant science · 2026Article
- Real-Time Accurate Apple Detection Based on Improved YOLOv8n in Complex Natural Environments.Plants (Basel, Switzerland) · 2025Article
- A spontaneous keypoints connection algorithm for leafy plants skeletonization and phenotypes extraction.Frontiers in plant science · 2025Article
- Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.Frontiers in plant science · 2025Review
- DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments.Plant methods · 2024Article
- EasyDAM_V4: Guided-GAN-based cross-species data labeling for fruit detection with significant shape difference.Horticulture research · 2024Article
- DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling.Plant phenomics (Washington, D.C.) · 2024Article
- In Situ Root Dataset Expansion Strategy Based on an Improved CycleGAN Generator.Plant phenomics (Washington, D.C.) · 2024Article
- Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage.Plant phenomics (Washington, D.C.) · 2023Article
- Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models.Plant phenomics (Washington, D.C.) · 2023Article
- EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph.Plant phenomics (Washington, D.C.) · 2023Article
- Real-Time Prediction of Growth Characteristics for Individual Fruits Using Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment.Plant phenomics (Washington, D.C.) · 2022Article
- EasyDAM_V2: Efficient Data Labeling Method for Multishape, Cross-Species Fruit Detection.Plant phenomics (Washington, D.C.) · 2022Article
- Domain Adaptation of Synthetic Images for Wheat Head Detection.Plants (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Fruit detection and counting are essential tasks for horticulture research. With computer vision technology development, fruit detection techniques based on deep learning have been widely used in modern orchards. However, most deep learning-based fruit detection models are generated based on fully supervised approaches, which means a model trained with one domain species may not be transferred to another. There is always a need to recreate and label the relevant training dataset, but such a procedure is time-consuming and labor-intensive. This paper proposed a domain adaptation method that can transfer an existing model trained from one domain to a new domain without extra manual labeling. The method includes three main steps: transform the source fruit image (with labeled information) into the target fruit image (without labeled information) through the CycleGAN network; Automatically label the target fruit image by a pseudo-label process; Improve the labeling accuracy by a pseudo-label self-learning approach. Use a labeled orange image dataset as the source domain, unlabeled apple and tomato image dataset as the target domain, the performance of the proposed method from the perspective of fruit detection has been evaluated. Without manual labeling for target domain image, the mean average precision reached 87.5% for apple detection and 76.9% for tomato detection, which shows that the proposed method can potentially fill the species gap in deep learning-based fruit detection.
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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.