ReviewFrontiers in plant science2022
Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review.
Review in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 65 citations in OpenAlex.
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- TRD-Net: an efficient tomato ripeness detection network based on improved YOLO v8 for selective harvesting.Frontiers in plant science · 2026Article
- Multi-Task NoisyViT for Enhanced Fruit and Vegetable Freshness Detection and Type Classification.Sensors (Basel, Switzerland) · 2025Article
- Food Image Recognition Based on Anti-Noise Learning and Covariance Feature Enhancement.Foods (Basel, Switzerland) · 2025Article
- Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention Mechanism.Foods (Basel, Switzerland) · 2025Article
- Cluster segmentation and stereo vision-based apple localization algorithm for robotic harvesting.Frontiers in plant science · 2025Article
- RLK-YOLOv8: multi-stage detection of strawberry fruits throughout the full growth cycle in greenhouses based on large kernel convolutions and improved YOLOv8.Frontiers in plant science · 2025Article
- Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- DINOV2-FCS: a model for fruit leaf disease classification and severity prediction.Frontiers in plant science · 2024Article
- An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network.Current research in food science · 2024Article
- Nutritional composition analysis in food images: an innovative Swin Transformer approach.Frontiers in nutrition · 2024Article
- Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review.Frontiers in plant science · 2023Review
- Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
9 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As one of the representative algorithms of deep learning, a convolutional neural network (CNN) with the advantage of local perception and parameter sharing has been rapidly developed. CNN-based detection technology has been widely used in computer vision, natural language processing, and other fields. Fresh fruit production is an important socioeconomic activity, where CNN-based deep learning detection technology has been successfully applied to its important links. To the best of our knowledge, this review is the first on the whole production process of fresh fruit. We first introduced the network architecture and implementation principle of CNN and described the training process of a CNN-based deep learning model in detail. A large number of articles were investigated, which have made breakthroughs in response to challenges using CNN-based deep learning detection technology in important links of fresh fruit production including fruit flower detection, fruit detection, fruit harvesting, and fruit grading. Object detection based on CNN deep learning was elaborated from data acquisition to model training, and different detection methods based on CNN deep learning were compared in each link of the fresh fruit production. The investigation results of this review show that improved CNN deep learning models can give full play to detection potential by combining with the characteristics of each link of fruit production. The investigation results also imply that CNN-based detection may penetrate the challenges created by environmental issues, new area exploration, and multiple task execution of fresh fruit production in the future.
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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.