ArticleSensors (Basel, Switzerland)2022
Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Vision Systems for Fruit Inspection and Classification: Systematic Literature Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Artificial intelligence and biotechnology for smart storage: Quality and safety perspectives in horticultural produce.Current research in food science · 2026Review
- Multi-Task NoisyViT for Enhanced Fruit and Vegetable Freshness Detection and Type Classification.Sensors (Basel, Switzerland) · 2025Article
- Enhancing food recognition accuracy using hybrid transformer models and image preprocessing techniques.Scientific reports · 2025Article
- An improved deep learning network for image detection and its application in Dendrobii caulis decoction piece.Scientific reports · 2024Article
- ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness.Scientific reports · 2024Article
- Vegetable and fruit freshness detection based on deep features and principal component analysis.Current research in food science · 2024Article
- Article
- A novel method for vegetable and fruit classification based on using diffusion maps and machine learning.Current research in food 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
- Hydroponic Chinese flowering cabbage detection and localization algorithm based on improved YOLOv5s.PloS one · 2024Article
- Data Anomaly Detection for Structural Health Monitoring Based on a Convolutional Neural Network.Sensors (Basel, Switzerland) · 2023Article
- An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images.Sensors (Basel, Switzerland) · 2023Article
- Forest Fire Smoke Detection Based on Deep Learning Approaches and Unmanned Aerial Vehicle Images.Sensors (Basel, Switzerland) · 2023Article
- Deep learning based object detection and surrounding environment description for visually impaired people.Heliyon · 2023Article
- Automatic Detection of Small Sample Apple Surface Defects Using ASDINet.Foods (Basel, Switzerland) · 2023Article
- A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments.Sensors (Basel, Switzerland) · 2023Article
- Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People.Sensors (Basel, Switzerland) · 2023Article
- A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is challenging. This study presents a deep-learning system for multiclass fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves the development of an optimized YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone of the proposed model was enhanced using the Mish activation function for more precise and rapid detection. Compared with the previous YOLO series, a complete experimental evaluation of the proposed method can obtain a higher average precision than the original YOLOv4 and YOLOv3 with 50.4%, 49.3%, and 41.7%, respectively. The proposed system has outstanding prospects for the construction of an autonomous and real-time fruit and vegetable classification system for the food industry and marketplaces and can also help visually impaired people to choose fresh food and avoid food poisoning.
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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.