SynthesisSensors (Basel, Switzerland)2025
Artificial Vision Systems for Fruit Inspection and Classification: Systematic Literature Review.
Synthesis in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Postharvest Fruit Grading Technologies and Equipment: A Review.Foods (Basel, Switzerland) · 2026Review
- SE-DBIRNet: Squeeze-and-Excitation Driven Dual-Path Residual Network for Mango Shelf-Life Stages Classification.Foods (Basel, Switzerland) · 2026Article
- Comparative Assessment of Hyperspectral Image Segmentation Algorithms for Fruit Defect Detection Under Different Illumination Conditions.Journal of imaging · 2026Article
- Physiological and image-based characterization ofFrontiers in plant science · 2026Article
- Robust Industrial Surface Defect Detection Using Statistical Feature Extraction and Capsule Network Architectures.Sensors (Basel, Switzerland) · 2025Article
- Multi-Task NoisyViT for Enhanced Fruit and Vegetable Freshness Detection and Type Classification.Sensors (Basel, Switzerland) · 2025Article
- A machine learning approach for classifying date fruit varieties at the Rutab stage.Frontiers in plant science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fruit sorting and quality inspection using computer vision is a key tool to ensure quality and safety in the fruit industry. This study presents a systematic literature review, following the PRISMA methodology, with the aim of identifying different fields of application, typical hardware configurations, and the techniques and algorithms used for fruit sorting. In this study, 56 articles published between 2015 and 2024 were analyzed, selected from relevant databases such as Web of Science and Scopus. The results indicate that the main fields of application include orchards, industrial processing lines, and final consumption points, such as supermarkets and homes, each with specific technical requirements. Regarding hardware, RGB cameras and LED lighting systems predominate in controlled applications, although multispectral cameras are also important in complex applications such as foreign material detection. Processing techniques include traditional algorithms such as Otsu and Sobel for segmentation and deep learning models such as ResNet and VGG, often optimized with transfer learning for classification. This systematic review could provide a basic guide for the development of fruit quality inspection and classification systems in different environments.
Indexed as
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What OpenQuestion holds
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.