ArticleFoods (Basel, Switzerland)2022
Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models.
Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- Phytochemical, Nutritional and Physicochemical Characteristics of Fruits and Leaves from theInternational journal of molecular sciences · 2026Pooled it
- Development and early feasibility testing of machine-learning algorithms to non-invasively assess hemoglobin levels.npj biomedical innovations · 2026Article
- Carotid vulnerable plaque in coronary heart disease: a machine learning-based diagnostic model integrating tongue parameters and blood metabolic biomarkers.Frontiers in cardiovascular medicine · 2026Article
- Designing of guava quality classification model based on ANOVA and machine learning.Scientific reports · 2025Article
- Synthesis of Neem-Oil-Infused Niosome and Starch Nanoparticle Coatings for Preserving the Quality of Strawberry Fruit.Foods (Basel, Switzerland) · 2025Article
- Impact of organic acid cross-linking on the structure and functional properties of gum arabic and guar gum: Formulation of an edible coating for enhancing strawberry shelf life.Food chemistry: X · 2025Article
- Impact of Soil-Applied Biopesticides on Yield and the Postharvest Quality of Strawberry Fruits in Southeast Texas.Plants (Basel, Switzerland) · 2025Article
- Revolutionizing Chinese medicine granule placebo with a machine learning four-color model.Chinese medicine · 2025Article
- Hyperspectral technology and machine learning models to estimate the fruit quality parameters of mango and strawberry crops.PloS one · 2025Article
- Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing.Sensors (Basel, Switzerland) · 2024Article
- Integrating Image Analysis and Machine Learning for Moisture Prediction and Appearance Quality Evaluation: A Case Study of Kiwifruit Drying Pretreatment.Foods (Basel, Switzerland) · 2024Article
- Application of COFoods (Basel, Switzerland) · 2024Article
- Tongue color parameters in predicting the degree of coronary stenosis: a retrospective cohort study of 282 patients with coronary angiography.Frontiers in cardiovascular medicine · 2024Article
- Nano and Technological Frontiers as a Sustainable Platform for Postharvest Preservation of Berry Fruits.Foods (Basel, Switzerland) · 2023Review
- TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits.Sensors (Basel, Switzerland) · 2023Article
- Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors.Foods (Basel, Switzerland) · 2023Article
- Emerging Postharvest Technologies to Enhance the Shelf-Life of Fruit and Vegetables: An Overview.Foods (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 2 countries.
Funding
Abstract
Determination of internal qualities such as total soluble solids (TSS) and pH is a paramount concern in strawberry cultivation. Therefore, the main objective of the current study was to develop a non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries. Six hundred samples (100 samples in each ripening stage) in six ripening stages were collected randomly for measuring the biometrical characteristics, i.e., length, diameters, weight and TSS and pH values. An image of each strawberry fruit was captured for colour feature extraction using an image processing technique. Channels of each colour space (RGB, HSV and HSL) were used as input variables for developing multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The result of the study indicated that SVM-R model with HSV colour space performed slightly better than MLR model for TSS and pH prediction. The HSV based SVM-R model could explain a maximum of 84.1% and 79.2% for TSS and 78.8% and 72.6% for pH of the variations in measured and predicted data in training and testing stages, respectively. Further experiments need to be conducted with different strawberry cultivars for the prediction of more internal qualities along with the improvement of model performance.
Indexed as
Identifiers
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.