ArticleFrontiers in plant science2021
Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Ultralow concentrations of curcumin as a structural modifier in chitosan coatings for eco-friendly postharvest preservation of strawberries.Food science and biotechnology · 2026Article
- Visualization of Three-Dimensional SSC (Soluble Solids Content) Across the Entire Surface of Strawberries Using Near-Infrared Hyperspectral Imaging.Foods (Basel, Switzerland) · 2026Article
- Deep learning in food quality and safety management: A review of architectures, applications, and future directions.Current research in food science · 2026Review
- Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam.Foods (Basel, Switzerland) · 2025Article
- A Review of Research on Fruit and Vegetable Picking Robots Based on Deep Learning.Sensors (Basel, Switzerland) · 2025Review
- Multidimensional quality evaluation and traceability study of Fritillariae Cirrhosae Bulbus from different sources.Frontiers in plant science · 2025Article
- ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness.Scientific reports · 2024Article
- Prediction and visualization map for physicochemical indices of kiwifruits by hyperspectral imaging.Frontiers in nutrition · 2024Article
- Low temperature response index for monitoring freezing injury of tea plant.Frontiers in plant science · 2023Article
- A fine recognition method of strawberry ripeness combining Mask R-CNN and region segmentation.Frontiers in plant science · 2023Article
- Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review.Foods (Basel, Switzerland) · 2022Review
- Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models.Frontiers in plant science · 2022Article
- Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation.Frontiers in plant science · 2022Article
- Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology.Plant phenomics (Washington, D.C.) · 2022Article
- Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves.Plant phenomics (Washington, D.C.) · 2022Article
- DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification.Computational intelligence and neuroscience · 2022Article
- Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain.Food engineering reviews · 2022Review
- Hyperspectral detection of fresh corn peeling damage using germinating sparse classification method.Frontiers in plant science · 2022Article
- Citrus Huanglongbing Detection Based on Multi-Modal Feature Fusion Learning.Frontiers in plant science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (
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.