ArticleSensors (Basel, Switzerland)2021
Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Hyperspectral Imaging and Grading of Kiwifruit with Hierarchical 3D Convolution Data Processing.Sensors (Basel, Switzerland) · 2026Article
- Detection ofFrontiers in plant science · 2026Article
- A Preliminary Study of Hyperspectral Imaging Combined with Dual-Threshold Segmentation Technique for Peeling Rate Determination in Potatoes.Sensors (Basel, Switzerland) · 2025Article
- Non-Destructive Detection and Grading of Plum Quality Based on Multimodal Data.Sensors (Basel, Switzerland) · 2025Article
- In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology.Plant methods · 2025Article
- Cutting-edge computational approaches to plant phenotyping.Plant molecular biology · 2025Review
- Combination of gray level features with deep transfer learning for copra classification using machine learning and neural networks.Scientific reports · 2025Article
- Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification.Plant methods · 2024Article
- Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications.Plants (Basel, Switzerland) · 2024Article
- Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.Sensors (Basel, Switzerland) · 2024Article
- Development of Multimodal Fusion Technology for Tomato Maturity Assessment.Sensors (Basel, Switzerland) · 2024Article
- Prediction and visualization map for physicochemical indices of kiwifruits by hyperspectral imaging.Frontiers in nutrition · 2024Article
- Deep learning methods improve genomic prediction of wheat breeding.Frontiers in plant science · 2024Article
- A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7.Sensors (Basel, Switzerland) · 2023Article
- Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8.Sensors (Basel, Switzerland) · 2023Article
- Study on the detection of water status of tomato (Frontiers in plant science · 2023Article
- Applications of deep learning into supply chain management: a systematic literature review and a framework for future research.Artificial intelligence review · 2023Article
- Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review.Frontiers in plant science · 2023Review
- Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection.Foods (Basel, Switzerland) · 2021Article
- RRG-GAN Restoring Network for Simple Lens Imaging System.Sensors (Basel, Switzerland) · 2021Article
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2 authors.
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Abstract
Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya fruit. Deep learning architectures have gained respect and brought breakthroughs in unimodal processing. This paper suggests a novel non-destructive and multimodal classification using deep convolutional neural networks that estimate fruit maturity by feature concatenation of data acquired from two imaging modes: visible-light and hyperspectral imaging systems. Morphological changes in the sample fruits can be easily measured with RGB images, while spectral signatures that provide high sensitivity and high correlation with the internal properties of fruits can be extracted from hyperspectral images with wavelength range in between 400 nm and 900 nm-factors that must be considered when building a model. This study further modified the architectures: AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 to utilize multimodal data cubes composed of RGB and hyperspectral data for sensitivity analyses. These multimodal variants can achieve up to 0.90 F1 scores and 1.45% top-2 error rate for the classification of six stages. Overall, taking advantage of multimodal input coupled with powerful deep convolutional neural network models can classify fruit maturity even at refined levels of six stages. This indicates that multimodal deep learning architectures and multimodal imaging have great potential for real-time in-field fruit maturity estimation that can help estimate optimal harvest time and other in-field industrial applications.
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