Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

4-Octyl Itaconate Promotes Diabetic Wound Healing by Enhancing Pro-Resolving Macrophages via the Efferocytosis-MCT1-Lactate-GPR132 Pathway and Macrophage-Independent Synergistic Effects (Diabetes Metab J 2026;50:707-23).

Diabetes & metabolism journal·2026
Same author

"Immune senescence and dormant tumor cells: reconceptualizing breast cancer recurrence as an affliction of aging and chronic inflammation".

Annals of medicine and surgery (2012)·2026
Same author

Assessment of spatio-temporal dynamics of tropospheric NO<sub>2</sub> and covariates using the wavelet analysis: a Punjab case study.

Environmental monitoring and assessment·2026
Same author

Effectiveness of Mobile Health Augmented Cardiac Rehabilitation on Clinical Outcomes among Post-Acute Coronary Syndrome Patients: A Randomized Controlled Trial.

Pakistan journal of medical sciences·2026
Same author

Multiple Carboxylase Deficiency in an Infant Presenting With Severe Metabolic Acidosis and Sepsis-Like Features: A Case Report and Literature Review.

Clinical case reports·2026
Same author

Coronary Artery Calcium Scoring for Risk Reclassification and Prediction of Hard Cardiovascular Events in Asymptomatic Adults at Low-to-Intermediate Cardiovascular Risk: A Systematic Review.

Cureus·2026

Related Experiment Videos

SE-DBIRNet: Squeeze-and-Excitation Driven Dual-Path Residual Network for Mango Shelf-Life Stages Classification.

Ibrar Ahmad1, Bushra Siddique1, Muhammad Junaid2

  • 1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.

Foods (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

A new deep learning model, SE-DBIRNet, accurately classifies mango shelf-life stages in real-time. This lightweight architecture offers an optimal balance of accuracy, speed, and memory efficiency for post-harvest monitoring.

Keywords:
artificial intelligencedeep learningfood qualitypostharvest operationsshelf-life assessment

Related Experiment Videos

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Post-harvest mango losses in developing economies range from 5% to 30% due to manual, subjective quality assessments.
  • Current methods rely on visual inspection, leading to significant economic losses and inconsistent quality grading.

Purpose of the Study:

  • To develop a lightweight deep learning architecture, SE-DBIRNet, for real-time mango shelf-life stage classification.
  • To improve the accuracy and efficiency of post-harvest mango quality monitoring.

Main Methods:

  • Proposed SE-DBIRNet, a lightweight CNN incorporating depthwise separable convolutions, a double-branch inverted residual (DBIR) module, and a squeeze-and-excitation (SE) attention mechanism.
  • Evaluated the model on a public dataset of 4428 RGB images using 10-fold cross-validation.
  • Compared SE-DBIRNet against other lightweight CNNs like EfficientNetB0, MobileNetV2, and ResNet50.

Main Results:

  • SE-DBIRNet achieved 98.24% accuracy in classifying mangoes into five shelf-life stages (unripe to perished).
  • Outperformed EfficientNetB0 by 1.67 percentage points, offering a superior trade-off between accuracy, inference speed (56.9 FPS), and memory efficiency (8871 MB).
  • EigenCAM visualizations confirmed the model focuses on relevant visual features like color, texture, and decay.

Conclusions:

  • SE-DBIRNet provides a Pareto-optimal solution for real-time, edge-deployable mango quality monitoring, especially in resource-constrained environments.
  • The model's efficiency and accuracy make it suitable for reducing post-harvest losses and standardizing mango quality assessment.