Image Processing for Fruit and Vegetable Estimation
Summary
Image processing techniques have become essential for non-destructive, rapid and accurate estimation of size, volume and mass of fruits and vegetables. By combining segmentation algorithms, feature extraction and statistical or machine-learning models, researchers can derive physical attributes from 2D or 3D image data without direct contact. Approaches range from simple top-view acquisition with single cameras to advanced three-dimensional reconstruction using depth sensors. Such methods support grading, sorting and yield prediction in post-harvest systems and precision agriculture, reducing labour costs and enhancing consistency. Advances in algorithmic efficiency and hardware affordability are driving global adoption in commercial packing lines and field monitoring applications.
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Image Processing for Fruit and Vegetable Estimation publication trend
The graph below shows the total number of articles in image processing for fruit and vegetable estimation across all publications each year (not limited to Nature Index journals).
Technical terms
Image segmentation: Partitioning an image into meaningful regions to isolate objects from the background.
Regression analysis: Statistical methods to model relationships between image-derived features and quantitative measurements such as weight or volume.
Artificial neural network (ANN): A computational model of interconnected nodes that learns complex, nonlinear patterns from data.
Support vector regression (SVR): A machine-learning technique that fits a function within a margin of error to perform regression using support vectors.
References
- A vision-based method to estimate volume and mass of fruit/vegetable: Case study of sweet potato. International Journal of Food Properties (2022).
- Non-Destructive Estimation of Fruit Weight of Strawberry Using Machine Learning Models. Agronomy (2022).
- Weight and volume estimation of single and occluded tomatoes using machine vision. International Journal of Food Properties (2021).
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