Automatic Fruit Size Evaluation System Based on LabVIEW

Fruit size is a key trait in small-fruit breeding, yet its measurement remains labor intensive and prone to human error. To address this, we developed a non-destructive, automated size measurement system based on machine vision and LabVIEW, designed for small fruits such as cherry, blueberry, and walnut. The system integrates a modular architecture, including flat-field correction, calibration, and pattern matching sub-VIs, to ensure user-friendly operation. These sub-VIs also enable the system’s core data analysis, such as real-size conversion through calibration and noise reduction for data accuracy through flat-field correction. An optimized image processing pipeline (grayscale conversion, Canny edge detection, morphological operations) enables precise contour extraction, even for fruits with stems or irregular surfaces. The system supports multi-species adaptation through lightweight parameter adjustments, without hardware modification. Experiments involved 15 samples per species (cherry ‘Tieton’, blueberry ‘Northland’, walnut ‘Xiangling’). A gold-standard protocol was established using a pre-calibrated digital caliper operated by two experienced technicians, with the mean of six replicates per fruit defined as the true value. Results demonstrated low root mean square errors, with coefficients of determination (R2) exceeding 0.98. Paired t-tests confirmed no significant differences from the gold standard. The system achieved a measurement speed of 0.4 s per fruit, six times faster than manual methods, and complied with the precision requirements of GB/T 26906-2024 (Sweet Cherry). This system offers a cost-effective, high-throughput solution for fruit breeding and phenotyping, effectively overcoming the limitations of manual measurement.