Citrus fruit diameter estimation in the field using monocular camera

Abstract

Accurate and efficient measurement of citrus fruit size is essential for managing tree form and estimating yields. Conventional manual methods are reliable but highly labour-intensive, while existing machine vision solutions often require specialised setups (e.g., distance calibration or 3D sensors). In this study, a low-cost, monocular-based framework that uses mature and healthy leaves as natural reference objects was proposed, eliminating the need for manual markers or complex camera parameter calibration. By compiling an offline leaf-size distribution from multiple citrus varieties, this method automatically converts fruit pixels to real-world diameters using the largest near-frontal leaf in each image. Further, the work integrates the deformable convolution (DNCv2) and shuffle attention (SA) into a YOLOv8 detector to improve occlusion handling, ensuring robust detection even when fruits are partially obscured by foliage. Extensive validation on three different citrus cultivars shows that leaf-size variability contributes less than 3.2% relative error in diameter estimation, while the overall approach achieves 93.14% accuracy and R² = 0.76. Key contributions include: (1) a novel monocular technique leveraging inherent orchard elements (leaves) as references, (2) advanced detection modules to tackle partial occlusion, (3) cross-variety validation demonstrating consistent performance, and (4) a fast, user-friendly workflow suitable for real-world orchard applications. Future work will explore multi-frame or multi-view strategies to further refine diameter measurement under heavy occlusion.