  As technology progresses, smart automated systems will serve an increasingly
important role in the agricultural industry. Current existing vision systems
for yield estimation face difficulties in occlusion and scalability as they
utilize a camera system that is large and expensive, which are unsuitable for
orchard environments. To overcome these problems, this paper presents a size
measurement method combining a machine learning model and depth images captured
from three low cost RGBD cameras to detect and measure the height and width of
tomatoes. The performance of the presented system is evaluated on a lab
environment with real tomato fruits and fake leaves to simulate occlusion in
the real farm environment. To improve accuracy by addressing fruit occlusion,
our three-camera system was able to achieve a height measurement accuracy of
0.9114 and a width accuracy of 0.9443.
