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Yield estimation can provide critical information for high-value crop growers. It helps them to plan and coordinate the logistics of harvesting operations. Most existing yield estimation techniques are based on manual sampling and statistical approaches, which are complex, labor-intensive, and time-consuming. In recent years, advances in image processing and the ubiquitous use of autonomous platforms, such as unmanned aerial platforms, have provided an opportunity for faster and more accurate yield estimation techniques for different crops. A new rapid, efficient, and accurate image processing algorithm was proposed in this study. The image segmentation technique was able to separate mature citrus trees and natural background in CIE (Commission International Eclairage) L*a*b* (i.e., Lab) color space. The effect of camera distance to object, affected resolution, and detection accuracy were evaluated and discussed. The results showed that the distance between 1.524 meters (5 feet) and 2.134 meters (7 feet) could provide high detection accuracy. The number of correctly counted fruit reached 91.69% at the average processing time of 1.1 s per picture for 132 pictures. A correlation coefficient (R2) of 0.98 was obtained between the citrus counting algorithm and counting performed through human observation of 66 sample trees. The comparative analysis of the proposed method showed a higher accuracy rate and robustness compared to other similar studies.