Citrus Fruit Detection
Overview
In-field fruit detection and counting are crucial tasks for
agricultural automation. For example, they can be used to reduce
routine farming and breeding activities as well as provide
insightful estimates for harvest and forthcoming growing seasons.
Moreover, accurate fruit detection enables the possibility of
robotic harvesting, which has the potential to eliminate one of the
most labor-intensive processes for growers. Many imaging and
sensing technologies have been used for detecting fruit such as
hyperspectral, laser scanning, thermal, and RGBD sensors, yet the
most common technology is the standard RGB camera. Although
conventional RGB cameras are widely accessible, they present
several challenges for in-orchard fruit detection such as variation
in appearance, irregular lighting, and severe occlusion. Recent
works have used deep learning approaches to overcome these
challenges. Nonetheless, due to the lack of standardized benchmark
datasets for agricultural automation, it is difficult to compare
existing methods with each other.
Contributions
- We created a high-quality citrus fruit image dataset along with baseline performance benchmarks on multiple state-of-the-art object detection algorithms