The apple detection method based on multimodal features.

ABSTRACT: Accurate detection of apples and other fruits in complex environments remains a formidable challenge due to the intricate interplay of varying lighting conditions, occlusions, and background clutter. Traditional detection methods, which primarily rely on RGB images or incremental improvement of deep learning models, often fail to achieve satisfactory detection accuracy. To address this, an innovative method of apple detection is proposed to improve the detection performance through multimodal feature fusion rather than radical architectural modifications. The proposed method integrates four complementary modalities: RGB image, color and edge feature maps, depth feature map, and point clouds. Chromatic properties of fruits and geometric boundaries of fruit-tree structures are captured by co

SUBMITTER: Liu X 

PROVIDER: S-EPMC12548850 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature