Learning accurate object detectors often requires large-scale training data with precise
object bounding boxes. However, labeling such data is expensive and time-consuming. As …

The object detection method based on deep learning convolutional neural network (CNN)
significantly improves the detection performance of wheat head on wheat images obtained …

Wheat head detection is a core computer vision problem related to plant phenotyping that in
recent years has seen increased interest as large-scale datasets have been made available …

Wheat head detection can measure wheat traits such as head density and head
characteristics. Standard wheat breeding largely relies on manual observation to detect …

In the detection of global wheat heads, it is easy to give rise to difficulties due to different
wheat varieties, planting densities and growth periods of wheat plants in different countries …

Domain generalisation aims to promote the learning of domain-invariant features while
suppressing domain-specific features, so that a model can generalise better to previously …

Deep learning has shown potential in domains with large-scale annotated datasets.
However, manual annotation is expensive, time-consuming, and tedious. Pixel-level …

This article proposes a practical and generalizable object detector, termed feature extraction-
fusion-prediction network (FEFP-Net) for real-world application scenarios. The existing …

The flowering period is one of the important indexes of wheat breeding. The early or late
flowering affects the final yield and character stability of wheat. In order to solve the problem …

Wheat head detection is essential in estimating the important characteristics of wheat.
However, detecting wheat heads in images from different domains has been challenging …