Cereal plant heads must be identified precisely and effectively in a range of agricultural
applications, including yield estimation, disease detection, and breeding. Traditional …

The current deep-learning-based tomato target detection algorithm has many parameters; it
has drawbacks of large computation, long time consumption, and reliance on high …

This review systematically examines the progression of the You Only Look Once (YOLO)
object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a …

Background Detection and counting of wheat heads are of crucial importance in the field of
plant science, as they can be used for crop field management, yield prediction, and …

Leaf segmentation learns more about leaf-level traits such as leaf area, count, stress, and
development phases. In plant phenotyping, segmentation and counting of plant organs like …

The number of wheat spikes per unit area is one of the most important agronomic traits
associated with wheat yield. However, quick and accurate detection for the counting of …

Detection and counting of wheat heads are crucial for wheat yield estimation. To address the
issues of overlapping and small volumes of wheat heads on complex backgrounds, this …

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 yield is positively correlated with the number of wheat spikes in the field, which is an
essential index for plant breeders. To efficiently calculate this index, there is a high demand …

A non-destructive, convenient, and low-cost yield estimation at the field scale is vital for
precision farming. Significant progress has been made in using UAV-based canopy features …