A generalized model for accurate wheat spike detection and counting in complex scenarios.

Wheat is a crucial crop worldwide, and accurate detection and counting of wheat spikes are vital for yield estimation and breeding. However, these tasks are daunting in complex field environments. To tackle this, we introduce RIA-SpikeNet, a model designed to detect and count wheat spikes in such conditions.

Pest-YOLO: A model for large-scale multi-class dense and tiny pest detection and counting.

Frequent outbreaks of agricultural pests can reduce crop production severely and restrict agricultural production. Therefore, automatic monitoring and precise recognition of crop pests have a high practical value in the process of agricultural planting. In recent years, pest recognition and detection have been rapidly improved with the development of deep learning-based methods.

Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet.

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 wheat spikes faces persistent challenges due to the complexity of wheat field conditions. This work has trained a RetinaNet (SpikeRetinaNet) based on several optimizations to detect and count wheat spikes efficiently.

In situ observation and measurement of composites subjected to extremely high temperature.

In this work, we develop an instrument to study the ablation and oxidation process of materials such as C/SiC (carbon fiber reinforced silicon carbide composites) and ultra-high temperature ceramic in extremely high temperature environment. The instrument is integrated with high speed cameras with filtering lens, infrared thermometers and water vapor generator for image capture, temperature measurement, and humid atmosphere, respectively. The ablation process and thermal shock as well as the temperature on both sides of the specimen can be in situ monitored.