The orbit of each of the planets is slightly affected by the gravitational influences of the other planets. History Discovery of Pluto Pluto imaged by New Horizons Twelve minor planets with a semi-major axis greater than 150 au and perihelion greater than 30 au are known, which are called extreme trans-Neptunian objects (ETNOs). They are thought to be composed of mixtures of rock, amorphous carbon and volatile ices such as water and methane, coated with tholins and other organic compounds. TNOs vary in color and are either grey-blue (BB) or very red (RR). More than 80 satellites have been discovered in orbit of trans-Neptunian objects. The most massive TNO known is Eris, followed by Pluto, Haumea, Makemake, and Gonggong. It took until 1992 to discover a second trans-Neptunian object orbiting the Sun directly, 15760 Albion. The first trans-Neptunian object to be discovered was Pluto in 1930. As of October 2020, the catalog of minor planets contains 678 numbered and more than 2,000 unnumbered TNOs. ![]() Typically, TNOs are further divided into the classical and resonant objects of the Kuiper belt, the scattered disc and detached objects with the sednoids being the most distant ones. ![]() ![]() It opens new avenues for analyzing rice phenotypes under different nitrogen treatments and environmental conditions, thereby aiding in advancing agronomic research and cultivation practices.A trans-Neptunian object ( TNO), also written transneptunian object, is any minor planet in the Solar System that orbits the Sun at a greater average distance than Neptune, which has an orbital semi-major axis of 30.1 astronomical units (au). In conclusion, the proposed pipeline demonstrates a non-destructive, accurate, and efficient approach to obtaining panicle traits. It was observed that nitrogen application increases the panicle number but also affects grain filling initiation and duration. The study also highlighted the effect of nitrogen on rice heading and flowering, indicating potential impacts on grain filling. The model exhibited robust performance in tracking rice panicles, with approximately 70% of panicles tracked wholly and continuously despite environmental changes. The tracking of individual panicles revealed that higher nitrogen application led to earlier flowering initiation and longer flowering periods. This study facilitated the analysis of the rice flowering process and heading date identification, aligning closely with manual counts and field observations.įurthermore, the method effectively identified sensitive flowering changes due to environmental factors like temperature and humidity. It also effectively detected panicles with varying shapes, colors, and textures.įor panicle classification, the ResNet 50-based model distinguished between vigorous and non-vigorous flowering panicles with high accuracy. ![]() The panicle detection model, evaluated against different nitrogen treatments, maintained consistent accuracy across years, indicating its universality for different rice varieties. This analysis revealed that increased nitrogen leads to more panicles, longer flowering durations, and earlier flowering initiation. Moreover, the method facilitated the analysis of flowering duration and individual panicle flowering times. Results showed high accuracy in panicle counting (R 2 = 0.96, RMSE = 1.73) and precise estimation of the heading date (absolute error of 0.25 days). This method was tested for its ability to detect subtle differences in panicle development under varying nitrogen treatments. In this study, researchers developed a pipeline utilizing YOLO v5, ResNet50, and DeepSORT models to automatically extract detailed panicle traits from time-series images. In June 2023, Plant Phenomics published a research article titled " Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series Tracking." Addressing this gap requires combining field cameras with deep learning for detailed, real-time monitoring. However, these techniques face limitations in capturing the dynamic growth of rice panicles over time. Recent advancements in computer vision and machine learning, especially deep learning, have improved plant phenotyping, with methods like the scale-invariant feature transform (SIFT) algorithm and convolutional neural networks aiding in rice panicle analysis.
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