Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.

Rice () is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features.

Integration of transcriptomic and proteomic analyses reveals several levels of metabolic regulation in the excess starch and early senescent leaf mutant lses1 in rice.

Background: The normal metabolism of transitory starch in leaves plays an important role in ensuring photosynthesis, delaying senescence and maintaining high yield in crops. OsCKI1 (casein kinase I1) plays crucial regulatory roles in multiple important physiological processes, including root development, hormonal signaling and low temperature-treatment adaptive growth in rice; however, its potential role in regulating temporary starch metabolism or premature leaf senescence remains unclear. To reveal the molecular regulatory mechanism of OsCKI1 in rice leaves, physiological, transcriptomic and proteomic analyses of leaves of osckI1 allele mutant lses1 (leaf starch excess and senescence 1) and its wild-type varieties (WT) were performed.

Compressive spectral imaging using variable number of measurements.

The compressive spectral imaging method always cuts down on the number of images for obtaining the spectral data cube of a scene. Our method cuts down on the number of sensors on the imaging plane, so as to fit some practical constraints, (e.g.

High-resolution spectral imaging based on coded dispersion.

Since the energy of the incident light is constant, the spatial and spectral resolution can hardly be improved without scarifying the other with the spectral imaging method of a pushbroom scanner. Thus, a new spectral imaging method is proposed to obtain a high-resolution (HR) spectral image with a low-resolution detector array. The method, namely coded dispersion, by which compressive measurement is achieved, improves light collection efficiency, and then a high-quality reconstructed HR spectral image is obtained with fewer sensors.