Predicting cropland latent heat flux (LHF) from commonly measured low-cost meteorological parameters (MPs) like net solar radiation, soil and air temperature, vapor pressure deficit, wind speed, and canopy temperature of the crops is essential for modeling crop production and managing water resources economically. In this treatise, we explore the deep reinforcement learning framework for short-term LHF trend estimation from the above MPs. The problem is reformulated as a classification problem, where each MP is acquired for a cost, and the objective is to optimize the trade-off between the predicted trend error and the relative MP acquisition cost. A sequential trend forecasting problem is evaluated via Q-learning with a linear guesstimate and a deep Q-learning scheme via neural network, where the distinct actions are the individual request for the MP values, and each episode is terminated by anticipating a trend. The proposed methodology is validated on the acquired farm-data, collected from the field experiments conducted on the cropland monitoring sites at Bidhan Chandra Krishi Viswavidyalaya State Agricultural University, Kalyani, West Bengal, India. The three non-rice crops, namely the yellow Sarson (mustard), potato, and green-gram, are studied owing to their similar energy balance partitioning patterns.
A Faint Object Spectrograph and Camera (FOSC) is designed for the upcoming 360-cm optical telescope at
Devasthal. The design is based on other available similar instruments, having a collimator and camera unit. The
instrument converts F/9 beam from the telescope to a nearly F/4.3 beam. The collimator and camera optics
have 7 and 5 elements respectively with one aspheric component. The low dispersion glasses such as CaF2 and
PBM/PBL/FSL are used in order to minimize the chromatic aberrations. These glasses also have very good
transmission near blue wavelengths. The imaging is possible both in narrow and broad band filters up to the
field of view of ~ 14' x 14' or 19' along the diagonal. The spectroscopy can be performed in the wavelength
range 350 - 900 nm with several choices of grisms and slits with resolution in the range of 250 - 2000. The
theoretical spot sizes in the imaging mode are expected in the range 0:04" - 0:11". The overall transmission of
the camera and collimator optics is expected as ~ 75% at 350 nm and > 95% at wavelengths above 400 nm.
The total weight of the instrument as designed is around 350 kg. The instrument is currently planned to be
assembled in the Institute laboratory and to be commissioned on the 360-cm telescope in October 2013. The
design methodology, techniques, and expected performance of the optics are presented here.
Concept of asynchronous DB defects inspection machine was contrived to the purpose of reducing the price which had
large scale flash memory buffer. This memory buffer was located in between reference data rendering computer and
scanner; also it was located in scanner and image computer. As first step to make the concept model real, an
experimental system was built which had virtual scanner.
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