Rice yield estimation is an important aspect in the agriculture research field. For the rice yield estimation, rice density is one of its useful factors. In this paper, we propose a new method to automatically detect the rice density from the rice transplanting stage to rice jointing stage. It devotes to detect rice planting density by image low-level features of the rice image sequences taken in the fields. Moreover, a rice jointing stage automatic detection method is proposed so as to terminate the rice density detection algorithm. The validities of the proposed rice density detection method and the rice jointing stage automatic detection method are proved in the experiment.
Image super-resolution methods based on forward-feed convolutional neural networks (CNN) reconstruct the image with more details and sharper texture. However, most of these methods do not consider the influence of high level semantic feature to improve image perceptual effect. In this paper, we propose a deep CNN architecture jointing low-high level feature for image super-resolution. Our method uses 17 weight layers to predict residual between the high resolution and low resolution image. And we joint the low level and high level image features to constraint the network parameters updating. Experimental results validate that our method reconstruct the high resolution images with clear edge and less warp.
Columnar joints form by cracking during cooling-induced contraction of lava, allowing hydrothermal fluid circulation. A lack of direct observations of their formation has led to ambiguity about the temperature window of jointing and its impact on fluid flow. Here we develop a novel thermo-mechanical experiment to disclose the temperature of columnar jointing in lavas. Using basalts from Eyjafjallajokull volcano (Iceland) we show that contraction during cooling induces stress build-up below the solidus temperature (980 degrees C), resulting in localised macroscopic failure between 890 and 840 degrees C. This temperature window for incipient columnar jointing is supported by modelling informed by mechanical testing and thermal expansivity measurements. We demonstrate that columnar jointing takes place well within the solid state of volcanic rocks, and is followed by a nonlinear increase in system permeability of <9 orders of magnitude during cooling. Columnar jointing may promote advective cooling in magmatic-hydrothermal environments and fluid loss during geothermal drilling and thermal stimulation.