Plants in their natural habitats adapt to drought stress in the environment through a variety of mechanisms, ranging from transient responses to low soil moisture to major survival mechanisms of escape by early flowering in absence of seasonal rainfall. However, crop plants selected by humans to yield products such as grain, vegetable, or fruit in favorable environments with high inputs of water and fertilizer are expected to yield an economic product in response to inputs. Crop plants selected for their economic yield need to survive drought stress through mechanisms that maintain crop yield. Studies on model plants for their survival under stress do not, therefore, always translate to yield of crop plants under stress, and different aspects of drought stress response need to be emphasized. The crop plant model rice ( Oryza sativa) is used here as an example to highlight mechanisms and genes for adaptation of crop plants to drought stress.

Figure 1: DeepGlobe Challenges: Example road extraction, building detection, and land cover classification training images superimposed on corresponding satellite images. AbstractWe present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images ( Figure 1). Similar to other challenges in computer vision domain such as DAVIS[21] and COCO[33], DeepGlobe proposes three datasets and corresponding evaluation methodologies, coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2018.We observed that satellite imagery is a rich and structured source of information, yet it is less investigated than everyday images by computer vision researchers. However, bridging modern computer vision with remote sensing data analysis could have critical impact to the way we understand our environment and lead to major breakthroughs in global urban planning or climate change research. Keeping such bridging objective in mind, DeepGlobe aims to bring together researchers from different domains to raise awareness of remote sensing in the computer vision community and vice-versa. We aim to improve and evaluate state-of-the-art satellite image understanding approaches, which can hopefully serve as reference benchmarks for future research in the same topic. In this paper, we analyze characteristics of each dataset, define the evaluation criteria of the competitions, and provide baselines for each task.

Salinity, drought and low temperature are the common forms of abiotic stress encountered by land plants. To cope with these adverse environmental factors, plants execute several physiological and metabolic responses. Both osmotic stress (elicited by water deficit or high salt) and cold stress increase the endogenous level of the phytohormone abscisic acid (ABA). ABA-dependent stomatal closure to reduce water loss is associated with small signaling molecules like nitric oxide, reactive oxygen species and cytosolic free calcium, and mediated by rapidly altering ion fluxes in guard cells. ABA also triggers the expression of osmotic stress-responsive (OR) genes, which usually contain single/multiple copies of cis-acting sequence called abscisic acid-responsive element (ABRE) in their upstream regions, mostly recognized by the basic leucine zipper-transcription factors (TFs), namely, ABA-responsive element-binding protein/ABA-binding factor. Another conserved sequence called the dehydration-responsive element (DRE)/C-repeat, responding to cold or osmotic stress, but not to ABA, occurs in some OR promoters, to which the DRE-binding protein/C-repeat-binding factor binds. In contrast, there are genes or TFs containing both DRE/CRT and ABRE, which can integrate input stimuli from salinity, drought, cold and ABA signaling pathways, thereby enabling cross-tolerance to multiple stresses. A strong candidate that mediates such cross-talk is calcium, which serves as a common second messenger for abiotic stress conditions and ABA. The present review highlights the involvement of both ABA-dependent and ABA-independent signaling components and their interaction or convergence in activating the stress genes. We restrict our discussion to salinity, drought and cold stress.

Plants capture solar energy and atmospheric carbon dioxide (CO2) through photosynthesis, which is the primary component of crop yield, and needs to be increased considerably to meet the growing global demand for food. Environmental stresses, which are increasing with climate change, adversely affect photosynthetic carbon metabolism (PCM) and limit yield of cereals such as rice (Oryza sativa) that feeds half the world. To study the regulation of photosynthesis, we developed a rice gene regulatory network and identified a transcription factor HYR (HIGHER YIELD RICE) associated with PCM, which on expression in rice enhances photosynthesis under multiple environmental conditions, determining a morpho-physiological programme leading to higher grain yield under normal, drought and high-temperature stress conditions. We show HYR is a master regulator, directly activating photosynthesis genes, cascades of transcription factors and other downstream genes involved in PCM and yield stability under drought and high-temperature environmental stress conditions.

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