Optimal irrigation water allocation using a genetic algorithm under various weather conditions

Growing water scarcity, due to growing populations and varying natural conditions, puts pressure on irrigation systems, which often are the main consumptive water users. Therefore, water resources management to improve the allocation of limited water supplies is essential. In this study, a non-linear programming optimization model with an integrated soil/water balance is developed to determine the optimal reservoir release policies and the optimal cropping pattern around Doroudzan Dam in the South-West of Iran. The proposed model was solved using a genetic algorithm (GA). Four weather conditions were identified by combining the probability levels of rainfall, evapotranspiration and inflow. Moreover, two irrigation strategies, full irrigation and deficit irrigation were modeled under each weather condition. The results indicate that for all weather conditions the total farm income and the total cropped area under deficit irrigation were larger than those under full irrigation. In addition, our results show that when the weather conditions and the availability of water changes the optimal area under corn and sugar beet decreases sharply. In contrast, the change in area cropped with wheat is small. It is concluded that the optimization approach has been successfully applied to Doroudzan Dam region. Thus, decision makers and water authorities can use it as an effective tool for such large and complex irrigation planning problems

Fast-AT: Fast Automatic Thumbnail Generation using Deep Neural Networks

Fast-AT is an automatic thumbnail generation system based on deep neural networks. It is a fully-convolutional deep neural network, which learns specific filters for thumbnails of different sizes and aspect ratios. During inference, the appropriate filter is selected depending on the dimensions of the target thumbnail. Unlike most previous work, Fast-AT does not utilize saliency but addresses the problem directly. In addition, it eliminates the need to conduct region search on the saliency map. The model generalizes to thumbnails of different sizes including those with extreme aspect ratios and can generate thumbnails in real time. A data set of more than 70,000 thumbnail annotations was collected to train Fast-AT. We show competitive results in comparison to existing techniques

Contextual cropping and scaling of TV productions

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0804-3. Copyright @ Springer Science+Business Media, LLC 2011.In this paper, an application is presented which automatically adapts SDTV (Standard Definition Television) sports productions to smaller displays through intelligent cropping and scaling. It crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. This approach allows a context-based composition of cropped images. It provides a differentiation between the original SD version of the production and the processed one adapted to the requirements for mobile TV. The system has been comprehensively evaluated by comparing the outcome of the proposed method with manually and statically cropped versions, as well as with non-cropped versions. Envisaged is the integration of the tool in post-production and live workflows

Optimal Crop Plans for Sustainable Water Use in Punjab

A linear programming model has been formulated to suggest the optimal cropping pattern for maximizing net returns and ensuring significant savings of groundwater with the aim of sustaining groundwater use in the Punjab agriculture. The primary data obtained from the project, “Comprehensive scheme to study the cost of cultivation of principal crops in Punjab†for the year 2002-03 pertain to 170 farmers selected through three-stage stratified random sampling technique. As the period of transplantation of paddy has a significant bearing on the amount of groundwater used and its sustainability, the paddy crop has been further classified into Paddy 1 (transplanted before 10th June); Paddy 2 (transplanted during 11th June to 20th June) and Paddy 3 (transplanted after 20th June). At the existing level of water availability, the optimal crop plan has not revealed any significant changes in the production pattern. Restricting the availability of groundwater has resulted into a major shift in the cropping pattern. Such changes could ensure groundwater savings of almost 25 per cent, without any adverse impact on the net returns from crop production. Introduction of new crops in the production plan, such as Bt cotton, has further enhanced the returns from crop production by about 4 per cent along with groundwater savings of 26.55 per cent. The study has suggested that alternate wetting and drying, adoption of system of rice intensification (SRI), use of tensiometers and direct plantation of paddy are some of the other techniques which can save water.Agricultural and Food Policy,