Growing More with Less Using Cell Phones and Satellite Data
نویسندگان
چکیده
منابع مشابه
More for Less - Getting More Clients by Broadcasting Less Data
Broadcasting is scalable in terms of served users but not in terms of served data volume. Additionally, waiting time deadlines may be difficult to uphold due to the data clutter, forcing the clients to flee the system. This work proposes a way of selecting subsets of the original data that ensure near-optimal service ratio. The proposed technique relies on the novel data compatibility distance,...
متن کاملEstimating the Yield and Biomass of Maize during the Growing Season Using Satellite (Data) (A Case Study: Dasht-e-Farahan)
Nowadays, the satellite data and remote sensing technologies are widely known as efficient tools for the inspection, identification and management of land resources and precision agriculture in most countries. Satellite information could be used in supplying basic and updated information in the estimation of vegetation cover map, irrigated land area and some biological indices of the major agri...
متن کاملLess is More: Improving the Speed and Prediction Power of Knowledge Tracing by Using Less Data
Knowledge Tracing is perhaps the most widely used student model in the field of educational data mining. In this paper we report on the effects of using only a subset of data in training the Bayesian Network that represents this student model. The standard practice is to use all of the students’ data for a given skill to fit the model. We analyze two datasets; one from the Algebra Cognitive tut...
متن کاملGrowing Rice With Less Water
Effective and efficient irrigation practices are critical to successful rice production and to the sustainability of rice production. To better understand how to address the issue of water management in rice we initially installed water flow meters on two studies that were designed to evaluate disease incidence using fertility, variety/hybrid, and water management as treatment variables. One st...
متن کاملGet More With Less: Near Real-Time Image Clustering on Mobile Phones
Machine learning algorithms, in conjunction with user data, hold the promise of revolutionizing the way we interact with our phones, and indeed their widespread adoption in the design of apps bear testimony to this promise. However, currently, the computationally expensive segments of the learning pipeline, such as feature extraction and model training, are offloaded to the cloud, resulting in ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Eos
سال: 2017
ISSN: 2324-9250
DOI: 10.1029/2017eo075143