Topic
Precision agriculture
About: Precision agriculture is a research topic. Over the lifetime, 5528 publications have been published within this topic receiving 87497 citations. The topic is also known as: SSCM & precision farming.
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Patent•
28 Jun 2016
TL;DR: In this article, a method for creating precision agriculture data maps is presented, where one or more hand-carried or vehicle transported mobile sensors that feed various types of agriculture-related soil and plant measurements into a mobile processing device that has software that automatically plots the data geospatially and contours the readings in color-coded or shaded gradients that can be displayed in layers and that can save and compared over time.
Abstract: A method for creating precision agriculture data maps utlilizing one or more hand-carried or vehicle transported mobile sensors that feed various types of agriculture-related soil and plant measurements into a mobile processing device that has software that automatically plots the data geospatially and contours the data readings in color-coded or shaded gradients that can be displayed in layers and that can be saved and compared over time.
Posted Content•
TL;DR: A spatial inference engine consisting of modular stages for processing spatial environmental data, generating predictions with machine-learning techniques, and analyzing these predictions is developed.
Abstract: The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data are too coarse or sparse for a given need (e.g., precision agriculture), one can leverage machine-learning techniques coupled with other sources of environmental information (e.g., topography) to generate gap-free information and at a finer spatial resolution (i.e., increased granularity). To this end, we develop a spatial inference engine consisting of modular stages for processing spatial environmental data, generating predictions with machine-learning techniques, and analyzing these predictions. We demonstrate the functionality of this approach and the effects of data processing choices via multiple prediction maps over a United States ecological region with a highly diverse soil moisture profile (i.e., the Middle Atlantic Coastal Plains). The relevance of our work derives from a pressing need to improve the spatial representation of soil moisture for applications in environmental sciences (e.g., ecological niche modeling, carbon monitoring systems, and other Earth system models) and precision agriculture (e.g., optimizing irrigation practices and other land management decisions).
01 Jan 2003