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.

Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping.

Abstract: Early and accurate detection and diagnosis of plant diseases are key factors in plant production and the reduction of both qualitative and quantitative losses in crop yield. Optical techniques, such as RGB imaging, multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence, have proven their potential in automated, objective, and reproducible detection systems for the identification and quantification of plant diseases at early time points in epidemics. Recently, 3D scanning has also been added as an optical analysis that supplies additional information on crop plant vitality. Different platforms from proximal to remote sensing are available for multiscale monitoring of single crop organs or entire fields. Accurate and reliable detection of diseases is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems. Nondestructive, sensor-based methods support and expand upon visual and/or molecular approaches to plant disease assessment. The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping.

Application of soil electrical conductivity to precision agriculture: theory, principles, and guidelines

Abstract: Due in large measure to the prodigious research efforts of Rhoades and his colleagues at the George E. Brown, Jr., Salinity Laboratory over the past two decades, soil electrical conductivity (EC), measured using electrical resistivity and electromagnetic induction (EM), is among the most useful and easily obtained spatial properties of soil that influences crop productivity. As a result, soil EC has become one of the most frequently used measurements to characterize field variability for application to precision agriculture. The value of spatial measurements of soil EC to precision agriculture is widely acknowledged, but soil EC is still often misunderstood and misinterpreted. To help clarify misconceptions, a general overview of the application of soil EC to precision agriculture is presented. The following areas are discussed with particular emphasis on spatial EC measurements: a brief history of the measurement of soil salinity with EC, the basic theories and principles of the soil EC measurement and what it actually measures, an overview of the measurement of soil salinity with various EC measurement techniques and equipment (specifically, electrical resistivity with the Wenner array and EM), examples of spatial EC surveys and their interpretation, applications and value of spatial measurements of soil EC to precision agriculture, and current and future developments. Precision agriculture is an outgrowth of technological developments, such as the soil EC measurement, which facilitate a spatial understanding of soil-water-plant relationships. The future of precision agriculture rests on the reliability, reproducibility, and understanding of these technologies.

Aspects of precision agriculture

Abstract: Precision agriculture is the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production for the purpose of improving crop performance and environmental quality. Success in precision agriculture is related to how well it can be applied to assess, manage, and evaluate the space-time continuum in crop production. This theme is used here to assess the current and potential capabilities of precision agriculture. Precision agriculture is technology enabled. It is through the integration of specific technologies that the potential is created to assess and manage variability at levels of detail never before obtainable and, when done correctly, at levels of quality never before achieved. The agronomic feasibility of precision agriculture has been intuitive, depending largely on the application of traditional management recommendations at finer scales, although new approaches are appearing. The agronomic success of precision agriculture has been limited and inconsistent although quite convincing in some cases, such as N management in sugar beet (Beta vulgaris L.). Our analysis suggests prospects for current precision management increase as the degree of spatial dependence increases, but the degree of difficulty in achieving precision management increases with temporal variance. Thus, management parameters with high spatial dependence and low temporal variance (e.g., liming, P, and K) will be more easily managed precisely than those with large temporal variance (e.g., mobile insects). The potential for economic, environmental, and social benefits of precision agriculture is complex and largely unrealized because the space-time continuum of crop production has not been adequately addressed.