Geospatial Technologies in Precision Agriculture

BY PETER RODERICKS OISEBE


Since the agrarian revolution that hit Europe and America during the 18th century, the use of technology to improve the effectiveness and efficiency of farming practices has increased tremendously. During the 20thand 21st century for instance, discoveries in the field of science and technology have enabled farmers to effectively use their input to maximize their yield. These advancements have been greatly assisted by the use of sophisticated machineries, planting practices, use of fertilizers, herbicides and pesticides and so on. At the present moment however, the success of large-scale farming highly relies on geographic information technology through what is know as precision farming.

What is Precision Agriculture?
Precision agriculture, or precision farming, is therefore a farming concept that utilizes geographical information to determine field variability to ensure optimal use of inputs and maximize the output from a farm (Esri, 2008). Precision agriculture gained popularity after the realization that diverse fields of land hold different properties. Large tracts of land usually have spatial variations of soils types, moisture content, nutrient availability and so on. Therefore, with the use of remote sensing, geographical information systems (GIS) and global positioning systems (GPS), farmers can more precisely determine what inputs to put exactly where and with what quantities. This information helps farmers to effectively use expensive resources such as fertilizers, pesticides and herbicides, and more efficiently use water resources. In the end, farmers who use this method not only maximize on their yields but also reduce their operating expenses, thus increasing their profits. On these grounds therefore, this article shall focus on the use of geospatial technologies in precision farming. To achieve this, the paper shall focus on how geospatial data is collected, analyzed and used in the decision making process to maximize on yields.

Geospatial Data Collection, Analysis, and Interpretation for Agricultural Purposes
Geospatial technology cannot be successful if the correct data is not collected and analyzed effectively. To achieve this, several techniques have been advanced most of which are based on remote sensing. Remote sensing is essential in dividing a large farm into management zones (Grisso, 2009). Each zone has specific requirements that require the use of GIS and GPS to satisfy its needs. Thus, the first step of precision farming therefore is to divide the land into management zone. The division of this land into zones is mainly based on:

• Soil types
• pH rates
• Pest infestation
• Nutrient availability
• Soil moisture content
• Fertility requirements
• Weather predictions
• Crop characteristics
• Hybrid responses

This information can be accessed by reviewing available records. Most farms usually have records of soil survey maps, historical characteristics of crops, and records that show the cropping practices of the regions. Additionally, aerial and satellite photographs can be used in this process. For example, in the image sample below taken on January 30, 2001, three parameters were analyzed from a Daedalus sensor aboard a NASA aircraft. The individual fields are numbered in each of the images. The top image (mostly yellow) shows vegetation density. The color differences indicate crop density with dark blues and greens for lush vegetation and reds for areas with bare soil (known as “Normalized Difference Vegetation Index”, or NDVI). The middle image analyzed water distribution with green and blue areas measuring wet soil and red areas indicating dry soil. The middle image was derived from reflectance and temperature measures from the Daedalus sensor. The last image on the bottom measures crop stress with red and yellow pixels indicating areas of high stress. The data collected from analyzing these different conditions allows the farmer to micromanage the application of water to best address differing soil conditions and vegetation growth.

THE IMAGES ABOVE WERE ACQUIRED BY THE DAEDALUS SENSOR ABOARD A NASA AIRCRAFT FLYING OVER THE MARICOPA AGRICULTURAL CENTER IN ARIZONA ON JANUARY 30, 2001.

Additionally, one can generate up-to-date aerial and satellite photographs of the farm during different periods of the year or seasons. With this information, the farmer is able to determine the productivity of different management zones. At the same time, the growth and yield patterns of different zones within the farm can also be identified.

Various remote sensing techniques can be used to increase the effectiveness of this process. The most common remote sensing technique that has been applied over the years is observation with the use of the human eye. With the help of modern technology, any observation that is made using this method is usually geo-referenced into a GIS database. Much of precision agriculture relies on image-based data from remote sensing such as determining the greenness of the field using a technique to determine the productivity/yield of different managemen zones (Brisco et al, n.d.). This technique is based on the relationship that arises from the comparison of the reflection of red light and near infrared light. Data from RADARSAT has also provided farmers with reliable information regarding the parameters that determine soil conditions and crop performance.

The data that is collected from remote sensing acts as a source of point data. From the trends and frequencies that have been recorded, this dataset can easily be converted into spatial data that reflects the situation of all management zones within the farm with the use various GIS techniques and tools. Kriging is an example of a method that can be used to convert point data from remote sensing into spatial data (Brisco et al, n.d.). Spatial data can then be used to determine the possible problems that might be present in various management zones. This gives farmers the chance to come up with informed and effective decisions to alleviate the prevailing problems in order to boost the overall production of the farm.

Once point data has been collected, it needs to be stored and analyzed for it to be useful to the farmer. It is at this point that GIS tools come into use. GIS software can be used to develop digital maps that transform spatial information that has been collected on the ground into digital format. At the same time, the point data that had been collected on the field can now be transformed into spatial data to reflect the entire farm. To effectively differentiate points with different values within the management zones, the collected data is normally presented in either raster or vector formats (Brisco et al, n.d.). In raster format, imaginary grids within a map are developed. Points within the map that have different values are assigned different colours. Therefore, from a glance, a user can be able to identify points that have similar characteristics and differentiate them with points that have different characteristics. This form of data representation is useful in spatial modelling to show the relationship that exists within grouped data. Vector format on the other hand uses coordinates from the x-axis and y-axis to assign a specific point within a map. Points that have similar characteristics are plotted and joined together to form a borderline. This form of data presentation is effective in computerized mapping and spatial database management.

Once spatial data has been mapped, comparison of the results that are presented with the field notes is essential. This process is conducted to determine any trends and relationships that might be present on the ground. At this point, an area that has high content of nutrients in the soil or a region that is highly infested with parasites might be identified. This distribution can either be in the form of uniform or non-uniform variability. With this information, favourable management techniques can be put in place to increase the efficiency of farming to ensure optimal use of inputs and to maximize the output. Thus, the information that has been provided with the use of remote sensing and GIS can be used to make site-specific decisions with regards to the use of fertilizer, herbicides and pesticides, irrigation and so on. Most importantly, the data that has been generated needs to be stored in a systematic manner for future reference. This is essential, as it will increase the effectiveness and efficiencies of future surveys.

The main reason of collecting this data is for a farmer to have a clear understanding of the needs of different points in the farm to maximize his production. As this need increases, the use of automated farm machinery is inevitable (Sohne et al, 1994). These machines are expected to conduct their work precisely according to the information that has been fed on them. With the use of GIS and GPS, automated farm machineries are now more accurate, safe, eliminate human effort required to drive them and most importantly, increase the productivity of farms.

Geospatial Technologies on Tractors
Automated farm machineries are operated with the help of Navigation Geographic Information Systems (NGIS). This system is a combination of GPS and GIS systems that enables the machine to:

• Map Display
• Path Planning
• Navigation Control
• Sensor System Analysis
• Precision Positioning
• Data Communication

The system also enhances the management of the automated machines by enabling the user to control its speed, direction, and to monitor the surrounding conditions (Xiangjian and Gang, 2007). For automated machines to conduct their roles effectively and efficiently, they need to be fed with positioning information. This information is usually sent via a GPS receiver that contains precise time, latitudes and longitudes. The machine also received information with regards to the height above ground as well as the height above sea level. With the help of its GPS system, the machine is usually guided through an optimal path. Factors such as the length, traffic characteristics, corners and costs are usually considered while generating the path that shall be followed by the machine. Steering of the machine is determined by the angle that exists between the target points within the path. This ensures that the machines cover all the target points that have been identified from the spatial data from GIS. This therefore ensures that the machine will traverse the farm and spray, deposit or plant the exact amount or quantity of input that is required to maximize the output of a given site as per the findings in the farm.

TRIMBLE IS ONE GEOSPATIAL VENDOR FOR PRECISION AGRICULTURE TECHNOLOGY. TRACTOR WITHTRIMBLE BASED GPS TECHNOLOGY ON BOARD.

Conclusion

With the use of remote sensing, GPS and GIS, farmers can be able to understand site-specific needs of their farms. With this information, they are capable of formulating and implementing management techniques that will ensure the optimal use of inputs to maximize their output and profits. Geospatial technologies therefore provide a farmer with an information resource that he/she can use to make informed decisions that guarantee effective and efficient management of the farm to maximize its productivity. Thus, farmers should understand and implement these technologies in conjunction with their experience and expertise to get maximum benefits of their farms.

GEOSPATIAL TECHNOLOGY PLAYS A SIGNIFICANT ROLE IN MANY ASPECTS OF PRECISION FARMING (CLICK ON IMAGE FOR A LARGER VIEW). SOURCE: GPS4US.

References
Brisco, B., Brown, R., Hirose, J., McNairn, H. and Staenz, K. (n.d.). Precision Agriculture and the Role of Remote Sensing: A Review. Retrieved on 1st October 2012 from ftp://ftp.geogratis.gc.ca/part6/ess_pubs/219/219370/3520.pdf

Esri (2008). GIS for Sustainable Agriculture. GIS Best Practices. New York: ESRI Publications

Grisso, B. (2009). Precision Farming: A Comprehensive Approach. Retrieved on 1st October 2012 fromhttp://pubs.ext.vt.edu/442/442-500/442-500.html

Sohne, W., Heinze, O. and Groten, E. (1994). Integrated INS/GPS System for High Precision Navigation Applications. Record-IEEE PLANS, Position Location and Navigation Symposium, 35(2): 310-313.

Xiangjian, M. and Gang, L. (2007). Integrating GIS and GPS to Realise Autonomous Navigation of Farm Machinery.New Zealand Journal of Research, 50(1), 807-812

Related Resources

Agriculture and GIS

Source: GIS Lounge - Maps and GIS

Mapping urban food deserts

Interactive maps developed by MSU researchers show, rather than simply tell, how urban residents are losing access to fresh produce.
Credit: Image courtesy of Michigan State University


Maps are great for showing where things are. They're also good for showing where things aren't.


Two Michigan State University professors have developed interactive maps that offer a visual perspective of urban food deserts. By using GIS (geographic information systems) technology, they are showing, rather than simply telling, how urban residents are losing access to fresh produce and balanced nutrition.

Phil Howard, assistant professor of community, agriculture, recreation and resource studies, and Kirk Goldsberry, assistant professor of geography, conducted their research in Lansing. They found that many supermarkets have closed their stores that serve urban areas and have moved to the suburbs. They also showed that Michigan's state capital is a model for what's happening to food environments around the country.

"The change in food environments is recurring all over the nation," said Howard, whose research is supported by MSU's AgBioResearch. "The best selection of produce and the lowest prices have moved to the suburbs. So if you want lettuce in Lansing, or in most U.S. cities, you're going to have to drive to get it."

One aspect on which the study focused was store locations. It showed that less than 4 percent of the population lived within a 10-minute walk of a supermarket. The researchers also looked at the cost in reaching those stores as well as the availability of produce at each retail location. They took into account everything from urban party stores, which may offer lemons and limes, to suburban box stores, which offer nearly 250 different produce items.

By taking actual food inventories and pairing that data with geospatial inputs, the team was able to precisely measure geographic access to produce sections. What was revealed was a tale of two food environments. First, people with cars can overcome many geographical obstacles to obtain fresh produce while pedestrians' ability to obtain fresh produce is becoming increasingly challenging, Goldsberry said.

"I like to think of it as a nutritional CAT scan at the urban scale," Goldsberry said. "The Lansing food model definitely favors drivers because the stores with the best selection of produce are furthest from the densest population areas."

The maps give residents, city officials and community organizations an outreach tool to visualize their food environment. Having a highly detailed method to examine each city's food environment provides a graphic illustration of areas where produce is abundant and where it's lacking, Howard said.

"When people see a map, people gravitate to their neighborhood and make a decision about how it stacks up to others," he said. "They see a high-resolution image of their food environment that literally allows them to point out disparities and geographical challenges in their communities."


Story Source:
The above post is reprinted from materials provided by Michigan State University. Note: Materials may be edited for content and length.

Article Source: Science Daily

Is Remote Sensing The Answer To Today's Agriculture Problems? Wheat Growers Turn To Aerial Imagery To Overcome Economic, Environmental Challenges

Today's wheat growers face many economic and environmental challenges, but arguably their greatest challenge is the efficient use of fertilizer.


Growers need to apply nitrogen-based fertilizer in sufficient quantities to achieve the highest possible crop yields without over-applying - a situation that could lead to serious environmental effects. In wheat, a critical factor comes down to timing in order to determine how efficiently plants will use nitrogen fertilizer. Current methods for determining the optimum timing of nitrogen fertilizer application can be costly, time consuming, and difficult.

To assist wheat growers, scientists at North Carolina State University recently developed a technique to properly time nitrogen fertilizer applications. The technique? Remote sensing - a relatively new technology to today's modern agriculture that uses aerial photography and satellite imagery.

In this 2000-2001 study, scientists used remote sensing in the form of infrared aerial photographs to determine when early nitrogen fertilizer applications were required. By relating the infrared reflectance of the crop canopy to wheat tiller density, the scientists were able to differentiate wheat fields that would benefit from early nitrogen fertilizer applications compared to wheat fields that would benefit from standard nitrogen fertilizer applications. They tested 978 field locations, representing a wide range of environmental and climatic conditions. The remote sensing technique was found to accurately time nitrogen fertilizer applications 86% of the time across all field locations. The results of this study are published in the January/February 2003 issue of Agronomy Journal.

Michael Flowers, project scientist, said, "This is one of the first applications of remote sensing technology for nitrogen management available to growers. With the ability to cover large areas in a quick and efficient manner, this remote sensing technique will assist growers in making difficult nitrogen management decisions that affect profitability and environmental stewardship."

These scientists at North Carolina State University and other institutions around the world are continuing to research remote sensing techniques to improve the efficiency of nitrogen fertilizer applications in crops. These techniques will allow growers to more efficiently apply nitrogen fertilizer, increase profitability, and avoid detrimental environmental effects.



Agronomy Journal, http://agron.scijournals.org is a peer-reviewed, international journal of agriculture and natural resource sciences published six times a year by the American Society of Agronomy (ASA). Agronomy Journal contains research papers on all aspects of crop and soil science including resident education, military land use and management, agroclimatology and agronomic modeling, extension education, environmental quality, international agronomy, agricultural research station management, and integrated agricultural systems.

The American Society of Agronomy (ASA), the Crop Science Society of America (CSSA), and the Soil Science Society of America (SSSA) are educational organizations helping their 10,000+ members advance the disciplines and practices of agronomy, crop and soil sciences by supporting professional growth and science policy initiatives, and by providing quality, research-based publications and a variety of member services.



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The above post is reprinted from materials provided by American Society Of Agronomy. Note: Materials may be edited for content and length.

Article source: Science Daily

Remote Sensing Technique Uses Agricultural Aircraft

Once images have been retrieved from an agricultural aircraft, ARS scientists combine them to create a mosaic for study. This mosaic was used in a study of several catfish ponds near Lake Village, Arkansas.
Credit: Photo by Roger Bright



The need for higher resolution images in remote sensing projects has led to a new technique using agricultural airplanes in the Mississippi Delta.


Agricultural Research Service (ARS) agricultural engineer Steven J. Thomson, located in the ARS Application and Production Technology Research Unit, Stoneville, Miss., is applying remote sensing technology using agricultural aircraft to projects as diverse as crop water stress management, invasive imported fire ant control (a concern for ranchers and growers alike) and catfish production.

Thomson initially developed the method to collect field images as part of a concept known as precision agriculture. The idea is to determine only those areas in a field that require more attention by growers of cotton, soybeans, corn and other crops. This practice helps growers save on their input costs, such as fertilizer and pesticide, and reduces runoff.

An advantage of using agricultural aircraft is that they are potentially easier to schedule for remote sensing because they are frequently used in the field for pesticide spray operations, according to Thomson.

The new system is being used in studies for several applications with a variety of cameras, such as weed detection in cotton and soybean fields using digital video, and detection of crop nutrient or water stress using thermal imaging.

The use of agricultural aircraft for observation, as well as for spraying, has advantages other than additional utilization of the planes, including flexibility in how high or low the plane is flown. Flying an airplane close to the ground avoids atmosheric interference experienced with satellite images.

Although agricultural aircraft can be flown at a variety of altitudes, low flights limit the ability to capture images of large areas at once. That problem is overcome by making multiple flights over the site and assembling many images over different portions.

ARS is the U.S. Department of Agriculture's chief scientific research agency.



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The above post is reprinted from materials provided by USDA / Agricultural Research Service. Note: Materials may be edited for content and length.

Article source: Science Daily

North American Imagery Program And How To Develop Your Staff

Disclaimer: I know people and have friends that push pixels for a living, at least I did at the time of this writing.


The North American Imagery Program (NAIP) is a productive use of our federal tax dollars. NAIP is a program that is run by the United States Department of Agriculture (USDA), with a primary purpose of ensuring compliance in agriculture. Since many crops are subsidized and insured in this country, the NAIP conducts flights to image crops during the growing season in states that grow a large number of crops.

The imagery is used (remote sensed) to make sure that “Farmer Joe” is getting subsidies for the right crop. Typically, the imagery taken at a resolution of 2 meter pixels. However, every 5 years, generally, a state may be surveyed at the higher 1 meter pixels resolution. Once the imagery is certified by NAIP, it is released for public consumption via many sources, including Geospatial Data Gateway (GDG), a service run by the United States Department of Agriculture’s (USDA) through a close partnership between the three Service Center Agencies (SCA); Natural Resources Conservation Service (NRCS), Farm Service Agency (FSA), and Rural Development (RD).

NAIP and the GDG can be used to develop and train in-house staff on various geoprocesses, data management, data storage management, and understand the length and breadth of the associated processes. This approach can be very successful, and actually can yield quickly useable, realistic results. For those companies that have a reasonable number of geospatial analysts, one approach is to pick someone every year, and give them exposure to the NAIP and ask have them certain counties for a State or Area of Interest (AOI).

They would start by accessing the GDG, looking at the current year’s data, and begin downloading it. The GDG throttles how much data can be downloaded at once. Nonetheless, this would help the analyst start learning how to allocate resources, and manage time, with respects to keeping the downloads as continuous as possible.

Once the data is in house, a number of different applications and processes can be performed against the data. Some of these processes include understanding when pixels overlap from multiple images, how to choose which one you want to keep; how to ignore certain values (for example, black collars); and set up a workflow for the newly compiled image. “Rinse and repeat” these processes against other portions of the available data.

With analysts having a mature understanding to geospatial data processing, companies should enture that they working with the IT Infrastructure group on provisioning enough free storage capacity, such that it allows the analysts to combine several very large images (often over 100GB per image).

At this point the analyst can take ownership of the data set. Luckily, the actual time an analyst spends at the keyboard is relatively small. The majority of time is taken up with computer processing. However, analysts will check frequently to make sure the process is still running.

Once processed, the newly compiled data would get loaded into an image server, file share, webservice, or someone other device that can distribute and share the new aerial image for other users to utilize.

Ideally, processing of the NAIP data should take place every year. Analysts can look at images from the past and the current ones to see what had changed. In combination, there are great benefits for end-users, while providing each with a realistic learning experiences for internal analysts who want to be developed, and utilizing available resources .

The outcome of this process of training is to develop analysts who are capable of:

• Project management;
• Learning from mistakes;
• Learn new skill sets;
• Being aware of public datasets;
• How to improve analysts on their use and understanding of how data is formatted and transformed; and of course
• Taking pride in their work.

Source

Applying spatial analysis for precision conservation across the landscape

Journal article by J.K. Berry, J.A. Delgado, F.J. Pierce, and R. Khosla

ABSTRACT

Although new technologies such as precision farming will contribute to increasing yields per unit area, similarly soil and water conservation will be instrumental in maintaining these increases in productivity while reducing environmental degradation, off-site transport, and water pollution. Initially, ‘precision conservation’ was defined as the integration of spatial technologies such as global positioning systems (GPS), remote sensing, and geographic information systems (GIS) and the ability to analyze spatial relationships within and among mapped data. Surface modeling, spatial data mining and map analysis are three broad approaches that can be used to analyze layers of information to help develop and implement management practices that contribute to soil and water conservation in agricultural and natural ecosystems. In this paper, we expand the definition of precision conservation to a developing science that uses the new spatial technologies to link a system from a site specific location, to a field, to a set of fields (farm) to a regional scale. We also expand our discussion based on the status of precision conservation as it was shown by twenty six precision conservation papers presented at the 2004 Soil Science Society of America annual meeting. We propose that precision conservation will be a key science to contribute to the sustainability of our biosphere in this century.

For full text follow the link here.

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

A journal article by : Ran Huang, Chao Zhang, Jianxi Huang, Dehai Zhu, Limin Wang, and Jia Liu

Abstract

Air temperature is one of the most important factors in crop growth monitoring and simulation. In the present study, we estimated and mapped daily mean air temperature using daytime and nighttime land surface temperatures (LSTs) derived from TERRA and AQUA MODIS data. Linear regression models were calibrated using LSTs from 2003 to 2011 and validated using LST data from 2012 to 2013, combined with meteorological station data. The results show that these models can provide a robust estimation of measured daily mean air temperature and that models that only accounted for meteorological data from rural regions performed best. Daily mean air temperature maps were generated from each of four MODIS LST products and merged using different strategies that combined the four MODIS products in different orders when data from one product was unavailable for a pixel. The annual average spatial coverage increased from 20.28% to 55.46% in 2012 and 28.31% to 44.92% in 2013.The root-mean-square and mean absolute errors (RMSE and MAE) for the optimal image merging strategy were 2.41 and 1.84, respectively. Compared with the least-effective strategy, the RMSE and MAE decreased by 17.2% and 17.8%, respectively. The interpolation algorithm uses the available pixels from images with consecutive dates in a sliding-window mode. The most appropriate window size was selected based on the absolute spatial bias in the study area. With an optimal window size of 33 × 33 pixels, this approach increased data coverage by up to 76.99% in 2012 and 89.67% in 2013.

Keywords: daily mean air temperature; land surface temperature; MODIS; meteorological station data; Shaanxi

                                           

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cite

Follow link to access full text here.