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

Spatial Law and Geospatial Applications

BY SANGEETA DEOGAWANKA
Taking a look at spatial and and geospatial applications, this is the second of a three-article series on Spatial Law that focuses on the issues surrounding spatial technologies, the responsibilities and legalities thereon.

Geospatial technology is empowering society and governance in unprecedented ways. The application scenarios are as relevant to the security of a nation as to that of a citizen. At the same time, the broader cultural impact is an equally important concern. The most significant legal implications relate to the data captured, used or shared. Whether spatial data is captured for purpose of ecommerce analytics, national security or first response, every application scenario requires separate consideration and legal provision.

3a. Purpose of Data or Application scenario
A particular geospatial technology can be used to collect data for different purposes. For instance, imagery data of different spatial, spectral and temporal resolutions may be deployed for mapping and monitoring vegetation cover or developing crop assessment models. Likewise, imagery may be used to detect intrusion of neighborhood countries where there is a border dispute. UAVs are used to facilitate disaster response operations. Drones are also used bot in the military and civil space, as much as they are used for making fun videos. It is evident that Spatial Laws cannot be uniform for all such diverse applications. The purpose for which spatial or location data is being captured determines policies and regulations. For instance, the permissible timeline of data storage, usage and sharing, would depend upon the purpose of data capture. The core legalities of intellectual property rights, licensing, contracts and privacy policies are also determined by the purpose of spatial data capture. Concurrently, the type of party (military, government, business or community) exercising data capture or collection is considered for defining the scope of spatial regulations.

3b. Data capture
Geospatial practitioners are increasingly coming under the lens of governments for unethical and unlawful spatial data collection practices. Recently, the European Union served Google a notice, voicing concerns about its data gathering practices.

With applications using geospatial data on the rise, policies and guidelines are being introduced. As a geospatial practitioner, there is need to understand the rights with respect to spatial data capture. Areas of awareness and compliance include Government laws and regulations related to national security and export controls, federal or local restrictions, and privacy laws. This in turn supports development of best practices, identification of potential liabilities, and allocation of risks through indemnifications and agreements.

3c. Data usage
As data capture using the UAV, GPS and location-based consumer application increases, so does concerns with respect to privacy, ethics and national security. Companies collecting, using or distributing spatial data need to understand and comply with collection and use of spatial information.

The increase in commercial use of spatial data has often resulted in legal disputes over use and practice norms. Businesses and geospatial practitioners can reduce the risk of litigation by observing the spatial policy regulations in place and providing for legal claims that may arise.Ultimately, it is the role of the governments and federal authorities to put into place effective regulations to ensure protection of the rights of private citizens, properties and businesses.

While Europe, Australia, and Canada have been actively tackling data protection, there is a lag in most other countries. In the absence of concrete guidelines, the legal industry itself has taken to self governance. Initiatives like Centre for Information Policy Leadership and Cloud Security Alliance are paving the way towards a regulatory framework of how location or geographic data collected is used.

Companies like Uber and other location apps collect location information with their always-on functionality, giving rise to concern. When location data is leveraged for geo-targeting ads and foreseeing consumer needs, it is considered acceptable. But an always on location permission allows collection of other data, like raw accelerometer and gyroscope streams which combined with personally identifiable information (PII) and raw sensor data.

Waze, the Google traffic application, is a popular app with motorists, but not with the police community who feel showing up location of police cars could aid criminals plan their moves.

3d. Data sharing
Convergence of technologies and seamless applications has given rise to the data-as-a-service sector, where data is shared across platforms, businesses and in the public cloud. Spatial data sharing including location information has become a common practice, with a near-legitimacy tag with businesses. Connected cars, store sensors, mobile location data, Foursquare and Facebook messenger apps, collect and share spatial data in a Big Data landscape. Instances like the Uber cab-booking service sharing user rides data of ZCTA (Zip Code Tabulation Area) with Boston city planners may just be the tip of an iceberg. In the absence of effective policies, consumers and advocacy groups have undertaken the watchdog role. Legal firms have also undertaken an active role as spatial data sharing and distribution becomes increasingly complex.

References

Gabrynowicz, Joanne Irene; The Land Remote Sensing Laws and Policies of National Governments: A Global Survey; The National Center for Remote Sensing, Air, and Space Law at the University of Mississippi School of Law, 2007 [Web]

Ito, Atsuyo; Legal Aspects of Satellite Remote Sensing, Brill Publishers [2011]

Slonecker, E. Terrence; Shaw, Denice M.; Lillesand Thomas; M. Emerging Legal and Ethical Issues in Advanced Remote Sensing Technology, Photogrammetric Engineering & Remote Sensing, 64:6, June 1998 [Web]

Websites


The Spatial Law and Policy initiative
Policy guidelines for drone operations
Blog on Public Domain spatial data

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JavaScript for Geospatial applications: An Overview

By Muthukumar Kumar

JavaScript has formed a strong relationship with the Geospatial world (WebGIS, et.al) in a manner that is reminiscent of the relationship between Python and Desktop GIS. JavaScript is often regarded as more of a scripting language than a full-fledged object oriented language but I must admit it has got a much wider functionality than one might imagine and for the record, JavaScript is officially termed as Object-Scripting language (read: Mozilla’s JS page). If you are looking for an overview of programming languages used in GIS, have a look at my previous post.

While the capabilities of Desktop GIS and their applications are undisputed, there has been a steady growth in the number of WebGIS applications and Apps. Majority of such applications utilize one JavaScript library or the other. Depending on whether you just want to use JavaScript for developing a simple map for your website or visualize tweets in real-time, one of the many JavaScript libraries and tools might be of interest to you. Here’s an overview of JavaScript for Geospatial applications:

OpenLayers

OpenLayers has great documentation, a new version (3.0), lots of examples to get you started and the best thing of them all – Open source! Certainly my favorite JavaScript library and is one of the easiest ways to get a map on the web.

ESRI API for JavaScript

ESRI does it and does it well. Not for nothing, is ESRI considered as the GIS pioneer. Some of the functionality that you see with ESRI’s API is not that easily achievable with the Open source solutions especially if you have limited programming skills. However ESRI is not alone in the JavaScript for Geospatial arena and it looks like this is going to be one interesting competition.

CartoDB

CartoDB impressed us with their living cities visualization partnering with HERE and then they “wowed” us with the real-time geo-tagged twitter maps. Guess those two examples are enough to understand why CartoDB.js is a great library for geospatial applications. Here’s a presentation (Jan 2013) about using CartoDB to develop maps for the web.

MapBox

MapBox.js is another cool library for building interactive maps. FourSquare, Pinterest, National Geographic are some of the companies that utilize MapBox’s JavaScript libraries for their web maps.

D3

Data-Driven Documents or D3 is general purpose data visualization library. D3 is certainly a hot topic of discussion and development among geospatial professionals. Considering that D3 supports a new format called TopoJSON for topology data, this is a given I guess! Our geo-geek blog partners at digital-geography do most certainly love this. Here’s a blog post regarding mapping using D3.

Leaflet

Leaflet is a great tool for making mobile-friendly interactive maps and it extremely light-weight at 33 KB. Leaflet has a lot going for it and was one of the fundamental driving forces behind the recent redevelopment of OpenLayers 3.0.

Node.js & Node Postgres

Great JavaScript libraries for building a web based PostGIS application.

Open Weather Map API

Get detailed weather information using this free JavaScript API.

Cesium

Really cool JavaScript library for rendering interactive 3D (0r 2D) graphic visualizations without any plug-ins on the browser, requires WebGL though.

HERE Maps API

If you want to make use of the HERE’s awesome expertise with traffic information, routing and more.

StoryMap.js

The Open source alternative to ESRI’s Story map application.

And of course, Yahoo Maps API, Bing Maps API and Google Maps API. If you dont want to use the data from the big players, there is always OpenStreetMap.

Learning JavaScript
There are lots of JavaScript books, resources and tutorials available and to each one his/her own but you might want to have a look at OpenGeo’s resource for learing to use JavaScript for Geospatial applications aka programming WebGIS applications – OpenGeo’s JS page.

I am sure that I am missing a lot of really nice JavaScript libraries and APIs. It would be wonderful to have your input in this regard. After all, that’s what the comments section is for.

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