GIS & remote sensing for precision agriculture

Precision agriculture satellite farming or site specific crop management (SSCM) is a farming management concept based on observing measuring and responding to inter and intra field variability in crops through GIS.

GIS & remote sensing for precision agricultureThe goal of precision agriculture research is to define a decision support system (DSS) for whole farm management with the goal of optimizing returns on inputs while preserving resources. The practice of precision agriculture has been enabled by the advent of GPS.

The farmer’s and/or researcher’s ability to locate their precise position in a field allows for the creation of maps of the spatial variability of as many variables as can be measured (e.g. crop yield, terrain features/topography, organic matter content, moisture levels, nitrogen levels, pH, EC, Mg, K, and others).

Similar data is collected by sensor arrays mounted on GPS equipped combine harvesters. These arrays consist of real time sensors that measure everything from chlorophyll levels to plant water status along with multispectral imagery.

 This data is used in conjunction with satellite imagery by variable rate technology (VRT) including seeders, sprayers etc. to optimally distribute resources.

Precision agriculture has also been enabled by unmanned aerial vehicles like the DJI Phantom which are relatively inexpensive and can be operated by novice pilots.

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These systems commonly known as drones can be equipped with hyperspectral or RGB cameras to capture many images of a field that can be processed using photogrammetric methods to create orthophotos and NDVI maps.

Precision agriculture is usually done as a four stage process to observe spatial variability.

Data collection:

Geolocating a field enables the farmer to overlay information gathered from analysis of soils and residual nitrogen and information on previous crops and soil resistivity. Geolocation is done in two ways:

  • The field is delineated using an in vehicle GPS receiver as the farmer drives a tractor around the field.
  • The field is delineated on a basemap derived from aerial or satellite imagery. The base images must have the right level of resolution and geometric quality to ensure that geolocation is sufficiently accurate.

Variables:

Intra and inter field variability may result from a number of factors. These include climatic conditions (hail, drought, rain, etc.), soils (texture, depth, nitrogen levels), cropping practices (no till farming), weeds and disease. Permanent indicators chiefly soil indicators provide farmers with information about the main environmental constants.

Point indicators allow them to track a crop’s status i.e. to see whether diseases are developing if the crop is suffering from water stress, nitrogen stress, or lodging. whether it has been damaged by ice and so on.

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This information may come from weather stations and other sensors (soil electrical resistivity, detection with the naked eye, satellite imagery etc.). Soil resistivity measurements combined with soil analysis make it possible to measure moisture content. Soil resistivity is also a relatively simple and cheap measurement.

Strategies:

Using soil maps farmers can pursue two strategies to adjust field inputs:

  • Predictive approach: based on analysis of static indicators (soil, resistivity, field history, etc.) during the crop cycle.
  • Control approach: information from static indicators is regularly updated during the crop cycle by:
    • sampling: weighing biomass, measuring leaf chlorophyll content, weighing fruit etc.
    • proxy detection: in vehicle sensors measure leaf status this requires the farmer to drive around the entire field.
    • aerial or satellite remote sensing: multispectral imagery is acquired and processed to derive maps of crop biophysical parameters including indicators of disease. Airborne instruments are able to measure the amount of plant cover and to distinguish between crops and weeds.

Decision may be based on decision support models (crop simulation models and recommendation models) but in the final analysis it is up to the farmer to decide in terms of business value and impacts on the environment.

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It is important to realize why Precision Agriculture technology is or is not adopted “for Precision Agriculture technology adoption to occur the farmer has to perceive the technology as useful and easy to use.

It might be insufficient to have positive outside data on the economic benefits of Precision Agriculture technology as perceptions of farmers have to reflect these economic considerations.”

Implementing practices:

New information and communication technologies make field level crop management more operational and easier to achieve for farmers. Application of crop management decisions calls for agricultural equipment that supports variable rate technology (VRT) for example varying seed density along with variable rate application (VRA) of nitrogen.

Precision agriculture uses technology on agricultural equipment (e.g. tractors, sprayers, harvesters, etc.):

  • positioning system(e.g. GPS receivers that use satellite signals to precisely determine a position on the globe).
  • geographic information systems(GIS)  e. software that makes sense of all the available data variable rate farming equipment (seeder, spreader).

Authors: GIS & Remote Sensing for Precision Agriculture

Muhammad Umer Hameed, Muhammad Sajjad

(University of Agriculture Faisalabad)

Author: Muhammad Umer

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