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B N Shukla1, V Sahu1 and V Mishra2 

  1. Research Scholar, ICAR-CIFE, Mumbai
  2. Research Scholar, College of Fisheries, MPUAT, Udaipur

                 Corresponding author:


Farming of fish and shellfish is currently the fastest growing area of animal food production due to its high source of protein. Therefore, there is a high source of demand for improving fish farming technologies to make the most efficient use of the world’s water bodies. Satellite farming or precision farming a new technique is to be adopted to increase the aquaculture. It is the concept of using the new technologies and collected field information, doing the right thing, in the right place, at the right time. It helps in avoiding unwanted practices to a crop, regardless of local soil/climate conditions, i.e., it reduces labour, water, inputs such as fertilizers, pesticides etc. and assures quality produce. With the satellite positioning system and electronic communication standards, position and time may be integrated into all procedures connected to farming to increase the production


The concept of precision agriculture first emerged in the United States in the early 1980s  by the advent of GPS and Remote sensing. Researchers at the University of Minnesota varied lime inputs in crop fields by using satellite as guide. Fertilizer that would have been spread in areas that don't need it can be placed in areas that do, thereby optimizing its use.

Practice of precision agriculture was enabled by the advent of GPS and remote sensing. Remote sensing is the science and art of collecting data by technical means on an object on or near the earth’s surface and interpreting the same to provide useful information. GPS uses the satellites in space to find the position anywhere on the earth. Both of these technologies provides data which can be used in geographical information system (GIS), which is a powerful tool for collecting, storing, organizing, retrieving, transforming and displaying data from the real world for a set of particular purposes (Burrough, 1986). GIS is a decision support system, which involves the integration of specially referenced data in a problem solving environment (Cowen, 1988).

Precision farming is well developed in agriculture sector (Precision agriculture- PA) and animal husbandry sector (Precision Livestock Farming –PLF).



Precision Livestock Farming is not well developed in aquaculture sector but it is used to identify and quantify appropriate sites for brackish water aquaculture development in many parts of the world using remote sensing, GPS and geographical information systems (GIS).But this technique is widely used in capture fisheries.

Precision agriculture is associated with site-specific fertilization, site-specific seeding and other agriculture practices. Expectations of farmers using the new technology are usually twofold — a decrease in fertilizer required for the same yield and: or higher yields with the use of the same amounts of fertilizer.

Precision Livestock Farming (PLF) in the field of monitoring is to develop on-line tools to monitor farm animals continuously during their life, in a fully automatic way, with objective measures and criteria calculated on-line from collected data and without imposing additional stress to the animals. The aim of these technical tools is not to replace but to support the farmer who always remains the crucial factor in good animal management. Besides on-line automatic monitoring, PLF offers also interesting possibilities in automatic control for supporting the management of such complex biological production processes (e.g. feeding strategies, growth rate control, activity control, see Morag et al., 2001; Halachmi et al., 2002; Aerts et al., 2003a, b;)

In the following sections Basic principles of Precision Livestock Farming and stages & tools used in Precision agriculture is discussed briefly for understanding the concept of satellite farming. The techniques discussed can be integrated and used in culture fisheries to increase the overall production of aquaculture.


  • Geo-location of data
  • Characterizing variability
  • Decision-making – two strategies for dealing with variability
  • Implementing practices to address variability
  • 3.1.1. Geo-location of data

    Geo-locating 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.
  • 3.1.2. Characterizing variability

    Intra and inter-field variability 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

    3.1 .3. Decision-making – two strategies for dealing with variability

    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:
    1. sampling: weighing biomass, measuring leaf chlorophyll content, weighing fruit, etc.
    2. remote sensing: measuring parameters like temperature (air/soil), humidity (air/soil/leaf), wind or stem diameter is possible thanks to Wireless Sensor Networks
    3. proxy-detection: in-vehicle sensors measure leaf status; this requires the farmer to drive around the entire field.
    4. aerial or satellite remote sensing: multispectral imagery is acquired and processed to derive maps of crop biophysical parameters.

    Decisions 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.(Wikipedia)

    3.1.4. Implementing practices to address variability

    New information and communication technologies (NICT) 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). Precision agriculture uses technology on agricultural equipment (e.g. tractors, sprayers, harvestors, etc.):

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


    Animals are complex, individually different and time-variant (meaning that they respond differently at different moments of time). Therefore, we say that animals are CIT systems (Complex, Individual and Time-variant) .Starting point in PLF is the recognition that each individual animal is such a CIT. To achieve favorable monitoring and control of such processes, three conditions must be fulfilled.

    1. The first condition is that animal variables must be measured continuously and this information is      analyzed continuously.

    2. A second condition is to realise accurate animal monitoring and management. At every moment a     reliable prediction (expectation) must be available on how the< animal variables will vary or how the     animal will respond to environmental changes.

    3. The third condition is that this prediction together with the on-line measurements are integrated in an      analyzing algorithm (a number of mathematical equations implemented in a microchip) to monitor or      manage the animals automatically and to achieve on-line monitoring of animal health, welfare, or      take control actions (climate control, feeding strategies,) (Breckmans, 2007).


    Presently satellite studies are being carried out to identify suitable sites in brackish water shrimp culture.

    Satellites can support aquaculture farmers and policy makers by:

    • identifying suitable sites

    • issuing warnings on potential water quality threats (e.g. pollution and harmful algal blooms)

    • monitoring the environmental impact of sea farms (GMES, 2012)


    Aerts J.-M., Wathes C.M., Berckmans D. 2003a. Dynamic Data-based Modelling of Heat Production and Growth of Broiler Chickens: Development of an Integrated Management System. Biosystems Engineering 84(3): 257-266.

    Aerts J.-M., Van Buggenhout S., Lippens M., Buyse J., Decuypere E., Vranken E., Berckmans D. 2003b. Active Control of the Growth Trajectory of Broiler Chickens based on On-Line Animal Responses. Poultry Science 82(12): 1853-1862.

    Berckmans D, 2004. AUTOMATIC ON-LINE MONITORING OF ANIMALS BY PRECISION LIVESTOCK FARMING. International Society for Animal Hygiène - Saint-Malo. pp 28-30

    GMES, • w, 2012

    Halachmi I., Metz J.H.M., van't Land A., Halachmi S., Kleijnen J.P.C. 2002. Case study: Optimal facility allocation in a robotic milking barn. Transactions of the ASAE 45 (5): 1539-1546.

    Karthik M. et al, 2005. Brackish water aquaculture site selection in Palghar Taluk, Thane district of Maharashtra, India, using the techniques of remote sensing and geographical information system. Aquacultural Engineering 32 (2005) 285–302

    Morag I, Edan Y, Maltz E. 2001. An individual feed allocation decision support system for the dairy farm. Journal of Agricultural Engineering Research 79 (2): 167-176.

    Rajitha K et al. ,2007 Applications of remote sensing and GIS for sustainable management of shrimp culture in India/ Aquacultural Engineering 36 (2007) 1–17.

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