The agricultural community is confronted with dual challenges; increasing production of nutritionally dense food and decreasing the impacts of these crop production systems on the land, water, and climate. You might have come across various methods of practicing agriculture that involve certain practices that either differs totally or slightly from the traditional agriculture that we practice. Today let’s take a closer look at one such method of practicing agriculture “PRECISION AGRICULTURE”.


Precision agriculture, or precision farming, is a farming concept that utilizes geographical information to determine field variability to ensure optimal use of inputs and maximize the output from a farm. 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, and 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.


Geospatial data, also known as geodata, has locational information connected to a dataset such as address, city or ZIP code. Geospatial data can also come from Global Positioning System (GPS) data, geospatial satellite imagery, telematics devices, IoT and geotagging.

Geospatial technology is used to collect, analyze and store geographic information. It uses software to map geographic locations while analyzing the impact of human activity. Geographic Information System (GIS) uses digital software to combine maps and datasets about environmental events and socioeconomic trends. GIS creates layered maps to better analyze complex data. The layering is possible because each data point is connected to a precise location on Earth. Other forms of geospatial technology include GPS, remote sensing, and geofencing.

Geospatial data can provide information on numerous parameters such as: –

  1. Soil types.
  2. pH rates.
  3. Pest infestation.
  4. Nutrient availability.
  5. Soil moisture content.
  6. Fertility requirements.
  7. Weather predictions.
  8. Crop characteristics.
  9. Hybrid responses etc.


Due to the patchiness of disease in production fields, the precision agriculture approach for disease management can potentially decrease pesticide use by applying pesticides only as needed in the field and thereby decrease environmental impact of the crop production system. Encouragingly, in a French study with 946 non-organic farms, high profitability remained with low pesticide use in 77% of the farms analyzed. The authors estimated that pesticide use could be reduced 42% without any negative effects on productivity in 59% of the farms. Precision agriculture could also decrease disease, hence pesticide use, in production fields by improving management of cropping system components such as fertilization and irrigation. Unbalanced fertilization and improper water conditions in field soils have been shown to increase the presence of certain diseases and pests.


Many methods are available to determine the spatial distribution of pathogens and disease in grower fields with visual determination by human experts being the most common. A human expert cannot efficiently evaluate all plants within a large area. Surveillance with aerial optical sensor platforms are attractive for this role as they offer the potential to capture and analyze, using the latest analytical tools, data from large crop acreage for disease diagnosis and determination of disease severity. Aerial sensors have been used to detect Cotton Root Rot, damage from root-knot nematodes, Late Blight on tomato, Northern Leaf Blight on corn, diseases on perennial/orchard crops, and other diseases in fields.

However, there are challenges with the remote sensing approach. Variable environmental conditions impact sensed plant characteristics and there is a need to differentiate among various abiotic and biotic stresses that may share similar spectral signatures. Also, intrinsic plant properties such as plant architecture and growth phase variation, and high sensitivity required for decision-making in disease control create challenges. Remote sensing is effective for precision management optimization for soil-borne diseases and nematodes.


Variable rate and site-specific pesticide application have potential to reduce pesticide application and thereby reduce environmental impacts. In many cases, by the time disease is evident using aerial sensors it is too late to apply control measures to prevent further damage to the crop. Therefore a system of human supervision, on-field sensors and remote sensing is used to access a field for any disease infestation. When disease infestation is made sure, selective application of required pesticides or bio-pesticides can be done.

A caveat regarding precision disease control is that in addition to connectivity and other infrastructure needs, there is a need to consider the increase in complexity of management of disease and the cropping system with this approach. These precision disease control systems need to be developed and deployed with the farmer in mind as many technologies are necessary for their implementation. Not all farmers have the knowledge and skills required to integrate all necessary technologies into a disease management system.


Although usage of geospatial data in precision farming is under rigorous research and development, it still promises huge impact over the way we practice agriculture. It would be interesting when it is released with full potential for commercialization in the agricultural fields as one can expect widespread benefits not only to the farmers but as well as to the environment.  

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