According to United Nations (UN) projection, the world’s population is expected to surpass 9.7 billion by 2050, increasing the number mouths to feed by 2.7billion. This increase according to FAO, needs to be met through a 70% rise in agricultural production (FAO, 2009) The scenario thus raises the query as to how the growing demand for food is going to be met with a constant decrease in arable land.

Embracing technological advancements; Internet of things (IoT), Smart Farming (the act of using modern information and communication technologies to increase the quality of produce while optimizing human labour required) and Drones/ Unmanned Aerial Vehicles (UAVs) offer a reprieve. IoT and UAVs augment each other to facilitate seamless smart farming practices.

At its core (smart farming) is crop health management and monitoring - the process of periodically and systematically observing, accessing and collecting data about crops throughout their growth cycle with aim of making informed decisions regarding crop management practices, optimizing resource allocation and maximizing yields. It is at this stage that the farmer either makes it or breaks it and yields are directly dependent on the actions taken here.

Today, drones are used in almost all industries with agriculture being a major benefactor. Nearly all phases of farming from pre-planting to post harvest in the agriculture value chain use drones directly or indirectly.

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Some of the tasks done by drone include but are not limited to;

LAND PREPARATION

  • Survey and mapping: Aerial surveying facilitates rapid land survey determine the actual acreages of the parcels to be planted for planning.
  • Topographical mapping: High resolution imagery captured by drones creates accurate topographical maps and Digital Elevation Models (DEMs) vital for levelling and drainage planning ensuring water is evenly distributed across the fields averting waterlogging.
  • Soil Analysis: Drones with electromagnetic induction and multi-spectral payloads can access the soil providing detailed information about the soil quality, moisture levels, pH, and nutrients content enabling farmers make informed decision about soil amendments and type of fertilizer to use.
  • Land-use optimization: Aerial Imagery is a planning tool that enables the farmer to optimize land use by identifying areas with potential yield limitations and make adjustments in a way that utilizes available space.

CROP MANAGEMENT

  • Fertilizer & Pesticide application: Spray drones offer precise application of fertilizer and pesticides based on real-time needs reducing wastage compared to other techniques such as aircrafts and tractors.
  • Pest and Disease detection: Drones with multispectral and hyperspectral sensor cameras can identify pest infestations and disease early enabling targeted treatment.

HARVESTING & POST-HARVEST

  • Harvesting: Crop scouting using remote sensing techniques, multispectral payloads can be used to locate mature crops and guide autonomous harvesters to the right areas.
  • Yield estimation: By analysing plants health and density drones provide crop yields estimates and produce yields maps depicting spatial variations in expected yield across the field assisting the farmer to optimize logistics and timing of harvesting operations.
  • Storage monitoring: Drones equipped with thermal mappers are used to inspect storage facilities to check for temperature and humidity level thus preventing spoilage.
  • Transportation: With the cost of fuel rising and climate changing at an alarming rate, the need for green energy has never been at its peak, transportation drones can assist farmers in transporting produce to processing centers reducing the carbon footprint of diesel-powered machines.

Advancing Agriculture: Overcoming Limitations in Crop Health Monitoring through IoT, Sensors, and UAV Technology

Crop health monitoring used to be done manually before the introduction of satellites, remote sensing, IoT, and UAVs, by visiting farms and collecting physical samples. Satellites ushered in a paradigm change away from physical means and toward the use of Remote Sensing Techniques, allowing enormous areas to be examined at once.

This, however, has its own restrictions. For example, the high cost of imagery gathering hampered many farmers, low resolution photography limited the amount of analysis that could be done, and its vulnerability to weather fluctuations meant that the data acquired fell short of the optimal level required for crop health monitoring.

With the development of the Internet of Things (IoT), sensor actuation platforms, and unmanned aerial vehicles (UAVs), a platform that might be used to overcome the limitations of satellites was offered. There are now multispectral sensors available. They were installed on satellites that scan the planet perpendicular to their orbital path, collecting light energy at various wavelengths that are both visible and invisible to the human eye.

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Multi-spectral and Hyper-spectral sensors payloads captures high resolution imagery carrying information in the range of infrared and visible light (RGB) part of electromagnetic spectrum.

Soaring Heights: Revolutionizing Agriculture with Drone Technology

A Comprehensive Exploration

These images can be analyzed using vegetation indices to give insights about the crop’s health. The indices are used at different stages of crop phenology giving different results. NDVI - Normalize Difference Vegetation Index, GNDVI- Green Normalized Vegetation Index and NDWI- Normalized Difference Water Index.

GNDVI

GNDVI is a vegetation index for estimating photosynthetic activity. It uses the Near Infrared and Green band of the electromagnetic spectrum which is more sensitive to chlorophyll variation thus majorly used to determine water and nitrogen uptake into the plant canopy. Predominantly applied in the intermediate and final stages of the crop cycle.

GNDVI= [NIR-GREEN]/[NIR+GREEN].

The values range from [-1 to 1] with [-1 to 0]- indicates presence of water of bare soil.

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NDWI

NDWI is a vegetation index used to monitor the water status of the crop. It identifies moisture deficit and saturation in the crop. It Utilizes Green & NIR bands and is mostly used in conjunction with NDVI change products to access the content of apparent change areas.

NDWI=[NIR-WIR]/[NIR+SWIR].

The values range from [-1 to 1] with high values representing high plant water content and coverage while low values representing low vegetation water content and sparse coverage.

NDVI

NDVI is metric for quantifying health and vigor of vegetation. It’s based on the principle that a healthy crop has high chlorophyll content (green coloring matter) and would thus absorb the blue and red light while reflecting green, thence appearing green when viewed by naked eye. It has 3 variants namely; NDRE, MSAVI2 and VISIBLE.

NDVI = (NIR-RED)/(NIR+RED).

NDVI Values:

  • Negative Values- [ -1 to 1] this indicates non￾vegetated/ barren surface I.e., water bodies, bare soil or urban areas. It normally appears blue or black on the color map.
  • Low Values - [0 to 0.2] indicates sparse, stressed or unhealthy vegetation, this could be due to water stress, minerals insufficiency or pest infestation. It normally appears red on the color map.
  • Modern values- [0.2 to 0.5] indicates healthy vegetation at not at its peak, usually brownish/greenish in color.
  • High values- [0.5 to 1] indicates healthy and vigorous vegetation likely to have sufficient nutrients and moisture. It is usually green on the color map.

With the map, farmers and agronomists can perform ground truthing by visiting the farms to validate the interpretations and make necessary changes for optimal yields.

NDVI Variants.

NDRE- Normalized Difference Red Edge.

It’s similar to NDVI but used to detect variations in crop health at an advanced stage. It uses red-edge light that penetrates leaves much deeper than the red light in NDVI making it suitable for generating nitrogen fertilizer maps.

NDRE= [NIR-RED EDGE] / [NIR + RED EDGE].

MSAVI2

Modified Soil Adjusted Vegetation Index.

It’s used in areas where NDVI and NDRE provide inconclusive or incomplete data mainly due to lack of vegetation or chlorophyll. Its ability to minimize the influence of bares soil makes it ideal for crop monitoring in its early stages like germination rate and crop emergence.

MSAVI2= [2*NIR+ 1-1 √(2*NIR+1) ^2 - 8*(NIR-RED) ^2] /2.

As I conclude, a revolution dubbed the “Fourth Agricultural Revolution” has been born from the union of technology and agriculture and is expected to revolutionize our farming methods. With a bird’s eye view that enables farmers to make knowledgeable decisions about their farms, drones have come to be recognized as the quiet sentinels of our fields.

Drones provide a potent instrument to solve the issues of food security and environmental conservation as we traverse the constantly changing landscape of sustainable and effective farming. These drones examine our fields and plant the seeds for a more sustainable agricultural future with each flight.

The sky is not the limit, but rather a launchpad for creativity, therefore as we say goodbye to this piece, let’s keep that in mind. Accept the drone revolution, and observe your fields prosper unlike anything you’ve ever seen. The farming of the future is hovering above us, ready to take your farming operations to new heights.