The implementation and effectiveness of geographic information systems and location intelligence technology in digital agriculture

By 2050, the world population is expected to be close to 10 billion, so in order to meet the demand, we would need to produce nearly 50% more food than we did in 2013.

Recent technological advancements have made it possible for us to considerably increase agricultural production in order to feed the world’s population, which is increasing exceedingly fast. The fourth agricultural revolution has begun thanks to advances in digital technologies, but there are still many obstacles to overcome, including a lack of cropland, declining water supplies, and climate change. As a result, we must take steps to ensure agricultural resilience in order to feed the world’s population. The geographic information system (GIS), in conjunction with other partner technologies like remote sensing, the global positioning system, artificial intelligence, computational systems, and data analytics, has been playing a crucial role in crop monitoring and in putting the best and most targeted management practises into place in an effort to increase crop productivity.

GIS in agriculture is mostly used to analyse the land, visualise field data on a map, and then utilise that data. Precision farming, which is supported by GIS and GNSS technologies, helps farmers make well-informed decisions and take appropriate action to maximise yields per acre while minimising environmental impact.

The tools that geospatial technology in agriculture uses include satellites, planes, drones, and sensors. With the aid of these tools, images may be created and linked to maps and non-visualized data. As a consequence, you receive a map with details on topography, soil type, fertiliser, crop position, health, and other topics.

Geoinformatics has a range of applications in agriculture. Let’s explore a few of them.

Crop yield prediction

Governments can assure food security with the use of accurate yield prediction, and corporations can forecast revenues and create budgets. These predictions are possible thanks to recent technological advancements linking satellites, sensors, big data, and AI.

Convolutional Neural networks (ConvNets or CNNs) are among this field’s most profound methods. A deep learning algorithm called a ConvNet is trained to recognise a crop’s productivity. To uncover productivity patterns, developers train this system by feeding it photos of crops whose yield is already known. The accuracy rate for CNN is roughly 82%.

Crop health surveillance

The least effective method is manually inspecting the health of numerous acres of crops. In farming, remote sensing and GIS work together to solve this problem.

To evaluate environmental variables across the field, such as humidity, air temperature, surface conditions, and others, satellite photos and input data can be combined. Precision farming, which is based on GIS, can improve such an assessment and assist you in determining which crops need further care.

An advanced method for monitoring crop temperature makes use of image sensors on satellites and aircraft. If the temperature is higher than usual, this could be a sign of sickness, an infestation, or inadequate watering.

Crop health can also be determined using neural networks like CNN, Radial Basis Function Network (RBFN), Perceptron, and others. The computers can look for unhealthy trends in photos.

Livestock monitoring

The tracking of specific animals’ movements is the simplest use of farm GIS software in animal husbandry. This enables farmers to locate them on a farm and keep an eye on their nutrition, fertility, and health. Trackers attached to animals and a mobile device that receives and displays data from those trackers are two GIS services that make it possible to achieve that.

Let me give you one. You should keep an eye on the weight of your beef cattle. Every animal has a tracker attached to its neck or ear. The digital scales scan the animal’s ID each time it steps on them and update the ID’s value in the system.

That information doesn’t need to be manually entered. You can locate the animal right away and assess its health if its weight suddenly changes in a worrying way.

Additional intriguing applications of agricultural GIS software include avoiding wolf-cattle encounters. The location-specific distribution of wildlife, especially wolves, is influenced by confusing spatial characteristics. Understanding those minute details, which could be accomplished by combining the use of AI and GIS in agriculture, could help us reduce unfavourable interactions.

Insect and pest control

Agriculture suffers significant harm from the infestation of dangerous insects and pests. A bird’s-eye view can help create precise, timely alarms to stop that.

However, even photos with excellent resolution could fail to show early symptoms of infection.

AI use would be an alternative. Making use of deep learning algorithms, you create a neural network and train it. By feeding the neural network pictures of infested land during this training, the network learns to recognise samples that point to infestation. The land you want to investigate is then fed to it in the form of satellite photos.

As indicated above, remote sensing and geospatial technologies can be used in agriculture to measure the temperature of the crops. When an infestation occurs, plants respond by heating up since they aren’t receiving enough water or nutrition.

Managing irrigation

Geoinformatics in agriculture can easily handle the difficult task of monitoring broad fields to make sure that each crop receives enough water.

Images captured by aircraft and satellites using high-resolution cameras enable AI algorithms to determine the water stress in each crop and identify visual patterns behind water shortages.

You can determine how well your current irrigation system is working by combining those photographs with water delivery system maps.

Preventing erosion, flooding, and drought

Agriculture and GIS can work together to prevent, evaluate, and lessen the effects of damaging natural events.

Utilizing flood inventory mapping techniques, you may locate regions that are vulnerable to flooding. You must gather information on previous floods, field studies, and satellite photographs. Create a dataset using those data to train a neural network to identify and map flood risks, and you’ll have the perfect tool for disaster management.

The Universal Soil Loss Equation (USLE) can be used in conjunction with GIS and remote sensing to assess the vulnerability of a piece of land to soil erosion. Run spectral analysis on satellite photos to validate USLE factors, and then confirm those images with field measurements. As a result, you may make a map showing how much the soil has deteriorated throughout the field.

Drought management techniques for agriculture can be applied.

Final thoughts

The number of applications and prominence of GIS have grown significantly in recent years as a result of advancements in digital technologies, which have been leveraging GIS as an essential partner technology for evaluating crops, soils, and their environments.

At every point along the agricultural value chain, GIS is used. The development of digital agricultural tools and technologies has increasingly taken advantage of the capabilities of GIS in new and emerging applications in high quality crop monitoring, yield prediction, precision farming, and supply chain management for both primary produce and biomass utilisation towards energy production.

Precision farming can increase the productivity and profitability of farms through the use of location and spatial intelligence, and GIS offers a wide range of capabilities and insights, including recent improvements to gather and analyse data in real time. GIS is essential for ensuring sustainable agricultural productivity thanks to its current and future applications, together with older and newer partner technologies.

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