7 Ways How GIS in Agriculture Eliminates Guesswork - P1
Find out how using GIS in agriculture puts field data to work to maximize yields, reduce expenses, and mitigate damage from natural disasters
GIS in agriculture has been boosted by the general advancement of technology in the past few decades.
The use of GIS in agriculture is all about analyzing the land, visualizing field data on a map, and putting those data to work. Powered by GIS, precision farming enables informed decisions and actions through which farmers get the most out of each acre without damaging the environment.
Speaking of tools, geospatial technology in agriculture relies on satellites, aircraft, drones, and sensors. These tools are used to make images and connect them with maps and non-visualized data. As a result, you get a map featuring crop position and health status, topography, soil type, fertilization, and similar information.
There are several applications of geoinformatics in agriculture. Let’s have a look at some of them.
From this article, you’ll learn about the following applications and use of GIS in agriculture:
- Crop yield prediction
- Crop health monitoring
- Livestock monitoring
- Insect and pest control
- Irrigation control
- Flooding, erosion, and drought control
- Farming automation
1. Crop yield prediction
Accurate yield prediction can help governments ensure food security and businesses forecast profits and plan budgets. The recent development of technology connecting satellites, sensing, big data, and AI can enable those predictions.
One of the most profound techniques in this field is Convolutional Neural Networks (ConvNets or CNNs). A ConvNet is a deep learning algorithm that is taught to identify the productivity of a crop. Developers train this algorithm by feeding it images of crops whose yield is already known to find productivity patterns. CNN has an accuracy of about 82%.
A crop-prediction technique workflow
2. Crop health monitoring
Checking crop health across multiple acres manually is the least efficient option. This is where remote sensing combined with GIS in farming comes to the rescue.
Satellite images and input information can be paired to assess environmental conditions across the field, such as humidity, air temperature, surface conditions, and others. Based on GIS, precision farming can upgrade such an assessmen and help you decide which crops require more attention.
A more sophisticated approach uses imaging sensors on satellites and air vehicles to check the temperature of crops. When the temperature is above normal, this might indicate a disease, infestation or insufficient irrigation.
Neural networks like CNN, Radial Basis Function Network (RBFN), Perceptron, and others can be helpful in assessing crop health too. The algorithms can analyze images for unhealthy patterns.
3. Livestock monitoring
The simplest application of farm GIS software in animal husbandry is the tracking of movement of specific animals. This helps farmers find them on a farm and monitor their health, fertility, and nutrition. GIS services that allow you to do that comprise trackers installed on animals and a mobile device that receives and visualizes information from those trackers.
Here’s one example. You want to monitor the weight of your beef cattle. Each animal has a tracker on its ear or neck. Every time it steps on the digital scales, the scales read the ID of that animal and assign a new value to that ID in the system.
You don’t need to manually enter that data. Meanwhile, if there’s an alarming change in the animal’s weight, you can quickly find that animal and check its health.
There are also more interesting use cases of farm GIS software, such as preventing wolf-cattle encounters. There are ambiguous spatial specifics that affect the distribution of wildlife in an area, including wolves. We could reduce undesirable encounters by understanding those subtle specifics, which could be done by the combined use of AI and GIS in agriculture.