4. Insect and pest control
The invasion of harmful insects and pests, or infestation, does heavy damage to agriculture. A look from above can enable accurate, timely alarms to prevent that.

Yet even high-resolution images might not provide visible early signs of infestation.

The alternative would be using AI. You develop a neural network and train it using deep learning algorithms. Through this training, you feed the neural network images of infested land, and the network learns to find samples that indicate infestation. After that, you feed it satellite images of the land you want analyzed.

As mentioned above, you can also use remote sensing along with geospatial technology in agriculture to check the temperature of the crops. Plants respond to infestation by heating up as they stop getting enough water or nutrition.

5. Irrigation control
Keeping an eye on vast fields to make sure that each crop gets enough water is a challenging task, but one easily tackled by geoinformatics in agriculture.

Aircraft and satellites equipped with high-resolution cameras take images that allow AI algorithms to calculate the water stress in each crop and spot visual patterns behind water shortages.

Pair those images with water delivery system maps, and you will find out how well your current irrigation scheme is performing.

6. Flooding, erosion, and drought control
Marrying GIS and agriculture can help prevent, assess, and mitigate the negative impact of destructive natural phenomena.

To identify flood-susceptible areas, you can use flood inventory mapping techniques. You need to collect data such as past floods, field surveys, and satellite images. Use those data to create a dataset to train a neural network to spot and map flood risks, and you will create an ultimate disaster management tool.

If you need to check land for susceptibility for soil erosion, you could pair Universal Soil Loss Equation (USLE) with GIS and remote sensing. Run satellite images through spectral analysis to check USLE factors and verify those images with field observations. As a result, you can create a map featuring the level of deterioration of the soil across the field.

Similar GIS solutions for agriculture can be used to control drought.

7. Farming automation
Seeding machines, intelligent irrigation systems, driverless harvesters, and weed remover robots are the inevitable future. You could equip each of your machines with sophisticated sensors, but why do that if you can connect them to an integral GIS system?

(That is not to say that automated vehicles don’t need sensors — they do.)

GIS in farming can provide precise maps, including all necessary information about the crops in the field. Maps like those are called task maps or application maps. Smart machines use them to tend to the field.

Here’s an example of how GIS solutions for agriculture might work. Once a GIS system has detected weed infestation, it assigns a “Weeding needed” label to that area. The weed remover robot reads the label and places this area on its list of tasks.

Apart from providing signals for machines, task maps can help unskilled workers do their job more efficiently.

Conclusion
If you browse the Internet for use cases of GIS in agriculture, you will find articles and studies dating back to the early nineties. The objectives of agriculture haven’t changed much since then — nor have the problems that GIS is expected to solve.

Source: Intellias