Using AI in precision agriculture eases farmers’ work by automating tasks like watering crops and enhancing crop yields. It makes informed predictions based on data gathered from sensors located on the farm and accessible through the internet. However, all this technology presents several cybersecurity threats. What challenges do farmers face, and how can they overcome them?

Cybersecurity Risks When Using AI

Agriculture is a vital component of the global economy, producing crops that are sold worldwide. Cybersecurity risks associated with AI can potentially lead to significant financial losses. These risks manifest in two ways: data corruption and machine malfunctions.

Corrupting Data

Data can be obtained by cybercriminals and manipulated to disrupt crop yields and other aspects of agriculture. Attackers can manipulate information, causing AI to make incorrect predictions or corrupt it during the training process, thereby making the system inaccurate. Training data can also be manipulated to trigger certain AI behavioral issues.

AI systems can be shut down or locked out by attackers tampering with the system. If farmers cannot access the information, then the whole farm could be in trouble. AI systems also share data with other farms in the network and utilize additional resources, such as satellites or weather predictors. Attackers can disrupt the channels where the information is coming through and cause data loss or inaccuracies.

Farming data can also be stolen by attackers and sold or manipulated to hurt crop yields. The login process can be hacked, allowing hackers to infiltrate the system. They can also forge a digital signature to make it appear as if they are the authorized user.

Machine Malfunctioning

The AI-controlled machines used to gather data can also be manipulated. Automated watering processes can monitor factors like soil moisture and temperature, and are becoming increasingly common in farm settings. However, the sensors that trigger the water can be manipulated, causing them to malfunction and either water the plants too much or too little, harming yields.

GPS systems are used for various agricultural processes, including determining the location of farms in relation to weather and identifying optimal times to plant and harvest. Attackers can jam the GPS with false information and cause it to provide inaccurate advice to farmers. Some AI machines are programmed to enter sleep mode when inactive, but hackers can exploit this feature and reduce battery life by forcing them to remain in sleep mode for extended periods.

Flash drives and SD cards store data for farmers to use in the future. Cyberattackers can hijack them with viruses that infect everything they’re plugged into later. Charging cables can also be exploited by hackers.

Strategies for Overcoming Risks

The cybersecurity risks associated with implementing AI into precision agriculture are substantial. The FBI reported that one farm lost $9 million of productivity after an attack. Developing strategies to mitigate these threats is crucial for continuing to use AI in agriculture safely.

1.Involve the Government

The government can introduce regulations that protect farming data while also allowing it to be shared to secure datasets. This should keep information more secure while still allowing farmers to benefit from the AI network.

2.Modify Training Models

Creating learning techniques that an attacker cannot easily poison when AI is being equipped to help a farm would reduce the risks. Since many attacks occur during training, a more robust system to block potential manipulation would be highly beneficial.

3.Create Detection Methods

Many areas of detection should be introduced to catch threats early and shut them down before they do irreparable damage. Areas for detection include models, data, code and adversarial.

4.Utilize Multifactor Identification

Most AI systems require farmers to access the data using a login. Having more than just a password creates multiple barriers between an attacker and the information, stopping many potential attacks and protecting information.

5.Secure Data With Backups

Having backups for any data that attackers could steal also limits risk. Farmers can also store information that preserves their farming progress before a breach occurs, minimizing losses that could arise from infiltration.

6.Separate Farming Networks

Different networks where farming data can be found, such as predicted weather, crop yields or automated water schedules, should be kept separate from each other. So, if an attacker breaches the system, they only have access to one section of information and are blocked from the others.

7.Train Workers

Farmers and their workers should be trained on how to utilize AI and recognize suspicious behavior to detect attacks as quickly as possible. Knowing what to look for and having failsafes in place to manage it are beneficial when needed.

8.Practice Caution

As with any AI application in any field, it is essential to practice its use with caution. Human monitoring is still necessary to ensure the system is performing its intended function, particularly in identifying threats.

AI in Agriculture

Precision agriculture is leveraging AI to automate processes and increase crop yields per season. However, its use brings an increased threat of cybersecurity attacks that manipulate data and harm operations. Taking strategies to mitigate the negative impact of these effects, as well as preventing them altogether, is crucial in helping farmers utilize this new technology effectively.