The old-fashion infrastructure meets modern technologies, involving communities. The agriculture industry has always experienced slow growth.
The impact of IoT challenges on the agriculture industry
The agriculture industry has always experienced slower growth compared to other sectors. Mainly because of food deficits and hunger problems in some areas of our planet. These are often related to the distribution and imbalance of farming, climate changes, urban influences, industrialization processes and usage of chemicals, as well as the replacement of small farms by industrial plantations. Private farmers still prevail in producing essential crops such as wheat, rice, and maize. Although they receive lower returns, mainly due to the ineffective supply chain and the absence of a proper market connection. Often during the transportation, carriers are not prepared to keep the right temperatures. They also reroute vehicles because of unexpected events. The opportunity to have a look into the real-time conditions inside fleet transport and applying weather predictions can result in a significant impact on the food industry and farmers benefits.
Data sharing in the agriculture industry
Artificial intelligence and new technologies create a great mixture of digital systems across agriculture, primarily through data sharing. Data flows in from a number of sources — field-based sensors, aerial sources and environmental data, as well as remote sensing data coming from various satellites. At the same moment, data distribution causes its own unique challenges. Typically, farmers are extremely concerned about access to their data and how it might be used against them. There two popular concerns: Could someone use the relevant data to influence the market prices? What if the government could use it to sue the farmer for violating regulations?
If we put these matters aside, there are enormous advantages to sharing data in the agriculture industry. Nowadays, many farmers who share data with their bank or insurance company receive lower interest rates or rewards. Therefore, the principal focus for IoT in agriculture should be transparency and control over data usage. But to achieve this, it is essential to assemble the right supervision from industry, government and researchers. For example, to warrant the precision farming concept, single agriculture industries will need to store logs with information about equipment, proving that it is up to date and operating accurately.
Data security in the agriculture industry
As in any other industry, farmers need to think about security to use technology in the right way. The precision farming concept naturally brings the agriculture sector to the risk of hacking and data theft. Additionally, there is a significant lack of information about data protection in this field. The most known cases occur because of hacktivists who destroy data to protest the use of genetically modified organisms or pesticides.
Farm equipment providers use IoT devices and data analysis practices to make farming more efficient. However, agriculture is still prevailing with old technologies that often do not involve the data security concept as well as a robust data backup. For example, some field monitoring drones connect to farm equipment. This equipment is often linked to general channels and the internet, yet usually it does not include basic security features like monitoring of employee logins or two-factor authentication for remote access sessions.
IoT as a service in the agriculture industry
Nowadays, many farm equipment companies provide integration of IoT as a service. The precision farming concept plays an enormous role in their business. Gathered real-time data provides better visibility to the farmer, and the knowledge of a specific territory can be easily shared with the community. Because of this, precision crop farming systems should include capabilities for device management, data storage, security and deep analytics on the data ingested from on-field sensors, aerial imagery and remote sensing techniques. It will generate real-time insights out of data for the farmers and support the agro-scientists with better decision-making.
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