Smart farming technologies and solutions have led to the accumulation of vast amounts of intricate data in the agriculture sector. While farmers have control over certain variables like crop variety, seeding time, and fertigation rate, there are numerous other factors, such as environmental conditions, that are beyond their control. These variables are highly complex and interconnected, making it challenging to identify correlations between input factors (e.g., time intervals for applying fertilizers) and desired outcomes (e.g., expected crop yield). This is where artificial intelligence (AI) demonstrates its capabilities in agriculture. AI is adept at tackling multifaceted problems characterized by multiple dimensions and significant variation, even in the presence of random elements.
The integration of advanced technologies into artificial intelligence (AI) has revolutionized various aspects of operations, including sensing, processing, and network communication. In recent years, AI in agriculture, specifically in the context of smart farming, has witnessed remarkable progress. Governments worldwide have shown a growing interest in this field, leading to increased research and development initiatives. An example of this support can be seen in the United States, where the federal government allocated a substantial budget of $973 million in 2019 for non-defense AI-related technologies and innovations, including those in agriculture. In addition, the government has taken steps to remove regulatory barriers, promoting the secure development and testing of Artificial Intelligence (AI) in agriculture market technologies. These efforts aim to foster the creation of new AI-based enterprises and encourage existing ventures to adopt AI technology. Notably, a dedicated portion of the federal budget for research and development is specifically reserved for advancements in artificial intelligence.
A wide range of agriculture technologies are available for various applications throughout the farming cycle, encompassing tasks such as data management, soil management, yield mapping, monitoring, spraying, harvesting, and planting. The global adoption of these technologies has led to the rapid accumulation of vast amounts of agricultural data pertaining to on-farm operations and fields. Managing this "big data" has become an urgent requirement in order to achieve higher accuracy, productivity, and minimize the need for manual data input. This is where the emergence of Artificial Intelligence (AI) in agriculture market has been widely embraced by farmers and other stakeholders. AI technology plays a crucial role in optimizing agricultural operations by utilizing the available data, resulting in innovative applications and improved outcomes. One of the most prevalent use cases of AI in agriculture is data analytics, which enables efficient farm production and reduces losses by leveraging advanced analytical techniques.
Cloud AI deployment software pertains to the utilization of AI services and platforms for data processing and generating valuable insights to facilitate decision-making. It involves leveraging the scalability of cloud infrastructure and the convenience of accessing AI services from anywhere with an internet connection. This approach focuses on deploying AI models on local hardware within an organization's premises, rather than relying on remote servers. Consequently, the AI model is installed and executed directly on the hardware within the organization, eliminating the need for a cloud-based service.
An instance of AI deployment software in agriculture can be seen in the application of computer vision algorithms to identify and classify different crops or pests. This software would require installing the AI model on local servers or computing devices, along with the use of local hardware like cameras or sensors to gather relevant data.
APIs, or application programming interfaces, enable companies to share the data and functionality of their programs with external developers, business partners, and internal divisions within their organizations. Through a defined interface, services and products can interact and utilize each other's data and capabilities. The user interface (UI), on the other hand, pertains to the means of human-computer interaction and communication within a device. This includes elements such as desktop displays, keyboards, mice, and other pointing devices. It also encompasses the way users engage with websites or applications. The field of human-computer interaction (HCI) aims to optimize user-computer interaction by designing interactive computer interfaces that cater to user needs. In the context of agriculture, AI models can be utilized within HCI to collect agricultural data and present them to farmers, enhancing their decision-making process.
The global market for artificial intelligence in agriculture is expected to experience substantial growth between 2022 and 2027, with notable advancements projected in North America, Europe, Asia-Pacific, and other regions. This growth can be primarily attributed to the growing integration of technology in the agricultural sector, coupled with government initiatives aimed at fostering its development. Supportive policies implemented by governments, along with the economic benefits derived from large-scale farming operations, have led to increased farm consolidation, particularly in developing nations. This trend has further contributed to the expansion of the artificial intelligence in agriculture market on a global scale.