The significance of digitalization within the agricultural domain is underscored through the portrayal of digital technologies as pivotal components facilitating the enhancement of operational efficiency and sustainability within the sector. The advent of digital farming, utilizing instruments such as sensors, drones, the Internet of Things (IoT), and comprehensive Big Data analysis, is instigating a transformative wave in the sphere of agricultural management. Presently, cultivators have the capacity to adopt precision agriculture methodologies, delving into the analysis of data pertaining to soil composition, meteorological conditions, and crop growth patterns. This analytical approach facilitates the optimization of productivity while concurrently diminishing reliance on chemical inputs. Notably, the deployment of drones furnishes a topographical vantage point, enabling the evaluation of terrain, acquisition of plant-related data, and pre-emptive measures against diseases.
The current era is marked by profound uncertainty regarding the future developments in the relationship between artificial intelligence (AI) and humans, presenting unprecedented entrepreneurial opportunities. There are instances of companies replacing workers with machines, yielding significant cost improvements. AI has become omniscient, now possessing ultra-fast generative capabilities, surpassing the passive access offered by search engines. Industries such as call centers, legal consultancy, and administrative personnel are at risk, while others, like builders and chefs, may remain unaffected, but robotics could lead to inevitable replacements. Drawbacks include unemployment, lack of transparency, distorted algorithms, profiling and privacy issues, misinformation dissemination, environmental impact, and the dominance of large technology companies.
The future of human labor could evolve into two scenarios: collaboration with AI or total replacement. Addressing these critical aspects is necessary to maximize benefits and mitigate risks associated with AI. The overall effect of digitalization is more efficient management of agricultural businesses, reducing waste, enhancing environmental sustainability, and improving product quality. However, challenges persist, such as limited connectivity in rural areas, low awareness of benefits, interoperability issues, and the need to address costs and data privacy.
The challenges of population growth and limited resources pose a significant obstacle to agriculture. Artificial intelligence is crucial for digital agriculture, enhancing decision-making through techniques like precision farming. This involves using digital tools to collect and analyze agricultural data, implementing artificial vision and machine learning to monitor soil conditions, identify diseases and deficiencies, and optimize the use of fertilizers and water.
Autonomous agricultural robots, such as drones and land vehicles, are employed for specific tasks like monitoring crops and targeted application of herbicides. However, challenges such as payload capacity and flight duration limit their scalability. Advanced land robots with robotic arms and specialized sensors are required for tasks like pruning and selective harvesting. AI is employed for the localization of agricultural robots, real-time monitoring of crops, and coordinated management of various agricultural activities, such as irrigation and chemical application. The application of AI in agriculture contributes to maximizing crop yields, addressing environmental challenges, and mitigating threats to food security.
Another example is VinAi Farm, a decision support system for winemakers. Using climate data analyzed with AI-based predictive models, the system anticipates the onset of vineyard pathologies. VinAi Farm provides disease monitoring, a historical record of climate data, and efficient planning of agricultural activities. Functions include a distributed system of weather stations, artificial intelligence for monitoring vineyard health, and environmental conservation through more precise decisions on the use of phytosanitary products.
Such tools not only automate and simplify agricultural activities but also introduce data analysis and processing, enhancing efficiency and precision in the sector. Examples include the development of optical sensors for fruit counting, autonomous tractors, self-moving robots for autonomous cultivation, hybrid robots for vineyard treatment, decision support systems for winemakers, aerial photogrammetry, crop monitoring, intelligent irrigation systems and localized spraying systems.
The trend towards using electric power is evident in the development and prototyping of various technologies recently emerged in agriculture. This preference leads to a reduction in gas emissions, with consequent benefits in terms of sustainability. In response to global population growth, farmers are adopting smarter techniques to efficiently manage land, water, and energy, contributing to preventing a global food catastrophe. Sensors, robots, and AI are considered crucial keys in addressing these challenges. AI technology, already successfully adopted in various industries, is poised to revolutionize agriculture through the use of drones, robots, and intelligent monitoring systems.
Looking to the future, the role of AI in the evolution towards Agriculture 4.0 is crucial. AI facilitates the recording of business data, monitoring agricultural activities, analyzing economic performance, improving animal and environmental health. These features converge towards the goal of smart agriculture and smart farming. AI leverages data to enhance the quality and clarity of decisions at all levels of the agricultural industry. Currently, the development of AI in agriculture is considered still in its early stages, with many solutions yet to achieve widespread adoption. Despite the prospect of AI surpassing humans in assessing food compliance with market specifications, its increasing integration in agricultural enterprises underscores the crucial role that artificial intelligence and sensor technologies are assuming in the agricultural sector. AI is a valuable resource capable of addressing challenges in modern agriculture such as climate change, growing food demand, and sustainable resource management.