This article provides insights into the challenges facing modern agriculture and how artificial intelligence serves as an innovative solution. It briefly discusses systems for data analysis, agricultural robots, and algorithms that predict crop yields and optimize resource usage.
To achieve higher yield quality sustainably and cost-effectively manner, digital farming and site-specific precision management reduce dependency on labor in modern agriculture. Nowadays, agricultural scientist, farmers and growers face the challenge of producing more food on less land.
The technologies which are AI-based has contributed to improve efficiency in all the fields including the agricultural tasks such as the crop yield, irrigation, soil content sensing, crop- monitoring, weeding, crop establishment.
AI has contributed to agriculture in various ways, for example, the images recognition and perception has contributed with the recognition, and surveillance, human body detection and geo-localization, search and rescue, forest fire detection. AI makes it possible for farmers to assemble large amount of data from government as well as public websites, analyze all of it and provide farmers with solutions to many ambiguous issues as well as it provides us with a smarter way of irrigation which results in higher yield to the farmers.
AI will blend technological and biological skills in farming in the near future which will not only serve as a better outcome in the matter of quality for all the farmers but also minimize their losses and workloads. UN states that, by 2050, 2/3rd of world's population will be living in urban areas which arises a need to lessen the burden on the farmers. AI in agriculture can be applied which would automate several processes, reduce risks and provide farmers with a comparatively easy and efficient farming.
Greater information availability necessitates deeper understanding. The practical application of directly usable agronomic knowledge has stagnated. Nowadays it still requires a considerable mass of statistics and software expertise for comprehensive application of precision agricultural technology. Agricultural field robots on the other hand contribute to increasing the reliability of operations, improved soil health, and improved yield. They are generally equipped with two or multiple sensors and cameras for navigation control, simultaneous localization localization and mapping, and path planning algorithms.
While some robots are still prototypes, they can perform various farming operations that can be subjected to an extremely dynamic environment. For example, the demander of season cultivation of fruits and vegetables require different aspects of automation and robotics in close-field plant production like greenhouses. There are multiple characteristics of the crops that need to be taken in account during the field robot for spraying or harvesting work, such as plant sizes, shapes, stems, branches, leaves, fruit colors, texture, obstacles and weather influences in order to operate efficiently. The sensing mechanism has to be delicate identifying the ripeness of fruits in the presence of multiple disturbances.
Weed control and targeted spraying robots for example, represent cutting-edge advancements in agriculture can distinguish between crops and weeds with high accuracy, they are pivotal in modern agriculture's quest for productivity while maintaining ecological balance because they can reduce the use of spraying agrochemical and pesticide on the field by 80%-90%.
The integration of AI and robotics in agriculture presents a promising path towards sustainability and efficiency. The ability to harness vast amounts of data through AI-driven solutions holds tremendous potential to optimize farming practices and mitigate environmental impact. However, the effective utilization of these technologies requires a deep understanding and expertise from users to ensure they yield efficient and beneficial outcomes.
Robots equipped with AI metrics and precise accuracy offer adaptive capabilities that can adjust to changing environmental conditions, enhancing productivity and resource management. Yet, it is crucial to acknowledge that there are still areas where human intervention remains indispensable such as complex problem solving, and adaptability to unpredictable situations that necessitate human assistance.
In addition, the transition towards less human involvement in agriculture, influenced by global demographic shifts, will likely increase the world's population residing in urban areas. This trend could potentially reduce the agricultural workforce by 2025; however, the impact of this shift may vary significantly across countries, depending on their level of development. Less developed countries, where the adoption of AI and robotics in agriculture may still be nascent, could experience a less pronounced impact from this transition.
I'm an economist specializing in agricultural economics, agribusiness planning, bioeconomy, and renewable energy. My passion for writing fuels my goal to contribute to a more resilient and efficient agricultural system that balances economic profitability with environmental stewardship. Through my research, I aim to advocate for sustainable practices that benefit both farmers and the environment.
