Data Driven Farm Mechanization using Artificial Intelligence: A Review

Author: Kailashkumar B. and G. Vijayakumar

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Abstract

The farm power availability at present scenario is 2.5 kW ha-1 and it is predicted to be higher in upcoming years. Several attempts were made to bridge the gap between farmers and machinery utilization, to increasing efficiency and reducing drudgery to the farmers, but it cannot be achieved completely and still the gap is more and widening. The lack of skilled labors and shortage of agricultural workers are the serious problem faced by the farmers in this era of agriculture. The various inventions of farm machineries and processing industries led the way of food production for our population. But in many cases the farmers are facing a lot of unpredictable factors and losing their interest in farming. In this scenario, the idea of artificial intelligence is that, machines can readily imitate human intellect and carry out tasks ranging from the most basic to the most complex, will able to sort out all those gaps. The major aims of AI are learning, predicting, and decision making. In agriculture sector the use of artificial intelligence (AI) is to enhance a diverse range of agricultural operations to make sustainable food supply chain. These responsibilities consist of crop production, pest management, monitoring of soil and growth conditions, data organization for farmers, and workload assistance. With the help of systematic data collection and understanding the functionality of farming operations, the AI helps to increase the adoption rate of mechanization. Despite the numerous benefits, the adoption of AI in farm mechanization is not without challenges. Issues such as high initial investment costs, technical complexity, and the need for robust data infrastructure pose significant barriers to widespread implementation. Additionally, there are concerns regarding data privacy, cybersecurity, and the displacement of the agricultural workforce. To overcome these challenges, it is essential to foster collaboration between stakeholders, including farmers, researchers, technology developers, and policymakers. Investing in training and capacity-building programs can equip farmers with the necessary skills to leverage AI technologies effectively. Furthermore, developing affordable and scalable AI solutions tailored to the specific needs of smallholder farmers can accelerate the adoption of AI-driven mechanization

Keywords

Artificial Intelligence, Machine Learning, Data Science, Agriculture, Farm Machinery

Conclusion

The integration of Artificial Intelligence (AI) in farm mechanization represents a transformative shift in modern agriculture, offering unprecedented opportunities for enhancing productivity, efficiency, and sustainability. This review has comprehensively examined the various AI-driven technologies and methodologies being employed in farm mechanization, highlighting their impact on different agricultural operations. AI technologies, such as machine learning, computer vision, and robotics, are revolutionizing traditional farming practices by enabling precise and efficient field operations. The application of AI in machinery, such as autonomous tractors, drones, and harvesters, has demonstrated significant improvements in field productivity and resource management. These smart machines are capable of performing tasks with high precision, reducing human labor and minimizing input wastage, thus contributing to cost savings and environmental conservation. The use of AI in predictive analytics and decision support systems has shown great promise in enhancing farm management strategies. By leveraging large datasets and sophisticated algorithms, farmers can make informed decisions regarding crop selection, planting schedules, irrigation, and pest control. This data-driven approach not only optimizes resource utilization but also mitigates risks associated with climate variability and market fluctuations. Moreover, AI-driven mechanization facilitates real-time monitoring and management of farm operations. Through the deployment of IoT devices and sensors, farmers can continuously monitor soil health, crop growth, and machinery performance. This real-time data collection and analysis enable timely interventions, ensuring optimal growing conditions and reducing the likelihood of crop failure. In conclusion, the integration of AI in farm mechanization holds immense potential to revolutionize agriculture, making it more efficient, sustainable, and resilient. While challenges remain, continued advancements in AI technologies, combined with supportive policies and collaborative efforts, can pave the way for a data-driven agricultural future. By embracing AI, the farming community can achieve higher productivity, better resource management, and improved economic outcomes, ultimately contributing to global food security and sustainable development

References

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How to cite this article

Kailashkumar B. and G. Vijayakumar (2024). Data Driven Farm Mechanization using Artificial Intelligence: A Review. Biological Forum – An International Journal, 16(5): 92-97.