Cultivating Agricultural Evolution: Revolutionizing Farming Through The Power of AI And Technology

Abstract

Objective: The objective of this study is to explore the current and potential role of Artificial Intelligence (AI) in the agricultural sector. We aim to analyze the adoption and impact of AI solutions in farming, identify challenges, and discuss the prospects for its future integration.
 Method: We conducted a comprehensive review of existing literature and ongoing research projects related to AI applications in agriculture. We also examined case studies, technological developments, and AI pioneers in the field.
 Results: Our analysis reveals that while AI solutions are being researched and applied in agriculture, there is a gap in widespread industry adoption. Large-scale research projects are underway, and some AI applications are available in the market. However, the development of predictive solutions to address real farming challenges is in the early stages. AI's influence extends across various sectors, contributing to the advancement of technologies such as big data, robotics, and the Internet of Things.
 An illustrative example is the styrofoam container device, which utilizes machine learning and computer vision to detect and categorize "safety occurrences." Although not all-encompassing, this technology gathers significant data, such as driver behavior, speed, and surroundings. IFM's system promptly alerts supervisors to safety breaches, enhancing both safety and productivity.
 Conclusion: The future of AI in agriculture hinges on the widespread adoption of AI solutions. The agricultural industry remains underserved in terms of AI integration, and the development of predictive solutions is in its early stages. However, AI's impact across sectors underscores its importance. Pioneers like IFM and IBM's patent statistics demonstrate the expanding scope of AI innovation.

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

“…The future trends in this domain suggest a trajectory towards more advanced, precise, and sustainable agricultural practices. Rattan (2023) explores the current and potential role of Artificial Intelligence (AI) in the agricultural sector, analyzing the adoption and impact of AI solutions in farming. The study reveals that while AI solutions are increasingly researched and applied in agriculture, there is a notable gap in widespread industry adoption.…”

“…These prospective developments in AI for agriculture are driven by the need to address pressing challenges in the sector while unlocking new efficiencies and capabilities. Rattan (2023) explores the transformative potential of AI in agriculture, highlighting that while AI applications are growing, there is a significant gap in widespread industry adoption. The development of predictive solutions to real farming challenges is still in its infancy.…”