Farm Robotics and Autonomous Systems from Farm to Fork

Prakash G. Athare (), Praveen Kumar and Aishwarya Patil
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Prakash G. Athare: Institute of Economic Growth
Praveen Kumar: ICAR-Indian Agricultural Research Institute
Aishwarya Patil: Panjabrao Deshmukh Krishi Vidyapeeth

Chapter Chapter 9 in Transforming Agriculture through Artificial Intelligence for Sustainable Food Systems, 2025, pp 137-154 from Springer

Abstract: Abstract Currently, the global agri-food system faces challenges of population growth, climate change, urbanization, environmental degradation, and competitiveness for quality and safe food. To address these challenges, advanced agricultural technologies including Robotics and Autonomous Systems (RAS) integrated with Artificial Intelligence (AI) and Machine Learning (ML), have emerged as key enablers for transforming the food chain. In agriculture, the automated seed-sowing system improves crop planting precision, while the automated irrigation system enhances water use efficiency using real-time data. Additionally, an autonomous harvesting system employs advanced vision technology like sensors and cameras to harvest the crops by picking them efficiently without causing damage. Moreover, post-harvest management practices minimize the losses during handling and transportation, thereby enhancing the efficiency of the food supply chain. Thus, RAS improves agricultural productivity by taking over tasks that are both labour-intensive and repetitive. In dairy farming, robotics, and autonomous systems have revolutionized operations such as milking, feeding systems, health and reproduction monitoring, and inventory management, ultimately improving animal productivity and welfare. Alongside food production, food safety has become a paramount area of concern in agriculture due to increased consumer awareness and regulatory aspects related to the quality and hygiene of food products. Therefore, AI-related technologies can use their potential to automate quality control, predict safety incidents, enhance traceability through the identification and removal of damaged products, real-time monitoring, anticipate risks, and automate compliance systems for food quality control and inspection. Looking ahead, the future of the resilient and sustainable food system is expected to be shaped by the symbiotic interaction between components of the RAS along with food safety management. However, like any other technological advancement, RAS faces challenges such as technical hurdles for stakeholders, high initial costs, ethical considerations, and demographic shifts. Nevertheless, these technologies are crucial for achieving a sustainable and prosperous future for the agri-food system.

Keywords: Artificial Intelligence; Machine Learning; Robotics; Food safety; Food traceability (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-981-96-4795-8_9

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