Leveraging Automation and Data-driven Logistics for Sustainable Farming of High-value Crops in Emerging Economies

Technology innovations present an opportunity for the agricultural sector to leverage in-field data and inform resource-demanding operations to ultimately promote Sustainable Development Goals (SDGs). The need for data-driven innovations in farming is particularly pertinent to resource-scarce regions, such as the Indian Punjab, where an amalgam of obscure policies and lack of real-time visibility of crops typically leads to the excessive use of farming inputs like freshwater. To this end, this research investigates the use of Internet of Things (IoT) implementations to cultivate Kinnow (a high-value citrus fruit) for assessing the impact of data-informed irrigation practices on the appropriation of natural sources, farming operations efficiency, and the well-being of smallholder farmers. First, a literature taxonomy demonstrates that studies on agri-field logistics often do not consider operations’ environmental and energy impact. In addition, the application of IoT and automated guided vehicles (AGVs) for informing farmers about precision irrigation planning has not been sufficiently explored. Second, an empirical-driven numerical investigation explores four alternative irrigation scenarios for cultivating Kinnow, namely: (i) flood irrigation; (ii) manual irrigation; (iii) AGV-informed manual irrigation; and (iv) AGV-assisted irrigation, which was cast as a Capacitated Vehicle Routing Problem. The analysis results compare the overall sustainability impact of the investigated practices on the water-energy nexus. This research is innovative as it focuses on data-driven logistics operations on the environmental, energy and farmers’ well-being impact associated with irrigation practices in agronomy. This study further supports the role of data-driven technology innovations towards the transition to SDG-centric food supply chains by providing guiding principles for community-led in-field logistics planning.