Farmer-centric design thinking principles for smart farming technologies

McCaig, Melanie; Dara, Rozita; Rezania, Davar

doi:10.1016/j.iot.2023.100898

Cited by 5 publications

(1 citation statement)

References 84 publications

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“…On the other hand, the outcome of the robotic operation (e.g., the performance or any collected data) cannot directly interact with other sources of information, providing comprehensive feedback to be used for further decision making. Such a potential connection can create a feedback loop, enabling farmers to adjust their strategies in real-time based on changing conditions and new information [37,38]. To that effect, there is a need to build and evaluate robotic systems that are part of an overall integrated system under a continuous two-way connection and interaction.…”

Section: Introductionmentioning

confidence: 99%

Integrated Route-Planning System for Agricultural Robots

Asiminari,

Moysiadis,

Kateris

et al. 2024

AgriEngineering

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Within the transition from precision agriculture (task-specific approach) to smart farming (system-specific approach) there is a need to build and evaluate robotic systems that are part of an overall integrated system under a continuous two-way connection and interaction. This paper presented an initial step in creating an integrated system for agri-robotics, enabling two-way communication between an unmanned ground vehicle (UGV) and a farm management information system (FMIS) under the general scope of smart farming implementation. In this initial step, the primary task of route-planning for the agricultural vehicles, as a prerequisite for the execution of any field operation, was selected as a use-case for building and evaluating this integration. The system that was developed involves advanced route-planning algorithms within the cloud-based FMIS, a comprehensive algorithmic package compatible with agricultural vehicles utilizing the Robot Operating System (ROS), and a communicational and computational unit (CCU) interconnecting the FMIS algorithms, the corresponding user interface, and the vehicles. Its analytical module provides valuable information about UGVs’ performance metrics, specifically performance indicators of working distance, non-working distance, overlapped area, and field-traversing efficiency. The system was demonstrated via the implementation of two robotic vehicles in route-execution tasks in various operational configurations, field features, and cropping systems (open field, row crops, orchards). The case studies showed variability in the operational performance of the field traversal efficiency to be between 79.2% and 93%, while, when implementing the optimal route-planning functionality of the system, there was an improvement of up to 9.5% in the field efficiency. The demonstrated results indicate that the user can obtain better control over field operations by making alterations to ensure optimum field performance, and the user can have complete supervision of the operation.

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Section: Introductionmentioning

confidence: 99%

Integrated Route-Planning System for Agricultural Robots

Asiminari,

Moysiadis,

Kateris

et al. 2024

AgriEngineering

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Navigating psychological barriers in agricultural innovation adoption: A multi-stakeholder perspective

Chindasombatcharoen,

Tsolakis,

Kumar

et al. 2024

Journal of Cleaner Production

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Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Mowla,

Shah

et al. 2023

IEEE Access

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The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing food production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization and control, early-stage pest and crop disease management, and energy conservation. Wireless application protocols such as ZigBee, WiFi, SigFox, and LoRaWAN are commonly employed to collect real-time data for monitoring purposes. Embracing advanced technology is imperative to ensure efficient annual production. Therefore, this study emphasizes a comprehensive, futureoriented approach, delving into IoT-WSNs, wireless network protocols, and their applications in agriculture since 2019. It thoroughly discusses the overview of IoT and WSNs, encompassing their architectures and summarization of network protocols. Furthermore, the study addresses recent issues and challenges related to IoT-WSNs and proposes mitigation strategies. It provides clear recommendations for the future, emphasizing the integration of advanced technology aiming to contribute to the future development of smart agriculture systems. INDEX TERMSInternet of things, wireless sensor networks, wireless network protocols, smart agriculture applications.

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Farmer-centric design thinking principles for smart farming technologies

Cited by 5 publications

References 84 publications

Integrated Route-Planning System for Agricultural Robots

Integrated Route-Planning System for Agricultural Robots

Navigating psychological barriers in agricultural innovation adoption: A multi-stakeholder perspective

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

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