Skip to main navigation menu Skip to main content Skip to site footer


Vol. 8 (2021)

Automated Agricultural Robot and Sensor Data Collection and Analysis through a Biomass Feedstock Production Information System

November 24, 2021


The increasing environmental pollution resulting from the use of non-renewable fossil fuels as well as the development of economic dependencies among countries because of the lack of such types of fuels underline the intense need for the use of sustainable forms of energy. Biomass derived biofuels provide such an alternative. The main tasks of biomass feedstock production are planting and cultivation, harvest, storage, and transportation. A number of complex decisions characterize each of these tasks. These decisions are related to the monitoring of crop health, the improvement of crop productivity using innovative technologies, and the examination of limitations in existing processes and technologies associated with biomass feedstock production. Other critical issues are the development of sustainable methods for the delivery of the biomass while maintaining product quality. There is the need for the development of an automated integrated research tool based on resilience and sustainability which will allow the coordination of different research fields but also perform research on its own. The specific tool should aim in the optimization of different parameters which specify the research done and in the case of biomass feedstock production; such parameters are the transportation of biomass from the field to the biorefinery, the equipment used, and the biomass storage conditions. This optimization would enhance decision making in the field of bioenergy production. Based on the need for such an automated integrated research tool, this paper presents an information system that provides automated functionalities for better decision making in the bioenergy production field based on the collection and analysis of agricultural robot and sensor data.


  1. The National Academies of Sciences, Engineering, Medicine (2021) Our Energy Sources - Fossil Fuels. Available at: (Accessed: 23 August 2021)
  2. U.S. Energy Information Administration (2021) How much petroleum does the United States import and export? Available at: (Accessed: 23 August 2021)
  3. U.S. Energy Information Administration (2021) Use of energy explained - Energy use for transportation. Available at: (Accessed: 23 August 2021)
  4. Argus (2021) World biofuels output up to 3.3mn b/d by 2026: IEA. Available at: (Accessed 22 August 2021)
  5. International Energy Agency (IEA) (2021) Transport Biofuels. Available at: (Accessed: 22 August 2021)
  6. RA. Lee and JM. Lavoie. 'From first- to third-generation biofuels: Challenges of producing a commodity from a biomass of increasing complexity', Animal Frontiers, 2013; 3(2): pp. 6-11.
  7. AEM. Hussian (2018) 'The Role of Microalgae in Renewable Energy Production: Challenges and Opportunities', in Turkoglu M. (ed.) Marine Ecology - Biotic and Abiotic Interactions. Intech Open.
  8. Dragone, G., Fernandes, B., Vicente, A.A. and Teixeira, J.A. (2010) 'Third generation biofuels from microalgae', Current Research, Technology and Education Topics in Applied Microbiology and Microbial Biotechnology, pp. 1355-1366
  9. Department of Plant and Soil Sciences, Oklahoma State University (2021) Environmental Impacts. Available at: (Accessed: 22 August 2021)
  10. Kinhal V. (2021) Safe and sound: Decentralisation with Miscanthus giganteus. AgriKinetics. Available at: (Accessed: 22 August 2021)
  11. IGIGlobal (2021) 'What is Social Informatics'. Available at: (Accessed: 05 July 2021)
  12. Meyer ET, Shankar K, Willis M, Sharma S, Sawyer S. (2019) 'The social informatics of knowledge', J Assoc Inf Sci Technol. 2019 Apr; 70(4): 307-312.
  13. Kozai T, Fujiwara K, Runkle ES. (Eds) LED Lighting for Urban Agriculture. Springer Singapore, Singapore 2016.
  14. Tropos (2021) The Tropos Methodology. Available at: (Accessed: 10 May 2021)
  15. De Clercq M, Vats A, Biel A. (2018) 'AGRICULTURE 4.0: THE FUTURE OF FARMING TECHNOLOGY'. World Government Summit. Available at: (Accessed: 12 May 2021)
  16. Ruiz-Ortiz V, García-López S, Solera A, Paredes J. 'Contribution of decision support systems to water management improvement in basins with high evaporation in Mediterranean climates'. Hydrology Research 2019; 1, 50(4): 1020-1036.
  17. Barksdale, J. and D. Scott McCrickard (2010) 'Concept Mapping in Agile Usability: A Case Study', CHI 2010: Usability Methods and New Domains, April 10-15, 2010, Atlanta, GA, United States, pp. 4691-4694.
  18. Gray SA, Zanre E and SRJ Gray. (2014) 'Fuzzy cognitive maps as representations of mental models and group beliefs: theoretical and technical issues'. pp. 29-48 in E. I. Papageorgiou (Ed.). Fuzzy cognitive maps for applied sciences and engineering -from fundamentals to extensions and learning algorithms. Springer, Heidelberg, Germany.
  19. Sharma, P. (2019) 'A Beginner's Guide to Hierarchical Clustering and how to Perform it in Python', AnalyticsVidhya. Available at: (Accessed: 15 July 2021)