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AgroDSS: A decision support system for agriculture and farming
Abstract
Abstract Decision support systems, data analysis and data mining have become significant tools for improving business in professional world. The emerging technologies are making the precision agriculture omnipresent and allow potential for enriching it with computer-assisted decision support systems for farm management. In this paper we describe a novel system AgroDSS that bridges the gap between agricultural systems and state-of-the-art decision support methodology. The described system is intended for integration into the existing farm management information systems and provides a cloud-based decision support toolbox, allowing farmers to upload their own data, utilize several data analysis methods and retrieve their outputs. The implemented tools include predictive modeling with explanation, accuracy evaluation, time series clustering and decomposition, and structural change detection. They can help users make predictions for simulated scenarios and better understand the dependencies (interactions) within their domain. We apply the AgroDSS system on a case study of pest population dynamics, illustrating the potential for its use.
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Cites
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Cited by
Yadav, V. S., Singh, A. R., Raut, R. D., & Govindarajan, U. H. (2020) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2020.104877 · Cited by 384
Forecasting yield by integrating agrarian factors and machine learning models: A survey
Elavarasan, D., Vincent, D. R., Sharma, V., Zomaya, A. Y., & Srinivasan, K. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.10.024 · Cited by 346
The Digitisation of Agriculture: a Survey of Research Activities on Smart Farming
Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019) · Array · doi:10.1016/j.array.2019.100009 · Cited by 346
Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review
Elbasi, E., Mostafa, N., AlArnaout, Z., Zreikat, A. I., Cina, E., Varghese, G., Shdefat, A., Topcu, A. E., Abdelbaki, W., Mathew, S., et al. (2023) · IEEE Access · doi:10.1109/access.2022.3232485 · Cited by 282
Metadata
- Authors
- Rupnik, R., Kukar, M., Vračar, P., Košir, D., Pevec, D., & Bosnić, Z.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 161
- Pages
- 260-271
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2797368616
- Cited by
- 251
- References
- 1
- FWCI
- 18.21
- Type
- journal article
- Language
- en
- Open access
- closed
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- Rupnik, R., Kukar, M., Vračar, P., Košir, D., Pevec, D., & Bosnić, Z. (2019). AgroDSS: A decision support system for agriculture and farming. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.04.001
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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