LEB5052 - Artificial Intelligence in Biosystems: Principles and Applications

Credit hours

Instructor
Roberto Fray da Silva

Objective
Enable the student to: (i) understand, analyze, and implement the main stages of the data life cycle,
from generating data to providing results to aid decision making; (ii) understand essential concepts of
artificial intelligence, such as data types, workflow involving the entire data cycle, types of learning,
types of models and their uses; (iii) understand the characteristics of the main artificial intelligence
models used in supervised learning and unsupervised learning; and (iv) analyze spatial and temporal
problems in biosystems using artificial intelligence models.

Content
1. Basic programming concepts in the Python language: workflow, variables, loops, data types,
algorithms, debugging; 2. Data life cycle: definition, components and their importance, data collection,
tabular data fusion, main forms of processing, exploratory data analysis, artificial intelligence and
knowledge extraction, analysis and interpretation of model results; 3. Artificial intelligence: definition,
data types, workflow, learning types, models types, main uses, examples of application in agricultural
and forestry systems, topics of great relevance in research in artificial intelligence; 4. Essential aspects
for choosing artificial intelligence models: model limitations, available data, desired results, problem
type, role of domain experts and decision makers; 5. Problems and difficulties in using artificial
intelligence models: curse of dimensionality, main implementation errors, main conceptual errors,
impact of lack of data in different approaches, impact of different processing techniques, impact of
standardization, detection and approaches to address data sets with outliers, the need to use baselines
to compare results; 6. Spatial, temporal, and spatio-temporal data: definition, difficulties, main
supervised and unsupervised models used, examples of application in biosystems; 7. Case studies in
biosystems, considering temporal, spatial, and spatio-temporal problems, such as forecasting commodity
prices using different supervised models, exploratory data analysis, forecasting potential zones impacted
by drought using unsupervised learning, among others.

Bibliography
1. Langtangen, H.P., 2016. A primer on scientific programming with Python. Springer-Verlag Berlin
Heidelberg.
2. Hao, J. and Ho, T.K., 2019. Machine learning made easy: a review of scikit-learn package in python
programming language. Journal of Educational and Behavioral Statistics, 44(3), pp.348-361.
3. De Silva, D. and Alahakoon, D., 2022. An artificial intelligence life cycle: From conception to
production. Patterns, 3(6).
4. Polyzotis, N., Roy, S., Whang, S.E. and Zinkevich, M., 2018. Data lifecycle challenges in production
machine learning: a survey. ACM SIGMOD Record, 47(2), pp.17-28.
5. Himeur, Y., Rimal, B., Tiwary, A. and Amira, A., 2022. Using artificial intelligence and data fusion for
environmental monitoring: A review and future perspectives. Information Fusion, 86, pp.44-75.
6. Ghosh, A., Nashaat, M., Miller, J., Quader, S. and Marston, C., 2018. A comprehensive review of tools
for exploratory analysis of tabular industrial datasets. Visual Informatics, 2(4), pp.235-253.
7. James, G., Witten, D., Hastie, T., Tibshirani, R. and Taylor, J., 2023. An introduction to statistical
learning: With applications in python. Springer Nature.
8. Russell, S.J. and Norvig, P., 2009. Artificial intelligence a modern approach. London. Pearson. 1152 p.
9. Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J. Big data in smart farming–a review. Agricultural
Systems, 153, 2017, pp.69-80.
10. Saleem, M.H., Potgieter, J. And Arif, K.M., 2021. Automation in agriculture by machine and deep
learning techniques: A review of recent developments. Precision Agriculture, 22, pp.2053-2091.
11. Lecun, Y., Bengio, Y., Hinton, G. Deep learning. Nature, 521(7553), 2015, pp.436-444.
12. Jordan, M.I. and Mitchell, T.M., 2015. Machine learning: Trends, perspectives, and prospects.
Science, 349(6245), pp.255-260.
13. Tian, H., Wang, T., Liu, Y., Qiao, X. and Li, Y., 2020. Computer vision technology in agricultural
automation—A review. Information Processing in Agriculture, 7(1), pp.1-19.
14. Klompenburg, T. V.; Kassahun, A.; Catal, C. (2020). Crop yield prediction using machine learning: a
systematic literature review. Computers and Electronics in Agriculture, 177.
15. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., Bochtis, D. (2018). Machine learning in
agriculture: A review. Sensors, 18(8), 1–29.
16. Patrício, D.I. and Rieder, R., 2018. Computer vision and artificial intelligence in precision agriculture
for grain crops: A systematic review. Computers and electronics in agriculture, 153, pp.69-81.
17. Pathan, M., Patel, N., Yagnik, H. and Shah, M., 2020. Artificial cognition for applications in smart
agriculture: A comprehensive review. Artificial Intelligence in Agriculture, 4, pp.81-95.
18. Kamilaris, A. and Prenafeta-Boldú, F.X., 2018. Deep learning in agriculture: A survey. Computers and
electronics in agriculture, 147, pp.70-90.
19. Wang, Y., Zhang, W., Gao, R., Jin, Z. and Wang, X., 2021. Recent advances in the application of deep
learning methods to forestry. Wood Science and Technology, 55(5), pp.1171-1202.
20. Diez, Y., Kentsch, S., Fukuda, M., Caceres, M.L.L., Moritake, K. and Cabezas, M., 2021. Deep learning
in forestry using uav-acquired rgb data: A practical review. Remote Sensing, 13(14), p.2837.
21. Mahmud, M.S., Zahid, A., Das, A.K., Muzammil, M. and Khan, M.U., 2021. A systematic literature
review on deep learning applications for precision cattle farming. Computers and Electronics in
Agriculture, 187, p.106313.
22. Atluri, G., Karpatne, A. and Kumar, V., 2018. Spatio-temporal data mining: A survey of problems and
methods. ACM Computing Surveys (CSUR), 51(4), pp.1-41.
23. Kisilevich, S., Mansmann, F., Nanni, M. and Rinzivillo, S., 2010. Spatio-temporal clustering (pp.
855-874). Springer US.
24. Casali, Y., Aydin, N.Y. and Comes, T., 2022. Machine learning for spatial analyses in urban areas: a
scoping review. Sustainable Cities and Society, 85, p.104050.
25. Rolnick, D., Donti, P.L., Kaack, L.H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A.S.,
Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A. and Luccioni, A.S., 2022. Tackling climate change
with machine learning. ACM Computing Surveys (CSUR), 55(2), pp.1-96.
26. Kaack, L.H., Donti, P.L., Strubell, E., Kamiya, G., Creutzig, F. and Rolnick, D., 2022. Aligning artificial
intelligence with climate change mitigation. Nature Climate Change, 12(6), pp.518-527.
27. Kikon, A. and Deka, P.C., 2022. Artificial intelligence application in drought assessment, monitoring
and forecasting: a review. Stochastic Environmental Research and Risk Assessment, 36(5),
pp.1197-1214.
28. Dikshit, A., Pradhan, B. and Santosh, M., 2022. Artificial neural networks in drought prediction in the
21st century–A scientometric analysis. Applied Soft Computing, 114, p.108080.