Open Access
| Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
|---|---|---|
| Article Number | 00013 | |
| Number of page(s) | 16 | |
| DOI | https://doi.org/10.1051/bioconf/20249700013 | |
| Published online | 05 April 2024 | |
- K. G. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 8, p. 2674, 2018. [CrossRef] [Google Scholar]
- H. Storm, K. Baylis, and T. Heckelei, “Machine learning in agricultural and applied economics,” European Review of Agricultural Economics, vol. 47, no. 3, pp. 849–892, 2020. [CrossRef] [Google Scholar]
- P. Surya and I. L. Aroquiaraj, “Crop yield prediction in agriculture using data mining predictive analytic techniques,” International Journal of Research and Analytical Reviews, vol. 5, no. 4, pp. 783–787, 2018. [Google Scholar]
- O. Mutanga, T. Dube, and O. Galal, “Remote sensing of crop health for food security in Africa: Potentials and constraints,” Remote Sensing Applications: Society and Environment, vol. 8, pp. 231–239, 2017. [CrossRef] [Google Scholar]
- D. Elavarasan and P. D. Vincent, “Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications,” IEEE access, vol. 8, pp. 86886–86901, 2020. [CrossRef] [Google Scholar]
- U. Shafi et al., “A multi-modal approach for crop health mapping using low altitude remote sensing, internet of things (IoT) and machine learning,” IEEE Access, vol. 8, pp. 112708–112724, 2020. [CrossRef] [Google Scholar]
- D. Mishra and D. Deepa, “Automation and integration of growth monitoring in plants (with disease prediction) and crop prediction,” Materials Today: Proceedings, vol. 43, pp. 3922–3927, 2021. [CrossRef] [Google Scholar]
- S. Shidnal, M. V. Latte, and A. Kapoor, “Crop yield prediction: two-tiered machine learning model approach,” International Journal of Information Technology, vol. 13, pp. 1983–1991, 2021. [CrossRef] [Google Scholar]
- R. Shukla et al., “Detecting crop health using machine learning techniques in smart agriculture system,” Journal of Scientific & Industrial Research, vol. 80, no. 08, pp. 699–706, 2021. [Google Scholar]
- A. Tripathi, R. K. Tiwari, and S. P. Tiwari, “A deep learning multi-layer perceptron and remote sensing approach for soil health based crop yield estimation,” International Journal of Applied Earth Observation and Geoinformation, vol. 113, p. 102959, 2022. [CrossRef] [Google Scholar]
- P. Muruganantham, S. Wibowo, S. Grandhi, N. H. Samrat, and N. Islam, “A systematic literature review on crop yield prediction with deep learning and remote sensing,” Remote Sensing, vol. 14, no. 9, p. 1990, 2022. [CrossRef] [Google Scholar]
- K. Malhotra and M. Firdaus, “Application of Artificial Intelligence in IoT Security for Crop Yield Prediction,” ResearchBerg Review of Science and Technology, vol. 2, no. 1, pp. 136–157, 2022. [Google Scholar]
- S. Gupta et al., “Machine learning-and feature selection-enabled framework for accurate crop yield prediction,” Journal of Food Quality, vol. 2022, pp. 1–7, 2022. [CrossRef] [Google Scholar]
- E. Elbasi et al., “Crop Prediction Model Using Machine Learning Algorithms,” Applied Sciences, vol. 13, no. 16, p. 9288, 2023. [CrossRef] [Google Scholar]
- M. M. Islam et al., “DeepCrop: Deep learning-based crop disease prediction with web application,” Journal of Agriculture and Food Research, vol. 14, p. 100764, 2023. [CrossRef] [Google Scholar]
- W. Young, G. Weckman, and W. Holland, “A survey of methodologies for the treatment of missing values within datasets: Limitations and benefits,” Theoretical Issues in Ergonomics Science, vol. 12, no. 1, pp. 15–43, 2011. [CrossRef] [Google Scholar]
- E. Bisong and E. Bisong, “Introduction to Scikit-learn,” Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, pp. 215–229, 2019. [CrossRef] [Google Scholar]
- S. Sharma and R. Jain, “Outlier detection in agriculture domain: application and techniques,” in Big Data Analytics: Proceedings of CSI 2015, 2018: Springer, pp. 283–296. [CrossRef] [Google Scholar]
- E. J. Jamshidi, Y. Yusup, J. S. Kayode, and M. A. Kamaruddin, “Detecting outliers in a univariate time series dataset using unsupervised combined statistical methods: A case study on surface water temperature,” Ecological Informatics, vol. 69, p. 101672, 2022. [CrossRef] [Google Scholar]
- Q. H. Nguyen et al., “Influence of data splitting on performance of machine learning models in prediction of shear strength of soil,” Mathematical Problems in Engineering, vol. 2021, pp. 1–15, 2021. [Google Scholar]
- S. Khan and L. AlSuwaidan, “Agricultural monitoring system in video surveillance object detection using feature extraction and classification by deep learning techniques,” Computers and Electrical Engineering, vol. 102, p. 108201, 2022. [CrossRef] [Google Scholar]
- A. Bhatia, A. Chug, A. Prakash Singh, and D. Singh, “Investigate the Impact of Resampling Techniques on Imbalanced Datasets: A Case Study in Plant Disease Prediction,” in 2021 Thirteenth International Conference on Contemporary Computing (IC3-2021), 2021, pp. 278–285. [CrossRef] [Google Scholar]
- R. Mohammed, J. Rawashdeh, and M. Abdullah, “Machine learning with oversampling and undersampling techniques: overview study and experimental results,” in 2020 11th international conference on information and communication systems (ICICS), 2020: IEEE, pp. 243–248. [CrossRef] [Google Scholar]
- A. D. Amirruddin, F. M. Muharam, M. H. Ismail, N. P. Tan, and M. F. Ismail, “Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles,” Computers and Electronics in Agriculture, vol. 193, p. 106646, 2022. [CrossRef] [Google Scholar]
- L.-B. Sweet, C. Müller, M. Anand, and J. Zscheischler, “Cross-validation strategy impacts the performance and interpretation of machine learning models,” Artificial Intelligence for the Earth Systems, vol. 2, no. 4, p. e230026, 2023. [Google Scholar]
- J.-L. Peng et al., “Accuracy evaluation of the crop-weather yield predictive models of Italian ryegrass and forage rye using cross-validation,” Journal of crop science and biotechnology, vol. 20, pp. 327–334, 2017. [CrossRef] [Google Scholar]
- A. Crane-Droesch, “Machine learning methods for crop yield prediction and climate change impact assessment in agriculture,” Environmental Research Letters, vol. 13, no. 11, p. 114003, 2018. [CrossRef] [Google Scholar]
- F. Osisanwo, J. Akinsola, O. Awodele, J. Hinmikaiye, O. Olakanmi, and J. Akinjobi, “Supervised machine learning algorithms: classification and comparison,” International Journal of Computer Trends and Technology (IJCTT), vol. 48, no. 3, pp. 128–138, 2017. [CrossRef] [Google Scholar]
- L. Benos, A. C. Tagarakis, G. Dolias, R. Berruto, D. Kateris, and D. Bochtis, “Machine learning in agriculture: A comprehensive updated review,” Sensors, vol. 21, no. 11, p. 3758, 2021. [CrossRef] [PubMed] [Google Scholar]
- L. Q. Thao et al., “PesViT: a deep learning approach for detecting misuse of pesticides on farm,” The Journal of Supercomputing, pp. 1–24, 2023. [Google Scholar]
- M. G. Ps, “Performance evaluation of best feature subsets for crop yield prediction using machine learning algorithms,” Applied Artificial Intelligence, vol. 33, no. 7, pp. 621–642, 2019. [CrossRef] [Google Scholar]
- S. Raja, B. Sawicka, Z. Stamenkovic, and G. Mariammal, “Crop prediction based on characteristics of the agricultural environment using various feature selection techniques and classifiers,” IEEE Access, vol. 10, pp. 23625–23641, 2022. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.