Corn and Wheat Plant Identification on Radar and Optical Image Data

Abstract

In recent years, prediction, detection, and classification applications have been made in many fields such as agriculture, health, stock market, economy, cybersecurity, etc., in Machine Learning and Artificial Intelligence. These applications are user-friendly and provide fast, high-quality, and accurate results. The advancements in these fields have shown that machine learning and deep learning methods are very useful in classifying large and complex data, especially when human brain and physical power are insufficient. Today's findings suggest there have been promising studies using these models, focused on time- and cost-effective and high-quality products. These studies provide efficiency in agricultural areas, thereby guiding both farmers and policymakers. In addition, the development and widespread implementation of unmanned aerial vehicles (UAVs) accelerated the process of obtaining multispectral aerial images. With the combined use of these technologies and high-speed computer software and hardware for precise and high-quality production in agriculture, it was possible to determine plant species and increase product quality. In this study, a dataset consisting of radar and optical image data was used to classify corn and wheat crops cultivated in agricultural areas. Four different machine learning models, namely Decision Tree (DT), K-Nearest Neighbors (K-NN), Naive Bayes (NB), and Support Vector Machines (SVM), were trained and compared on the dataset consisting of 174 features from Winnipeg, Canada. The dataset has been divided into 80% for training and 20% for testing. According to the results, the SVM model performed the best with the highest accuracy (0.9998) and F1-Score (0.9996), while the NB model performed the worst accuracy (0.9895) and F1-Score (0.9835). The detection of wheat and corn crop types by processing radar and optical image data with machine learning models has shown that other crops in cultivated lands in the Southeastern Anatolia Project (GAP) region can be classified using the same method, which shows the importance of this study.

Keywords

• machine learning
• unmanned aerial vehicle
• multispectral image
• agriculture
• plant classification

References

1. [1] A. B. Altinel, “Comparison of the performance of machine learning algorithms on sentiment analysis problem in Turkish texts,” 1st International Conference on Applied Engineering and Natural Sciences (ICAENS), 2021. [Online]. Available: https://doi.org/10.31590/ejosat.1011864
2. [2] K. B. Demircioglu, “Deep learning based dynamic Turkish sign language recognition with leap motion,” M.S. thesis, Dept. of Computer Engineering, Istanbul Technical University, Istanbul, Turkey, 2020. [Online]. Available: https://tez.yok.gov.tr/UlusalTezMerkezi
3. [3] A. Mucherino, P. Papajorgji, and P. Pardalos, “A survey of data mining techniques applied to agriculture,” Operational Research, vol. 9, no. 2, pp. 121–140, 2009. [Online]. Available: https://doi.org/10.1007/s12351-009-0054-6
4. [4] A. Tabanlioglu, A. C. Yucedag, M. F. Tuysuz, and M. E. Tenekeci, “Multicopter usage for analysis productivity in agriculture on GAP region,” in Proc. 23rd Signal Processing and Communications Applications Conf. (SIU), Malatya, Turkey, 2015, pp. 1102–1105. [Online]. Available: https://doi.org/10.1109/SIU.2015.7130027
5. [5] T. Rumpf, A. K. Mahlein, U. Steiner, E. C. Oerke, H. W. Dehne, and L. Plümer, “Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance,” Computers and Electronics in Agriculture, vol. 74, no. 1, pp. 91–99, 2010. [Online]. Available: https://doi.org/10.1016/j.compag.2010.06.009
6. [6] A. Gumuscu, M. E. Tenekeci, and A. V. Bilgili, “Estimation of wheat planting date using machine learning algorithms based on available climate data,” Sustainable Computing: Informatics and Systems, vol. 28, 2020. [Online]. Available: https://doi.org/10.1016/j.suscom.2019.01.010
7. [7] K. Karadag, R. Tasaltin, M. E. Tenekeci, and A. Gumuscu, “Determination of water stress for pepper from spectral reflections through artificial learning methods,” in Proc. 26th Signal Processing and Communications Applications Conf. (SIU), Izmir, Turkey, 2018, pp. 1–4. [Online]. Available: https://doi.org/10.1109/SIU.2018.8404765
8. [8] E. Gunes, E. Ulku, and K. Yildiz, “Classification of hazelnuts with CNN based deep learning system,” Selcuk University Journal of Engineering Sciences, vol. 21, no. 3, pp. 111–120, 2022. [Online]. Available: https://sujes.selcuk.edu.tr/sujes/article/view/609

9. [9] T. Boyar and K. Yildiz, “Powdery mildew detection in hazelnut with deep learning,” Hittite Journal of Science and Engineering, vol. 9, no. 3, pp. 159–166, 2022. [Online]. Available: https://doi.org/10.17350/HJSE19030000267
10. [10] H. N. Ngugi, A. E. Ezugwu, A. A. Akinyelu, and L. Abualigah, “Revolutionizing crop disease detection with computational deep learning: A comprehensive review,” Environmental Monitoring and Assessment, vol. 196, pp. 1–24, 2024. [Online]. Available: https://doi.org/10.1007/s10661-024-12454-z
11. [11] M. M. Khalid and O. Karan, “Deep learning for plant disease detection,” International Journal of Mathematics, Statistics, and Computer Science, vol. 2, pp. 75–84, 2023. [Online]. Available: https://doi.org/10.59543/ijmscs.v2i.8343
12. [12] Z. Zhang, S. Khanal, A. Raudenbush, K. Tilmon, and C. Stewart, “Assessing the efficacy of machine learning techniques to characterize soybean defoliation from unmanned aerial vehicles,” Computers and Electronics in Agriculture, vol. 193, 2022. [Online]. Available: https://doi.org/10.1016/j.compag.2021.106682
13. [13] “Crop mapping using fused optical-radar data set,” UCI Machine Learning Repository, 2020. [Online]. Available: https://doi.org/10.24432/C5G89D
14. [14] L. Breiman, J. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, 1st ed., Chapman and Hall/CRC, 1984. [Online]. Available: https://doi.org/10.1201/9781315139470
15. [15] J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, pp. 81–106, 1986. [Online]. Available: https://doi.org/10.1007/BF00116251
16. [16] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, 2009.
17. [17] M. Bansal, A. Goyal, and A. Choudhary, “A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short-term memory algorithms in machine learning,” Decision Analytics Journal, vol. 3, 2022. [Online]. Available: https://doi.org/10.1016/j.dajour.2022.100071
18. [18] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, Springer, 2013.
19. [19] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
20. [20] M. E. Boujnouni, “A study and identification of COVID-19 viruses using N-grams with naïve Bayes, K-nearest neighbors, artificial neural networks, decision tree and support vector machine,” in Proc. Int. Conf. Intelligent Systems and Computer Vision (ISCV), 2022. [Online]. Available: https://doi.org/10.1109/ISCV54655.2022.9806081
21. [21] G. Modica, G. D. Luca, G. Messina, and S. Praticò, “Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery: A case study in a citrus orchard and an onion crop,” European Journal of Remote Sensing, vol. 54, no. 1, 2021. [Online]. Available: https://doi.org/10.1080/22797254.2021.1951623
22. [22] K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
23. [23] V.-H. Nhu et al., “Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naïve Bayes tree, artificial neural network, and support vector machine algorithms,” International Journal of Environmental Research and Public Health, vol. 17, no. 8, p. 2749, 2020. [Online]. Available: https://doi.org/10.3390/ijerph17082749
24. [24] F. T. Teshome, H. K. Bayabil, G. Hoogenboom, B. Schaffer, A. Singh, and Y. Ampatzidis, “Unmanned aerial vehicle (UAV) imaging and machine learning applications for plant phenotyping,” Computers and Electronics in Agriculture, vol. 212, 2023. [Online]. Available: https://doi.org/10.1016/j.compag.2023.108064
25. [25] C. Huang, L. S. Davis, and J. R. G. Townshend, “An assessment of support vector machines for land cover classification,” International Journal of Remote Sensing, vol. 23, no. 4, pp. 725–749, 2002. [Online]. Available: https://doi.org/10.1080/01431160110040323
26. [26] G. Mountrakis, J. Im, and C. Ogole, “Support vector machines in remote sensing: A review,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 3, pp. 247–259, 2011. [Online]. Available: https://doi.org/10.1016/j.isprsjprs.2010.11.001
27. [27] Y. Lana et al., “Comparison of machine learning methods for citrus greening detection on UAV multispectral images,” Computers and Electronics in Agriculture, vol. 171, 2020. [Online]. Available: https://doi.org/10.1016/j.compag.2020.105234
28. [28] D. Powers, “Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation,” Journal of Machine Learning Technologies, vol. 2, 2011. [Online]. Available: https://doi.org/10.9735/2229-3981
29. [29] X. Li et al., “Feature analysis network: An interpretable idea in deep learning,” Cognitive Computation, vol. 16, pp. 803–826, 2024. [Online]. Available: https://doi.org/10.1007/s12559-023-10238-0
30. [30] A. Sankaran, P. Detterer, K. Kannan, N. Alachiotis, and F. Corradi, “An event-driven recurrent spiking neural network architecture for efficient inference on FPGA,” in Proc. Int. Conf. on Neuromorphic Systems (ICONS), 2022, Article 12, pp. 1–8. [Online]. Available: https://doi.org/10.1145/3546790.3546802