Multilabel Classification for Predicting Crop Pests in Niger
Keywords:
Machine Learning; Multilabel Classification; Prediction; Crop pest; AgricultureAbstract
Crop pests pose serious threats to agricultural production and food security. With the advent of climate change in Niger, pest attacks have become increasingly frequent. This has become a crucial problem and a priority for farmers and government, as it can destroy the crop or harvest, thereby causing economic harm to the detriment of farmers and the population. Machine learning techniques are widely used in crop pests’ prediction. However, the existing approaches generally focus on the prediction of crop pests using traditional classification methods. These approaches are limited, as they do not make it possible to predict multiple crop pests. Thus, simultaneous and rapid prediction of multiple pests remains a major challenge. In this study, we proposed an approach to predict all the pests of a crop in various localities by using multilabel classification techniques. We developed and compared nine (9) multilabel classification models over two different periods (monthly and annual) using historical data on crop pest infestation and climate. The classifiers are evaluated using Hamming Loss (HL). It was observed that the Radom k-labELsets (RAkEL) classifier is better both on monthly and annual prediction of all pests, with a comparative HL percentage value of 3.63% and 5.1%, respectively. This study extends the models available for crop pest prediction and opens a new path to improving the prediction of crop pests.
Downloads
References
J. Bogatinovski, L. Todorovski, S. Džeroski, and D. Kocev, ‘Comprehensive comparative study of multi-label classification methods’, Expert Syst. Appl., vol. 203, p. 117215, Oct. 2022, doi: 10.1016/j.eswa.2022.117215.
F. Herrera, F. Charte, A. J. Rivera, and M. J. del Jesus, ‘Multilabel Classification’, in Multilabel Classification : Problem Analysis, Metrics and Techniques, F. Herrera, F. Charte, A. J. Rivera, and M. J. del Jesus, Eds., Cham: Springer International Publishing, 2016, pp. 17–31. doi: 10.1007/978-3-319-41111-8_2.
Z. A. Habou, M. K. Boubacar, and T. Adam, ‘Les systèmes de productions agricoles du Niger face au changement climatique: défis et perspectives’, Int. J. Biol. Chem. Sci., vol. 10, no. 3, pp. 1262–1272, 2016.
H. Kaur, D. D. Prashar, and Madhuri, ‘Applications of Machine Learning In Plant Disease Detection’, Think India J., vol. 22, no. 17, Art. no. 17, Sep. 2019.
I. Bhakta, S. Phadikar, K. Majumder, H. Mukherjee, and A. Sau, ‘A novel plant disease prediction model based on thermal images using modified deep convolutional neural network’, Precis. Agric., vol. 24, no. 1, pp. 23–39, Feb. 2023, doi: 10.1007/s11119-022-09927-x.
G. Delnevo, R. Girau, C. Ceccarini, and C. Prandi, ‘A Deep Learning and Social IoT Approach for Plants Disease Prediction Toward a Sustainable Agriculture’, IEEE Internet Things J., vol. 9, no. 10, pp. 7243–7250, May 2022, doi: 10.1109/JIOT.2021.3097379.
S. Lee and C. M. Yun, ‘A deep learning model for predicting risks of crop pests and diseases from sequential environmental data’, Plant Methods, vol. 19, no. 1, p. 145, Dec. 2023, doi: 10.1186/s13007-023-01122-x.
Y. Xu et al., ‘A deep learning model for rapid classification of tea coal disease’, Plant Methods, vol. 19, no. 1, p. 98, Sep. 2023, doi: 10.1186/s13007-023-01074-2.
E. Ayan, ‘Genetic Algorithm-Based Hyperparameter Optimization for Convolutional Neural Networks in the Classification of Crop Pests’, Arab. J. Sci. Eng., vol. 49, no. 3, pp. 3079–3093, Mar. 2024, doi: 10.1007/s13369-023-07916-4.
S. Ramesh et al., ‘Plant Disease Detection Using Machine Learning’, in 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), Apr. 2018, pp. 41–45. doi: 10.1109/ICDI3C.2018.00017.
K. Ahmed, T. Shahidi, S. Irfanul Alam, and S. Momen, ‘Rice Leaf Disease Detection Using Machine Learning Techniques’, Dec. 2019, pp. 1–5. doi: 10.1109/STI47673.2019.9068096.
S. Mishra, R. Sachan, and D. Rajpal, ‘Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition’, Procedia Comput. Sci., vol. 167, pp. 2003–2010, Jan. 2020, doi: 10.1016/j.procs.2020.03.236.
S. Gnanasekaran and G. Opiyo, ‘A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks’, Egypt. Inform. J., vol. 22, Mar. 2020, doi: 10.1016/j.eij.2020.02.007.
M. Agarwal, A. Singh, S. Arjaria, A. Sinha, and S. Gupta, ‘ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network’, Procedia Comput. Sci., vol. 167, pp. 293–301, Jan. 2020, doi: 10.1016/j.procs.2020.03.225.
T. Devi, P. Neelamegam, and A. Srinivasan, ‘Plant Leaf Disease Detection using K means Segmentation’, 2018. Accessed: May 28, 2024. [Online]. Available: https://www.semanticscholar.org/paper/Plant-Leaf-Disease-Detection-using-K-means-Devi-Neelamegam/c361350782367f0473f89a069ff4b269dc218ba1
M. Shanthalakshmi, M. Sandhiya, M. Rajalakshmi, and V. Ratheesh, ‘Paddy Disease Detection and Pesticide Recommender System for Farmers Using Multi SVM Technique’, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., pp. 721–725, Mar. 2019, doi: 10.32628/CSEIT1952214.
Neelakantan . P, ‘Analyzing the best machine learning algorithm for plant disease classification’, Mater. Today Proc., vol. 80, pp. 3668–3671, Jan. 2023, doi: 10.1016/j.matpr.2021.07.358.
T. Kasinathan, D. Singaraju, and S. R. Uyyala, ‘Insect classification and detection in field crops using modern machine learning techniques’, Inf. Process. Agric., vol. 8, no. 3, pp. 446–457, Sep. 2021, doi: 10.1016/j.inpa.2020.09.006.
D. Marković, D. Vujičić, S. Tanasković, B. Đorđević, S. Ranđić, and Z. Stamenković, ‘Prediction of Pest Insect Appearance Using Sensors and Machine Learning’, Sensors, vol. 21, no. 14, p. 4846, Jul. 2021, doi: 10.3390/s21144846.
E. Almeyda, J. Paiva Mimbela, and W. Ipanaqué, ‘Pest Incidence Prediction in Organic Banana Crops with Machine Learning Techniques’, Oct. 2020, pp. 1–4. doi: 10.1109/EIRCON51178.2020.9254034.
A. Tageldin, D. Adly, H. Mostafa, and H. S. Mohammed, ‘Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse’. bioRxiv, p. 2020.09.17.301168, Sep. 19, 2020. doi: 10.1101/2020.09.17.301168.
Q. Xiao, W. Li, P. Chen (陈鹏), and B. Wang, ‘Prediction of Crop Pests and Diseases in Cotton by Long Short Term Memory Network’, 2018, pp. 11–16. doi: 10.1007/978-3-319-95933-7_2.
P. Szymański and T. Kajdanowicz, ‘scikit-multilearn: A Python library for Multi-Label Classification’, J. Mach. Learn. Res., vol. 20, no. 6, pp. 1–22, 2019.
Y. Ren et al., ‘Multi-label classification for multi-drug resistance prediction of Escherichia coli’, Comput. Struct. Biotechnol. J., vol. 20, pp. 1264–1270, 2022, doi: 10.1016/j.csbj.2022.03.007.
M. T r, V. K. V, D. K. V, O. Geman, M. Margala, and M. Guduri, ‘The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification’, Healthc. Anal., vol. 4, p. 100247, Dec. 2023, doi: 10.1016/j.health.2023.100247.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
