Pengembangan Sistem Prediksi Waktu Penyiraman Optimal pada Perkebunan: Pendekatan Machine Learning untuk Peningkatan Produktivitas Pertanian

Mohammad F Anggarda; Iwan Kustiawan; Deasy R Nurjanah; Nurul F A Hakim

doi:10.30598/jbdp.2023.19.2.124

Mohammad F Anggarda Program Studi Pendidikan Teknik Otomasi Industri dan Robotika, Fakultas Pendidikan Teknologi dan Kejuruan, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi No.229, Isola, Kec. Sukasari, Kota Bandung, Jawa Barat 40154 Indonesia
Iwan Kustiawan Program Studi Teknik Elektro, Fakultas Pendidikan Teknologi dan Kejuruan, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi No.229, Isola, Kec. Sukasari, Kota Bandung, Jawa Barat 40154 Indonesia
Deasy R Nurjanah Program Studi Teknik Elektronika Politeknik TEDC, Jl. Politeknik Jl. Pesantren No. 2, Cibabat, Kec. Cimahi Utara, Kota Cimahi, Jawa Barat 40513
Nurul F A Hakim Program Studi Pendidikan Teknik Otomasi Industri dan Robotika, Fakultas Pendidikan Teknologi dan Kejuruan, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi No.229, Isola, Kec. Sukasari, Kota Bandung, Jawa Barat 40154 Indonesia

DOI: https://doi.org/10.30598/jbdp.2023.19.2.124

Keywords: Development of prediction system, optimal watering time, machine learning and agricultural productivity

Abstract

Modern agriculture relies heavily on technology, especially in irrigation management and crop watering. Several previous studies have applied field data-based predictive techniques to improve crop yields. This research aims to develop a prediction system for optimal watering time in plantations and agriculture using a machine learning approach. The rigorous methodology includes data capture, pre-processing, model evaluation and testing, validation, and visualization. High accuracy demonstrates the system's reliability in determining optimal watering needs to improve resource efficiency and crop yields in agriculture. The data obtained from the automatic weather station (AWS) via thingsboard is processed sequentially, starting from data retrieval in json format using postman to transformation into csv files with proper timestamp adjustment. The pre-processing stage includes data cleaning, variable selection, data integration, and generating a clean dataset. In the evaluation stage, the dataset is divided into training data and test data, with the application and comparison of logistic regression, random forest and decision tree models applied as classifiers. Furthermore, the validation and results stage includes prediction, performance testing using the confusion matrix, and visualization of prediction results in the form of text and icons that aim to increase interpetability for users through Google Collaboratory. The results of this research provide an overview of the optimal watering time based on the dataset from the automatic weather station. Further analysis shows that the implementation of machine learning models significantly improves the prediction accuracy, proving the effectiveness of the system in providing more precise watering time recommendations to increase agricultural productivity. The main objective is to develop a machine learning-based watering time prediction system using data from the automatic weather station and evaluate various classifier algorithms to select the best model.

Downloads

Download data is not yet available.

References

Ang, K. L.-M., & Seng, J. K. P. (2021). Big Data and Machine Learning With Hyperspectral Information in Agriculture. IEEE Access, 9(2), 36699–36718. https://doi.org/10.1109/ACCESS.2021.3051196

Araújo, S. O., Peres, R. S., Ramalho, J. C., Lidon, F., & Barata, J. (2023). Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives. Agronomy, 13(12), 2976. https://doi.org/10.3390/agronomy13122976

Attri, I., Awasthi, L. K., & Sharma, T. P. (2023). Machine learning in agriculture: a review of crop management applications. Multimedia Tools and Applications, 83(5), 12875–12915. https://doi.org/10.1007/s11042-023-16105-2

Deng, P., Gao, Y., Mu, L., Hu, X., Yu, F., Jia, Y., Wang, Z., & Xing, B. (2023). Development potential of nanoenabled agriculture projected using machine learning. Proceedings of the National Academy of Sciences, 120(25). https://doi.org/10.1073/pnas.2301885120

Ferrag, M. A., Shu, L., Friha, O., & Yang, X. (2022). Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions. IEEE/CAA Journal of Automatica Sinica, 9(3), 407–436. https://doi.org/10.1109/JAS.2021.1004344

Indu, Baghel, A. S., Bhardwaj, A., & Ibrahim, W. (2022). Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning. Computational Intelligence and Neuroscience, 2022(1), 1–10. https://doi.org/10.1155/2022/9408535

Menaha, M., & Lavanya, J. (2023a). Machine Learning Techniques in Agriculture. REST Journal on Data Analytics and Artificial Intelligence, 2(3), 23–25. https://doi.org/10.46632//jdaai/2/3/5

Menaha, M., & Lavanya, J. (2023b). Machine Learning Techniques in Agriculture. REST Journal on Data Analytics and Artificial Intelligence, 2(3), 23–25. https://doi.org/10.46632//jdaai/2/3/5

Menaha, M., & Lavanya, J. (2023c). Machine Learning Techniques in Agriculture. REST Journal on Data Analytics and Artificial Intelligence, 2(3), 23–25. https://doi.org/10.46632/jdaai/2/3/5

Mohan, D. S., Dhote, V., Mishra, P., Singh, P., & Srivastav, A. (2023). IoT Framework for Precision Agriculture: Machine Learning Crop Prediction. International Journal of Intelligent Systems and Applications in Engineering.

Mohinur Rahaman, M., & Azharuddin, M. (2022). Wireless sensor networks in agriculture through machine learning: A survey. Computers and Electronics in Agriculture, 197(1), 106928. https://doi.org/10.1016/j.compag.2022.106928

Morota, G., Ventura, R. V., Silva, F. F., Koyama, M., & Fernando, S. C. (2018). Big data analytics and precision animal agriculture symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture1. Journal of Animal Science, 96(4), 1540–1550. https://doi.org/10.1093/jas/sky014

Prema, P., Veeramani, A. & Sivakumar, T. (2022). Machine learning applications in agriculture. Journal of Agriculture Research and Technology, Special(01), 126–129. https://doi.org/10.56228/JART.2022.SP120

Pallathadka, H., Mustafa, M., Sanchez, D. T., Sekhar Sajja, G., Gour, S., & Naved, M. (2023). Impact of machine learning on management, healthcare and agriculture. Materials Today: Proceedings, 80, 2803–2806. https://doi.org/10.1016/j.matpr.2021.07.042

Priyani, S., Hakani, R., & Jhala, D. K. (2023). Smart Agriculture system Using Machine Learning. Researchgate.Net.

Saba, T., Rehman, A., Haseeb, K., Bahaj, S. A., & Lloret, J. (2023). Trust-based decentralized blockchain system with machine learning using Internet of agriculture things. Computers and Electrical Engineering, 108(2), 108674. https://doi.org/10.1016/j.compeleceng.2023.108674

Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021). Machine learning applications for precision agriculture: A Comprehensive Review. IEEE Access, 9(2), 4843–4873. https://doi.org/10.1109/ACCESS.2020.3048415

Sharma, P., Dadheech, P., Aneja, N., & Aneja, S. (2023). Predicting agriculture yields based on machine learning using regression and deep learning. IEEE Access, 11, 111255–111264. https://doi.org/10.1109/ACCESS.2023.3321861

Veeragandham, S., & H, S. (2020). A review on the role of machine learning in agriculture. Scalable Computing: Practice and Experience, 21(4), 583–589. https://doi.org/10.12694/scpe.v21i4.1699

Pengembangan Sistem Prediksi Waktu Penyiraman Optimal pada Perkebunan: Pendekatan Machine Learning untuk Peningkatan Produktivitas Pertanian

Development of Optimal Watering Time Prediction System in Plantation: A Machine Learning Approach for Improved Agricultural Productivity

Abstract

Downloads

References