باقری، حمید. (1401). استفاده از الگوریتم فراابتکاری کرم شب تاب در بهبود دقت طبقه‌بندی تصویر ماهواره ای، مطالعه موردی: شهر رفسنجان. سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، انتشار آنلاین 6 تیرماه 1401. doi: 10.30495/girs.2022.692233
شریفی، عبید، اصغری بیرامی، بهنام، و مختارزاده، مهدی. (1400). طبقه‌بندی تصاویر ابرطیفی با استفاده از ادغام ویژگی‌های طیفی و مکانی در شبکه‌های عصبی پیچشی. نشریه مهندسی فناوری اطلاعات مکانی، 9(2)، 27-1. doi: 10.52547/jgit.9.2.1
کاویانی، مسعود. (1401). یادگیری ماشین و یادگیری عمیق با زبان‌های پایتون و R. انتشارات دیباگران. چاپ اول. 246 صفحه. تهران، ایران.
نیکخواه، امیره. (1397). واژه نامه علم داده. انتشارات لیلاژ. چاپ اول. 142 صفحه. تهران، ایران.
Aadhar, S., & Mishra, V. (2017). High-resolution near real-time drought monitoring in South Asia. Scientific Data, 4(1), 1-14. doi: 10.1038/sdata.2017.145
Ahmadi, A., Olyaei, M., Heydari, Z., Emami, M., Zeynolabedin, A., Ghomlaghi, A., ... & Sadegh, M. (2022). Groundwater level modeling with machine learning: a systematic review and meta-analysis. Water, 14(6), 949. doi: 10.3390/w14060949
Alibabaei, K., Gaspar, P. D., & Lima, T. M. (2021). Crop yield estimation using deep learning based on climate big data and irrigation scheduling. Energies, 14(11), 3004. doi: 10.3390/en14113004
Azari, A., Zeynoddin, M., Ebtehaj, I., Sattar, A. M., Gharabaghi, B., & Bonakdari, H. (2021). Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting. Acta Geophysica, 69(4), 1395-1411. doi: 10.1007/s11600-021-00617-2
Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758. doi: 10.3390/s21113758
Bhagat, M., Kumar, D., & Kumar, D. (2019). Role of Internet of Things (IoT) in smart farming: A brief survey. 2019 Devices for Integrated Circuit (DevIC), 141-145. doi: 10.1109/DEVIC.2019.8783800
Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote sensing of environment, 269, 112831. doi: 10.1016/j.rse.2021.112831
Burra, D. D., Hildebrand, J., Giles, J., Nguyen, T., Hasiner, E., Schroeder, K., ... & Kropff, W. (2021). Digital Agriculture Profile: Viet Nam. Food and Agriculture Organisation of the United Nations. Rome, Italy.
Cedric, L. S., Adoni, W. Y. H., Aworka, R., Zoueu, J. T., Mutombo, F. K., Krichen, M., & Kimpolo, C. L. M. (2022). Crops yield prediction based on machine learning models: Case of West African countries. Smart Agricultural Technology, 2, 100049. doi: 10.1016/j.atech.2022.100049
Chang, Y. C., Huang, T. W., & Huang, N. F. (2019, September). A machine learning based smart irrigation system with LoRa P2P networks. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS) (pp. 1-4). IEEE. https://doi.org/10.23919/APNOMS.2019.8893034
Chapman, P., (1999). Julian Clinton (SPSS), Randy Kerber (NCR), Thomas Khabaza (SPSS), Thomas Reinartz (DaimlerChrysler), Colin Shearer (SPSS) and Rüdiger Wirth (DaimlerChrysler),". CRISP-DM 1.0. Step-by-step data mining guide. Available online: https://www.coursehero.com/file/14884931/CRISP-DM-Process-Model-User-Guide
Chen, J., Li, M., & Wang, W. (2012). Statistical uncertainty estimation using random forests and its application to drought forecast. Mathematical Problems in Engineering, 2012(1), 915053, 1-12. doi: 10.1155/2012/915053
Chen, T. H., Lee, M. H., Hsia, I. W., Hsu, C. H., Yao, M. H., & Chang, F. J. (2022). Develop a Smart Microclimate Control System for Greenhouses through System Dynamics and Machine Learning Techniques. Water, 14(23), 3941. doi: 10.3390/w14233941
Chen, Z., Zhu, Z., Jiang, H., & Sun, S. (2020). Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. Journal of Hydrology, 591, 125286. doi: 10.1016/j.jhydrol.2020.125286
Clark, M., & Tilman, D. (2017). Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environmental Research Letters, 12(6), 064016. doi: 10.1088/1748-9326/aa6cd5
Dwivedi, R. (2020). How to use the Random Forest classifier in Machine learning?. [Online]. Available: https://www.analyticssteps.com/blogs/how-use-random-forest-classifier-machine-learning. Updated: 1/18/2021.
El Bilali, A., Taleb, A. and Brouziyne, Y., 2021. Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agricultural Water Management, 245, p.106625. doi: 10.1016/j.agwat.2020.106625
FAO. (2020). The State of Food and Agriculture 2020. Overcoming water challenges in agriculture. Rome.
Fan, J., Yue, W., Wu, L., Zhang, F., Cai, H., Wang, X., ... & Xiang, Y. (2018). Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agricultural and forest meteorology, 263, 225-241. doi: 10.1016/j.agrformet.2018.08.019
Feng, P., Wang, B., Li Liu, D., & Yu, Q. (2019). Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia. Agricultural Systems, 173, 303-316. doi: 10.1016/j.agsy.2019.03.015
Gao, H., Zhangzhong, L., Zheng, W., & Chen, G. (2023). How can agricultural water production be promoted? a review on machine learning for irrigation. Journal of Cleaner Production, 414, 137687. doi: 10.1016/j.jclepro.2023.137687
Ghassemi, B., Dujakovic, A., Żółtak, M., Immitzer, M., Atzberger, C., & Vuolo, F. (2022). Designing a European-wide crop type mapping approach based on machine learning algorithms using LUCAS field survey and Sentinel-2 data. Remote sensing, 14(3), 541. doi: 10.3390/rs14030541
Goap, A., Sharma, D., Shukla, A. K., & Krishna, C. R. (2018). An IoT based smart irrigation management system using Machine learning and open source technologies. Computers and electronics in agriculture, 155, 41-49. doi: 10.1016/j.compag.2018.09.040
Gong, L., Yan, J., Chen, Y., An, J., He, L., Zheng, L., & Zou, Z. (2022). An IoT-based intelligent irrigation system with data fusion and a self-powered wide-area network. Journal of Industrial Information Integration, 29, 100367. doi: 10.1016/j.jii.2022.100367
Gumma, M. K., Tummala, K., Dixit, S., Collivignarelli, F., Holecz, F., Kolli, R. N., & Whitbread, A. M. (2022). Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information. Geocarto International, 37(7), 1833-1849. doi: 10.1080/10106049.2020.1805029
Haghiabi, A. H., Nasrolahi, A. H., & Parsaie, A. (2018). Water quality prediction using machine learning methods. Water Quality Research Journal, 53(1), 3-13. doi: 10.2166/wqrj.2018.025
Hamrani, A., Akbarzadeh, A., & Madramootoo, C. A. (2020). Machine learning for predicting greenhouse gas emissions from agricultural soils. Science of The Total Environment, 741, 140338. doi: 10.1016/j.scitotenv.2020.140338
Hudait, M., & Patel, P. P. (2022). Crop-type mapping and acreage estimation in smallholding plots using Sentinel-2 images and machine learning algorithms: Some comparisons. The Egyptian Journal of Remote Sensing and Space Science, 25(1), 147-156. doi: 10.1016/j.ejrs.2022.01.004
IBM. (n.d.). K-Nearest Neighbors (KNN). IBM. Retrieved September 26, 2024, from https://www.ibm.com/topics/knn?mhsrc=ibmsearch_a&mhq=KNN
Jayaraman, P., Nagarajan, K. K., & Partheeban, P. (2022). A Review on Artificial intelligence Algorithms and Machine Learning to Predict the Quality of Groundwater for Irrigation Purposes. In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (Vol. 1, pp. 1-8). IEEE. doi: 10.1109/ICDSAAI55433.2022.10028857
Kamienski, C., Soininen, J. P., Taumberger, M., Dantas, R., Toscano, A., Salmon Cinotti, T., ... & Torre Neto, A. (2019). Smart water management platform: IoT-based precision irrigation for agriculture. Sensors, 19(2), 276. doi: 10.3390/s19020276
Kelleher, J. D., & Tierney, B. (2018). Data science. MIT Press. Massachusetts, USA. doi: 10.7551/mitpress/11140.001.0001
Khadr, M. (2016). Forecasting of meteorological drought using Hidden Markov Model (case study: The upper Blue Nile river basin, Ethiopia). Ain Shams Engineering Journal, 7(1), 47-56. doi: 10.1016/j.asej.2015.11.005
Khosla, E., Dharavath, R., & Priya, R. (2020). Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression. Environment. Development and Sustainability, 22, 5687-5708. doi: 10.1007/s10668-019-00445-x
Kotu, V., & Deshpande, B. (2018). Data science: concepts and practice. Morgan Kaufmann. Massachusetts, USA.
Kouadio, L., Deo, R. C., Byrareddy, V., Adamowski, J. F., & Mushtaq, S. (2018). Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties. Computers and electronics in agriculture, 155, 324-338. doi: j.compag.2018.10.014
Kranjčić, N., Medak, D., Župan, R., & Rezo, M. (2019). Machine learning methods for classification of the green infrastructure in city areas. ISPRS International Journal of Geo-Information, 8(10), 463. doi: 10.3390/ijgi8100463
Li, J., Zhou, S., & Hu, R. (2016). Hydrological drought class transition using SPI and SRI time series by loglinear regression. Water resources management, 30, 669-684. doi: 10.1007/s11269-015-1184-7
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. doi: 10.3390/s18082674
Lindemann, M. D. (2019). 169 awardee talk-nutrition from a risk management perspective. Journal of Animal Science, 97(Supplement_3), 174-175. doi: 10.1093/jas/skz258.358
Ly, Q. V., Nguyen, X. C., Lê, N. C., Truong, T. D., Hoang, T. H. T., Park, T. J., ... & Hur, J. (2021). Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea. Science of The Total Environment, 797, 149040. doi: 10.1016/j.scitotenv.2021.149040
Maxwell, S. (2015). One water: the need for more holistic thinking, analysis, and policymaking in water. Journal‐American Water Works Association, 107(3), 21-24. doi: 10.5942/jawwa.2015.107.0048
Mehdizadeh, S., Behmanesh, J., & Khalili, K. (2017). Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Computers and electronics in agriculture, 139, 103-114. doi: 10.1016/j.compag.2017.05.002
Moghadas, D., Jadoon, K. Z., & McCabe, M. F. (2019). Spatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network. Journal of Applied Geophysics, 169, 226-238. doi: 10.1016/j.jappgeo.2019.07.004
Muñoz, M., Guzmán, J. L., Sánchez-Molina, J. A., Rodríguez, F., Torres, M., & Berenguel, M. (2020). A new IoT-based platform for greenhouse crop production. IEEE Internet of Things Journal, 9(9), 6325-6334. doi: 10.1109/JIOT.2020.2996081
Nie, H., Yang, L., Li, X., Ren, L., Xu, J., & Feng, Y. (2018). Spatial prediction of soil moisture content in winter wheat based on machine learning model. In 2018 26th International Conference on Geoinformatics (pp. 1-6). IEEE. doi: 10.1109/GEOINFORMATICS.2018.8557119
Osman, A. I. A., Ahmed, A. N., Huang, Y. F., Kumar, P., Birima, A. H., Sherif, M., ... & El-Shafie, A. (2022). Past, present and perspective methodology for groundwater modeling-based machine learning approaches. Archives of Computational Methods in Engineering, 29(6), 3843-3859. doi: 10.1007/s11831-022-09715-w
Pan, X., & Zhao, J. (2018). High-resolution remote sensing image classification method based on convolutional neural network and restricted conditional random field. Remote Sensing, 10(6), 920. doi: 10.3390/rs10060920
Perea, R. G., Poyato, E. C., Montesinos, P., & Díaz, J. R. (2019). Prediction of irrigation event occurrence at farm level using optimal decision trees. Computers and electronics in agriculture, 157, 173-180. doi: 10.1016/j.compag.2018.12.043
Pierson, L. (2021). Data science for dummies. John Wiley & Sons, NewYork, USA.
Pourmohammadali, B., Hosseinifard, S. J., Salehi, M. H., Shirani, H., & Boroujeni, I. E. (2019). Effects of soil properties, water quality and management practices on pistachio yield in Rafsanjan region, southeast of Iran. Agricultural water management, 213, 894-902. doi: 10.1016/j.agwat.2018.12.005
Prodhan, F. A., Zhang, J., Yao, F., Shi, L., Pangali Sharma, T. P., Zhang, D., ... & Mohana, H. P. (2021). Deep learning for monitoring agricultural drought in South Asia using remote sensing data. Remote Sensing, 13(9), 1715. doi: 10.3390/rs13091715
Pu, F., Ding, C., Chao, Z., Yu, Y., & Xu, X. (2019). Water-quality classification of inland lakes using Landsat8 images by convolutional neural networks. Remote Sensing, 11(14), 1674. doi: 10.3390/rs11141674
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1, 81-106.
Raj. R. (2021). Supervised, Unsupervised, And Semi-Supervised Learning With Real-Life Usecase. Enjoy Algorithm. [Online]. Available: https://www.enjoyalgorithms.com/blogs/supervised-unsupervised-and-semisupervised-learning. Updated: 07/08/2021.
Ranjan, A. K., & Parida, B. R. (2019). Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj district, Jharkhand (India). Spatial Information Research, 27(4), 399-410. doi: 10.1007/s41324-019-00246-4
Raschka, S. (2018). STAT 479: Machine Learning Lecture Notes. Department of Statistics, University of Wisconsin–Madison. https://sebastianraschka.com/pdf/lecture-notes/stat479fs18/07_ensembles_notes.pdf
Rosegrant, M. W., Ringler, C., & Zhu, T. (2009). Water for agriculture: maintaining food security under growing scarcity. Annual review of Environment and resources, 34, 205-222. doi: 10.1146/annurev.environ.030308.090351
Saraiva, M., Protas, É., Salgado, M., & Souza Jr, C. (2020). Automatic mapping of center pivot irrigation systems from satellite images using deep learning. Remote Sensing, 12(3), 558. doi: 10.3390/rs12030558
ScienceDirect. (n.d.). Search results for "water, machine learning, agriculture". Retrieved December 2023, from https://www.sciencedirect.com
Shen, R., Huang, A., Li, B., & Guo, J. (2019). Construction of a drought monitoring model using deep learning based on multi-source remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 79, 48-57. doi: 10.1016/j.jag.2019.03.006
Singh, R., Deshwal, A., & Kumar, K. (2021). Implementation of smart irrigation system using intelligent systems and machine learning approaches. In Data Science and Innovations for Intelligent Systems (pp. 299-318). CRC Press, Florida, USA.
Sinha, B. B., & Dhanalakshmi, R. (2022). Recent advancements and challenges of Internet of Things in smart agriculture: A survey. Future Generation Computer Systems, 126, 169-184. doi: 10.1016/j.future.2021.08.006
Sundmaeker, H., Verdouw, C., Wolfert, S., & Freire, L. P. (2022). Internet of food and farm 2020. In Digitising the Industry Internet of Things Connecting the Physical, Digital and VirtualWorlds (pp. 129-151). River Publishers.
Virnodkar, S. S., Pachghare, V. K., Patil, V. C., & Jha, S. K. (2020). Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precision Agriculture, 21(5), 1121-1155. doi: 10.1007/s11119-020-09711-9
Wang, H., Liu, C., & Zhang, L. (2002). Water-saving agriculture in China: an overview. Advances in Agronomy, 75, 135-171. doi: 10.1016/S0065-2113(02)75004-9
Xing, L., Cui, N., Guo, L., Du, T., Gong, D., Zhan, C., ... & Wu, Z. (2022). Estimating daily reference evapotranspiration using a novel hybrid deep learning model. Journal of Hydrology, 614, 128567. doi: 10.1016/j.jhydrol.2022.128567
Yaseen, Z. M., Al-Juboori, A. M., Beyaztas, U., Al-Ansari, N., Chau, K. W., Qi, C., ... & Shahid, S. (2020). Prediction of evaporation in arid and semi-arid regions: A comparative study using different machine learning models. Engineering applications of computational fluid mechanics, 14(1), 70-89. doi: 10.1080/19942060.2019.1680576
Zanganeh, R., Mojaradi, B., & Jabbari, E. (2016). Leak Detection from the Buried Water Transmission Pipeline Using Landsat 8 Satellite Images (Case Study of the Kosar Water Transmission Pipeline). In Proceedings of the International Conference on Civil Engineering, Tehran, Iran (pp. 8-10).
Zhi, W., Feng, D., Tsai, W. P., Sterle, G., Harpold, A., Shen, C., & Li, L. (2021). From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale?. Environmental science & technology, 55(4), 2357-2368. doi: 10.1021/acs.est.0c06783
)
ارسال نظر در مورد این مقاله