References
- A Rodolfo, M.; Drilona, E. Climate Change in Sub-Saharan Africa’s Fragile States; International Monetary Fund: Washington, DC, USA, 2022.
- Kelvin, M.; Ng’ombe, J.N. Climate change impacts on sustainable maize production in Sub-Saharan Africa: A review. Maize Prod. Use 2019, 47–75.
- Nyaga, J.N. Assessment of Perceived Impacts of Climate Change on Agricultural Crops Productions and Its Effects on Food Security: A Case Study of Small-Scale Farmers in Murang’a County Kenya; Università Ca’Foscari Venezia: Venice, Italy, 2021; Volume 123.
- Kahn, M.E.; Mohaddes, K.; Ng, R.N.C.; Pesaran, M.H.; Raissi, M.; Yang, J.-C. Long-term macroeconomic effects of climate change: A cross-country analysis. Energy Econ. 2021, 104, 105624.
- Koudahe, K.; Djaman, K.; Bodian, A.; Irmak, S.; Sall, M.; Diop, L.; Balde, A.B.; Rudnick, D.R. Trend analysis in rainfall, reference evapotranspiration and aridity index in Southern Senegal: Adaptation to the vulnerability of rainfed rice cultivation to climate change. Atmos. Clim. Sci. 2017, 7, 476–495.
- Shen, X.; Liu, B.; Henderson, M.; Wang, L.; Jiang, M.; Lu, X. Vegetation greening, extended growing seasons, and temperature feedbacks in warming temperate grasslands of China. J. Clim. 2022, 35, 1–51.
- Domingues, T.; Brandão, T.; Ferreira, J.C. Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey. Agriculture 2022, 12, 1350.
- Sharma, A.; Jain, A.; Gupta, P.; Chowdary, V. Machine Learning Applications for Precision Agriculture: A Comprehensive Review. IEEE Access 2021, 9, 4843–4873.
- Shalev-Shwartz, S.; Ben-David, S. Understanding Machine Learning: From Theory to Algorithms, 3rd ed.; Cambridge University Press: Cambridge, UK, 2013.
- The Role of Weather Forecasting in Agriculture. Available online:
https://www.dtn.com/the-role-of-weather-forecasting-inagriculture/ (accessed on 8 April 2022). - Khan, N.A.; Qiao, J.; Abid, M.; Gao, Q. Understanding farm-level cognition of and autonomous adaptation to climate variability and associated factors: Evidence from the rice-growing zone of Pakistan. Land Use Policy 2021, 105, 105427
- Salehin, I.; Islam, M.S.; Saha, P.; Noman, S.; Tuni, A.; Hasan, M.M.; Baten, M.A. AutoML: A systematic review on automated machine learning with neural architecture search. J. Inf. Intell. 2024, 2, 52–81.
- Li, K.Y.; Burnside, N.G.; de Lima, R.S.; Peciña, M.V.; Sepp, K.; Cabral Pinheiro, V.H.; de Lima, B.R.C.A.; Yang, M.D.; Vain, A.; Sepp, K. An automated machine learning framework in unmanned aircraft systems: New insights into agricultural management practices recognition approaches. Remote Sens. 2021, 13, 319.
- Sharma, R.; Kamble, S.S.; Gunasekaran, A.; Kumar, V.; Kumar, A. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Oper. Res. 2020, 119, 104926.
- Benos, L.; Tagarakis, A.C.; Dolias, G.; Berruto, R.; Kateris, D.; Bochtis, D. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors 2021, 21, 3758.
- Peng, W.; Karimi Sadaghiani, O. A review on the applications of machine learning and deep learning in agriculture section for the production of crop biomass raw materials. Energy Sources Part A Recover. Util. Environ. Eff. 2023, 45, 9178–9201.
- Mohamed, S.A.; Metwaly, M.M.; Metwalli, M.R.; AbdelRahman, M.A.E.; Badreldin, N. Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions. Remote Sens. 2023, 15, 1751.
- Getachew Tegegne, Assefa M. Melesse, Abeyou W. Worqlul, Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes, Science of The Total Environment, Volume 704, 2020, 135357, ISSN 0048-9697,
https://doi.org/10.1016/j.scitotenv.2019.135357. - Mansfield, L.A., Nowack, P.J., Kasoar, M. et al. Predicting global patterns of long-term climate change from short-term simulations using machine learning. npj Clim Atmos Sci 3, 44 (2020).
https://doi.org/10.1038/s41612-020-00148-5. - Labe, Zachary & Barnes, Elizabeth. (2021). Detecting Climate Signals Using Explainable AI With Single-Forcing Large Ensembles. Journal of Advances in Modeling Earth Systems. 13. 1–18. 10.1029/2021MS002464.
- Fyfe, John C, Kharin, Viatcheslav V, A Santer, Benjamin D, Cole, Jason N. S., A Gillett, Nathan P. Significant impact of forcing uncertainty in a large ensemble of climate model simulations. 2021. Proceedings of the National Academy of Sciences, e2016549118. V 118, 23. doi:10.1073/pnas.2016549118.
- Khan Sajid, Verma Susheel. Ensemble modeling to predict the impact of future climate change on the global distribution of Ole Europaea subsp. Cuspidate Frontiers in Forests and Global Change, VOLUME(5), 2022. DOI=10.3389/ffgc.2022.977691
- Jose, D.M., Vincent, A.M. & Dwarakish, G.S. Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques. Sci Rep 12, 4678 (2022).
- Labe, Zachary & Barnes, Elizabeth. (2022). Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks. Geophysical Research Letters. 49. 1–37. 10.1029/2022GL098173.
https://github.com/Shubha-ml/Crop-Prediction-Based-on-Region-Wise-Weather-Data - Y. Chen and J. Li, “Recurrent Neural Networks algorithms and applications,” 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), Zhuhai, China, 2021, pp. 38–43, doi: 10.1109/ICBASE53849.2021.00015.
- A. C. S, “Advancements in CNN Architectures for Computer Vision: A Comprehensive Review,” 2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS), Kanjirapally, India, 2023, pp. 1–7, doi: 10.1109/AICERA/ICIS59538.2023.10420413.
- P. Nagpal, S. A. Bhinge and A. Shitole, “A Comparative Analysis of ResNet Architectures,” 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Bangalore, India, 2022, pp. 1–8, doi: 10.1109/SMARTGENCON56628.2022.10083966.