A Cognitive IoT Learning Models for Agro Climatic Estimation Aiding Farmers in Decision making

[1] A Rodolfo, M.; Drilona, E. Climate Change in Sub-Saharan Africa’s Fragile States; International Monetary Fund: Washington, DC, USA, 2022. A RodolfoM. DrilonaE. Climate Change in Sub-Saharan Africa’s Fragile States International Monetary Fund Washington, DC, USA 2022 Search in Google Scholar

[2] 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. KelvinM. Ng’ombeJ.N. Climate change impacts on sustainable maize production in Sub-Saharan Africa: A review Maize Prod. Use 2019 47 75 Search in Google Scholar

[3] 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. NyagaJ.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 123 Search in Google Scholar

[4] 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. KahnM.E. MohaddesK. NgR.N.C. PesaranM.H. RaissiM. YangJ.-C. Long-term macroeconomic effects of climate change: A cross-country analysis Energy Econ. 2021 104 105624 Search in Google Scholar

[5] 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. KoudaheK. DjamanK. BodianA. IrmakS. SallM. DiopL. BaldeA.B. RudnickD.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 Search in Google Scholar

[6] 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. ShenX. LiuB. HendersonM. WangL. JiangM. LuX. Vegetation greening, extended growing seasons, and temperature feedbacks in warming temperate grasslands of China J. Clim. 2022 35 1 51 Search in Google Scholar

[7] 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. DominguesT. BrandãoT. FerreiraJ.C. Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey Agriculture 2022 12 1350 Search in Google Scholar

[8] Sharma, A.; Jain, A.; Gupta, P.; Chowdary, V. Machine Learning Applications for Precision Agriculture: A Comprehensive Review. IEEE Access 2021, 9, 4843–4873. SharmaA. JainA. GuptaP. ChowdaryV. Machine Learning Applications for Precision Agriculture: A Comprehensive Review IEEE Access 2021 9 4843 4873 Search in Google Scholar

[9] Shalev-Shwartz, S.; Ben-David, S. Understanding Machine Learning: From Theory to Algorithms, 3rd ed.; Cambridge University Press: Cambridge, UK, 2013. Shalev-ShwartzS. Ben-DavidS. Understanding Machine Learning: From Theory to Algorithms 3rd ed Cambridge University Press Cambridge, UK 2013 Search in Google Scholar

[10] The Role of Weather Forecasting in Agriculture. Available online: https://www.dtn.com/the-role-of-weather-forecasting-inagriculture/ (accessed on 8 April 2022). The Role of Weather Forecasting in Agriculture Available online: https://www.dtn.com/the-role-of-weather-forecasting-inagriculture/ (accessed on 8 April 2022) Search in Google Scholar

[11] 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 KhanN.A. QiaoJ. AbidM. GaoQ. 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 Search in Google Scholar

[12] 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. SalehinI. IslamM.S. SahaP. NomanS. TuniA. HasanM.M. BatenM.A. AutoML: A systematic review on automated machine learning with neural architecture search J. Inf. Intell. 2024 2 52 81 Search in Google Scholar

[13] 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. LiK.Y. BurnsideN.G. de LimaR.S. PeciñaM.V. SeppK. Cabral PinheiroV.H. de LimaB.R.C.A. YangM.D. VainA. SeppK. An automated machine learning framework in unmanned aircraft systems: New insights into agricultural management practices recognition approaches Remote Sens. 2021 13 319 Search in Google Scholar

[14] 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. SharmaR. KambleS.S. GunasekaranA. KumarV. KumarA. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance Comput. Oper. Res. 2020 119 104926 Search in Google Scholar

[15] 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. BenosL. TagarakisA.C. DoliasG. BerrutoR. KaterisD. BochtisD. Machine Learning in Agriculture: A Comprehensive Updated Review Sensors 2021 21 3758 Search in Google Scholar

[16] 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. PengW. Karimi SadaghianiO. 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 Search in Google Scholar

[17] 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. MohamedS.A. MetwalyM.M. MetwalliM.R. AbdelRahmanM.A.E. BadreldinN. 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 Search in Google Scholar

[18] 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. TegegneGetachew MelesseAssefa M. WorqlulAbeyou W. Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes Science of The Total Environment 704 2020 135357 0048-9697 https://doi.org/10.1016/j.scitotenv.2019.135357 Search in Google Scholar

[19] 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. MansfieldL.A. NowackP.J. KasoarM. 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 Search in Google Scholar

[20] 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. LabeZachary BarnesElizabeth 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 Open DOISearch in Google Scholar

[21] 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. FyfeJohn C KharinViatcheslav V SanterA BenjaminD ColeJason N. S. GillettA NathanP. Significant impact of forcing uncertainty in a large ensemble of climate model simulations 2021 Proceedings of the National Academy of Sciences e2016549118 118 23 10.1073/pnas.2016549118 Open DOISearch in Google Scholar

[22] 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 SajidKhan SusheelVerma 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 5 2022 10.3389/ffgc.2022.977691 Open DOISearch in Google Scholar

[23] 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). JoseD.M. VincentA.M. DwarakishG.S. Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques Sci Rep 12 4678 2022 Search in Google Scholar

[24] 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. LabeZachary BarnesElizabeth 2022 Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks Geophysical Research Letters 49 1 37 10.1029/2022GL098173 Open DOISearch in Google Scholar

[25] https://github.com/Shubha-ml/Crop-Prediction-Based-on-Region-Wise-Weather-Data https://github.com/Shubha-ml/Crop-Prediction-Based-on-Region-Wise-Weather-Data Search in Google Scholar

[26] 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. ChenY. LiJ. Recurrent Neural Networks algorithms and applications 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) Zhuhai, China 2021 38 43 10.1109/ICBASE53849.2021.00015 Open DOISearch in Google Scholar

[27] 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. 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 1 7 10.1109/AICERA/ICIS59538.2023.10420413 Open DOISearch in Google Scholar

[28] 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. NagpalP. BhingeS. A. ShitoleA. A Comparative Analysis of ResNet Architectures 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) Bangalore, India 2022 1 8 10.1109/SMARTGENCON56628.2022.10083966 Open DOISearch in Google Scholar