The United States cotton industry is devoted to sustainable production strategies that reduce water, land, and energy consumption while enhancing soil health and cotton yield. Climate-smart agricultural solutions are being developed to increase yields and reduce operational costs. However, crop yield prediction is challenging because of the complex and nonlinear interactive effects of cultivar, soil type, management, pests and diseases, climate, and weather patterns on crops. To address this challenge, the machine learning (ML) method was used to predict yield, considering climatic change, soil diversity, cultivars, and fertilizer applications. Field data were collected over the southern US cotton belt in the 1980s and the 1990s. A second data source was generated from the process-based crop model GOSSYM to reflect the most recent effects of climate change over the last six years (2017–2022). We focused on nine locations in three southern states: Texas, Mississippi, and Georgia. The accumulated heat for each set of experimental data was used as an analogue for the time-series weather data to reduce the number of computations. The Random Forest (RF) regressor, Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM) regressor, Multiple Linear Regression (MLR), and neural networks were evaluated. Cross-validation was performed to obtain an improved model that did not suffer from overfitting. The RF regressor achieved an accuracy of 97.75%, with an R ² of roughly 0.98 and a root mean square error of 55.05 kg/ha. The results demonstrate how a simple and robust model can be developed and utilized to help cotton climate-smart efforts.

“Smart Cities” are a viable option to various issues caused by accelerated urban growth. To make smart cities a reality, smart citizens need to be connected to the “Smart City” through a digital ID. A digital ID allows citizens to make easy and effective use of amenities provided by the smart city such as healthcare, transport, finance, and energy. In this paper, we propose a proof-of-concept of facial authentication based end-to-end digital ID system for a smart city. Facial authentication systems are prone to various biometric template attacks and cyber security attacks. Our proposed system is designed to detect the first type of attack especially deepfake and presentation attacks. Users are authenticated each time before using facilities in the smart city. Authentication is performed on edge devices, making the process more secure as no data leaves the device during authentication. Facial data is stored at the cloud in a look up table format with an unidentifiable username. Our proposed solution achieved 97% accuracy in authentication with a False Rejection Ratio of 2% and False Acceptance Ratio of 3%.