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In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being a staple crop for billions of people, requires accurate and timely yield prediction to ensure global food security. This study was undertaken across two rice crop seasons in the Udham Singh Nagar district of Uttarakhand state to predict rice yield at 45, 60 and 90 days after transplanting (DAT) through machine learning (ML) models, utilizing a combination of optical and Synthetic Aperture Radar (SAR) data in conjunction with crop biophysical parameters. Results revealed that the ML models were able to provide relatively accurate early yield estimates. For summer rice, eXtreme gradient boosting (XGB) was the best-performing model at all three stages (45, 60, and 90 DAT), while for kharif rice, the best-performing models at 45, 60, and 90 DAT were XGB, Neural network (NNET), and Cubist, respectively. The combined ranking of ML models showed that prediction accuracy improved as the prediction date approaches harvest, and the best prediction of yield was observed at 90 DAT for both summer and kharif rice. Overall rankings indicate that for summer rice, the top three models were XGB, NNET, and Support vector regression, while for kharif rice, these were Cubist, NNET, and Random Forest, respectively. The findings of this study offer valuable insights into the potential of the combined use of remote sensing and biophysical parameters using ML models, which enhances food security planning and resource management by enabling more informed decision-making by stakeholders such as farmers, policy planners as well as researchers. [ABSTRACT FROM AUTHOR]

The article attempts to define and determine the intangible components of cultural heritage related to the spatial structure of land in a comprehensive way using computational methods. The components were quantified and a method of empirical evaluation of landscape durability was proposed for agricultural areas of significant cultural and historical value with an evident mosaic structure of fields, baulks, ponds, meadows, and forests. This method allows us to identify places more resistant to political transformation and those with greater cultural potential. The paper proposed an integrated approach to the measuring of the degree of preservation of spatial arrangements in the landscape based on a set of objects that describe the spatial land structure. The article classifies areas by the degree of preservation of rural spatial arrangements of land. The spatial analysis employed facilitated a synthetic quantification of the multi-criteria process. Three groups of factors were used: spatial assessment of land-cover type persistence (u), agricultural land structure persistence (w), and persistence of settlement buildings (z). The final results pinpointed areas in need of strategic intervention to sufficiently protect the rural cultural heritage, properly consider them in zoning planning, and ensure their sustainable development. The proposed tool can be used to monitor the degree of changes in the landscape layout structure when multiple time points are analysed as well. [ABSTRACT FROM AUTHOR]