The agriculture sector, once reliant only on external inputs for increased yields, faces repercussions of diminished natural systems and significant environmental impact. High-precision agriculture aims to curb excessive usage of fertilizers and pesticides while prioritizing sustainability. Modern technologies can shape decisions toward the betterment of soil health, plant protection, and yield enhancement. Predictive models employing machine learning aid farmers in crop performance estimation, disease detection, soil health assessment, and yield prediction among others. These insights inform field operations, logistics, and contract negotiations. Leveraging machinery data, weather data, and drone imagery using machine learning can help us refine farming practices. Recurrent neural networks (RNN) can aid in various farming challenges like plant stress detection and irrigation. Yet, challenges persist in optimizing hyperparameters, a task we tackled using the nature-inspired crayfish optimization algorithm (COA) adapted to tune echo state networks (ESN) for multivariate time series forecasting of crop yield. This study demonstrates the utility of machine learning in agriculture, enabling yield assessment for improved sustainability.

The problem of fake news on the Internet is not new. However, in the case of a global pandemic, this kind of misinformation can be dangerous, confusing, and costly in terms of the loss of human lives. The ongoing COVID-19 pandemic has unfortunately shown the significant and remarkable spread of fake news, concerning the disease itself, vaccination, number of deaths, and so on. It is necessary to develop an effective algorithm that will be able to detect COVID-19 misinformation and help scientists to easily separate fake from true news. The research presented in this paper proposes an arithmetic optimization algorithm (AOA) - based approach that can improve the classification results by reducing the number of features and achieve high accuracy. The AOA has been utilized as a wrapper feature selection. The obtained simulation results were subject to a comparative analysis with both world-class classifiers and other nature-inspired evolutionary approaches. The results of the simulation indicate better performance of the proposed approach with AOA over other algorithms and demonstrate that it obtains superior accuracy.