Today, one of the greatest challenges faced by the agriculture industry is the development of sustainable and environmentally-friendly systems to meet nutritional demands of the continuously growing global population. A number of research studies have recently been undertaken with the aim to indicate types of parameters used in plant production that would be able to improve plant growth as well as the effectiveness and quality of yield, and to help plants cope with environmental stress. The aim of this study was to verify a hypothesis that the implementation of a sustainable agricultural technology, based on the use of synthetic biostimulants, will allow not only increasing crop yield and quality but also improving the cost-effectiveness of common bean cultivation. The field experiment was conducted in three growing seasons (2016–2018). In the growing season, the plants were treated with Atonik and Tytanit biostimulants in the form of single or double spraying. We determinated biometric traits, seed yield, seed number, and 1000-seed weight. Further analyses included contents of nutraceutical potential. The economic effect of using biostimulants was also calculated. The results of our experiment allowed verifying a hypothesis that the implementation of a sustainable agricultural technology based on the use of synthetic preparations was an effective method to increase plant productivity and, consequently, economic profits to farmers.

Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.