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.

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.

Allelopathic plants can be widely used in bio-farming considering their potential role in the improvement of seed germination. The study presented in this work was designed to evaluate the effects of two extraction methods and the effects of allelopathic aqueous extracts from twenty plants as seed dressing preparations on the number of germinating and infested seeds of cauliflower (Brassica oleracea convarietas L. botrytis var. botrytis). Plant extracts (in the form of cold-soaked macerates and infusions) were used for seed dressing. The percentages of normally germinating, non-germinating, and pathogen-infested seeds were determined in a paper test. Of the 20 herbal plant species used in the study, the biopreparations extracted from Zea mays L. moles were the most effective as they evoked the most beneficial effects on both seed germination and reduction of infestation by microbial pathogens. The study also showed that infusions used for seed treatment were better at improving cauliflower seed germination than were macerates. This method of extract preparation probably enabled an increase both in the availability and activity of allelochemical compounds.

Nowadays the size and quality of potato yields is very important aspect in agriculture, due to the continuous climate change. Plants exposed to abiotic stress need new protection tools such as plant biostimulant. The new definition of this product include plant extracts as novel biostimulants. The aim of the study was to assess whether the extracts from Artemisia vulgaris L. would act as classic biostimulants, by affecting metabolic pathways. Since these are pilot studies, the content of chlorophyll, carotenoids, proline and polyphenols was chosen as indicators of changes in plants. The experiment was carried out under controlled environmental conditions on a very early cultivar Irys. The obtained results showed that foliar treatment of plants with extracts from Artemisia vulgaris L. had a positive effect on the increase of chlorophyll a and chlorophyll b content and its total concentration in potato leaves. The highest increase in the total chlorophyll content, amounting to 26.27% on average, was observed in plants sprayed with macerate at the dose of 0.6 mL·plant^-1. Additionally, an increase in the carotenoids content was observed in plants sprayed with macerate. The study demonstrated that the polyphenols level was largely dependent on the method of extracts production and the dose of the tested extracts. Macerate and infusion applied in a higher dose induced in plants the changes in the concentration of polyphenols. The overall evaluation of the effectiveness of the tested preparations showed higher effectiveness of the macerate for all the analyzed traits.

In laboratory tests, the effectiveness of water plant extracts obtained from 20 species of herbal plants on the degree of contamination of white mustard seeds (Sinapis alba L.) by fungal and bacterial pathogens was evaluated. The analysis showed that the contamination of the tested seed material was statistically significantly influenced by the plant species from which the biopreparations were obtained. However, there were no significant differences in the method of preparation of extracts for the examined traits. The tested natural preparations had a different effect on limiting the superficial growth of bacteria and fungi on seeds. However, infusion and macerate obtained from the bark of Salix alba L. were the most effective in limiting the contamination with both bacterial and fungal pathogens. The number of seeds infected with fungi after the application of these preparations was 24.25% on average, whereas in the case of bacterial colonization of the tested material, the value of this indicator was 1.00% on average.

A sufficiently early and accurate prediction can help to steer crop yields more consciously, resulting in food security, especially with an expanding world population. Additionally, prediction related to the possibility of reducing agricultural chemistry is very important in an era of climate change. This study analyzes the performance of pea (Pisum sativum L.) seed yield prediction by a linear (MLR) and non-linear (ANN) model. The study used meteorological, agronomic and phytophysical data from 2016–2020. The neural model (N2) generated highly accurate predictions of pea seed yield—the correlation coefficient was 0.936, and the RMS and MAPE errors were 0.443 and 7.976, respectively. The model significantly outperformed the multiple linear regression model (RS2), which had an RMS error of 6.401 and an MAPE error of 148.585. The sensitivity analysis carried out for the neural network showed that the characteristics with the greatest influence on the yield of pea seeds were the date of onset of maturity, the date of harvest, the total amount of rainfall and the mean air temperature.