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• The role of precision farming in crop production’s adaptation to climate change

  Zsófi Kudlacsek

  Views:

  15

  As a result of climate change, agricultural production is facing ever-greater challenges, including rising temperatures, changes in the distribution and quantity of precipitation, and an increase in the frequency of extreme weather events. The aim of this study is to outline the role of precision farming in adapting to climate change, with a particular focus on remote sensing, drone-based data collection, water and soil management, and the application of vegetation indices. The methodological basis of the research is a comprehensive review of relevant domestic and international literature. The study carries out a comparative assessment of the climate adaptation technologies and practical applications of precision farming, drawing on scientific publications, book chapters, doctoral theses, as well as statistical and specialist data sources. Following a review of the literature, it can be concluded that precision technologies contribute to the continuous monitoring of cropland, the implementation of site-specific interventions and the more efficient use of resources. Vegetation indices – in particular the NDVI, GNDVI, NDRE, NGRDI (VIGreen) and the leaf area index (LAI) – have proved to be particularly important in practical application, as they provide objective information on the current condition of the crop and support evidence-based decision-making regarding cultivation techniques. When combined with an appropriate agronomic approach and state-of-the-art digital technologies, precision farming can significantly increase the adaptability of agriculture, thereby contributing to the realisation of sustainable crop production. From a practical perspective, the findings of this review can facilitate the more targeted selection and application of precision technologies and vegetation indices, thereby directly supporting farmers’ data-driven decision-making, increasing resource efficiency, and mitigating production risks arising from climate change.

• Predicting maize yield with a multilayer perceptron (MLP) model using multivariate field data

  Péter Fejér

  Adrienn Széles

  Péter Ragán

  Csaba Juhász

  Éva Horváth

  Tamás Rátonyi

  Views:

  214

  This study presents the findings of a multi-year maize field trial conducted on experimental plots between 2017 and 2019, focusing on the application of machine learning techniques to enhance yield prediction accuracy. A multilayer perceptron (MLP) neural network was employed to model the effects of agronomic treatments, environmental variation, and compositional traits. Six distinct modeling scenarios were developed to explore different combinations of input variables, with the grain yield of maize serving as the sole output parameter. These scenarios range from treatment-only models to those incorporating detailed quality and compositional data. The primary objective was to evaluate how well MLP models can capture the complex, nonlinear relationships influencing yield under varying conditions. The findings provide valuable insight into the role of machine learning in supporting decision-making for sustainable crop production, especially under diverse technological and environmental settings. The approach demonstrated here offers a foundation for more adaptable, data-driven strategies in agronomic optimization.