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Artificial Intelligence (AI) in precision agriculture
87-104Views:62Artificial Intelligence (AI) is opening a new era in agriculture, particularly in the field of precision farming. This paper aims to provide an insightful overview of how AI technologies can be applied to yield prediction, crop health monitoring, and early pest and disease detection. Findings from international research clearly indicate that those countries and producers who adopt these tools early will gain a long-term competitive advantage.
The Hungarian agricultural sector faces increasing challenges: climate change, labor shortages, and market pressure. AI-based tools offer solutions through automation, precision, and cost efficiency. However, implementation requires not only access to technology but also a clear understanding, practical examples, and local case studies that demonstrate how AI works under Hungarian conditions and supports Hungarian farmers.
The future lies not only in technology but in its comprehension. Therefore, review articles like this one play a key role in bridging the gap between science and farm-level decision-making. The methods and models discussed here provide a foundation for developing domestic case studies and decision-support systems that directly benefit farmers in their everyday operations. -
Correlation study of NDVI and yield in maize at different phenological stages with different flight settings
31-50Views:283In this research, the authors sought solutions to one of the most important challenges facing agriculture. The growth of the world's population and the decline and degradation of arable land pose new challenges for agriculture. Cereal crops play a key role in food production, with maize being of particular importance as it is grown worldwide. Precision farming is playing an increasingly important role in modern agriculture, making remote sensing and data analysis of paramount importance.
The weather conditions in 2024 were unusual: spring was rainy and warm, while the summer months were exceptionally hot with an above-average number of heat days. The experiments were conducted during four different phenological stages of corn development (V5, V10, R1, and R3). Outside the growing season, we used three types of flight settings: measurements without RTK, with RTK, and with a combination of RTK and altitude tracking. During the study, three hybrids with different FAO numbers were analyzed, and the results were evaluated at five different nutrient levels in addition to the control.
It was observed that in the early (V5 and V10) phenological phases, there was a closer correlation between NDVI values and crop yield, which can be explained by the favorable spring and early summer weather conditions of the year. The flight settings showed similar results at three measurement times, but differences appeared in the R1 phenological phase. It can be assumed that the large amount of pollen deposited on the leaves during flowering influenced the NDVI values. In addition, the creation of orthomosaics from RTK and altitude tracking images proved to be more time-consuming and, in some cases, required multiple attempts with the WebODM software used. These results provided valuable data and serve as a good starting point for further research. -
Use of artificial intelligence in crop production experiments
47-66Views:56Understanding the relationships between crop yields, soil properties, weather patterns and input applications is important for optimising agricultural production. Sustainable intensification aims to increase productivity and input-use efficiency while enhancing the resilience of agricultural systems to adverse environmental conditions through improved management and technology. Artificial intelligence (AI) in precision agriculture (PA) enables growers to deploy highly targeted and precise farming practices based on site-specific agro-climatic field measurements. Recent advances in sensing, machine learning (ML) and modelling offer opportunities for novel smart digital technologies to enable sustainable intensification.
Through the review of the newest scientific publications the application of digital technology in crop production experiments was demonstrated in three topics: (i) continues monitoring of crop and soil characteristics, (ii) quantification of spatial and temporal variability of crop response and (iii) forecasting of crop yield by the use of machine learning approaches. It was concluded that the variation analysis and machine learning approaches can help identify and understand the practices that optimise yield. -
The evolution of decision support in crop production: yield models, precision data integration and artificial intelligence
151-170Views:50This study presents a literature review that traces the evolution of decision support for crop production, from process-based yield modeling through precision data sources to the application of artificial intelligence and hybrid models. Its aim is to provide a comparative analysis of how DSSAT, WOFOST, and AquaCrop-type models, as well as sensor, remote sensing, and yield mapping data, and AI-based methods, enhance the reliability of crop production decisions under various decision-making scenarios and conditions. The main finding of the review is that the practical value of decision support does not stem from the application of a single model or technology, but rather from the integration of scientifically sound models, quality-controlled site-specific data, and interpretable, adaptive algorithms. The study concludes that the key to domestic applicability lies in local calibration, strengthening long-term experimental and operational databases, and developing user competencies. -
The growing importance of short-rotation willow plantations in today’s changing agriculture
5-19Views:87Today’s experience clearly shows that crop production plays a key role in solving many of the challenges our world is facing. The effects of climate change are undeniable, as they influence yield stability in unpredictable ways and therefore also the profitability of growing crops. Growing environmental awareness is also changing the expectations towards crop production technologies. Plants remain important in renewable energy production, even though solar and nuclear energy are the main focus of current developments. Reducing the use of chemicals is also an important part of producing healthy food. The question is whether agriculture can introduce innovative technologies that help to meet these expectations.
The aim of this study is to explain why the cultivation of improved short-rotation willow (Salix sp.) deserves more attention in addressing today’s problems. We show how precision breeding can help to increase the biogas yield of energy tree plantations, which supports the wider use of willow as a raw material. On saline soils, planting salt-tolerant willow genotypes can provide several environmental benefits. Nowadays, the use of plant biostimulants is becoming more common. Aqueous extracts made from willow shoots can stimulate the growth and grain yield of maize plants.
The many possible ways of using willow confirm that Hungarian farmers should give more priority to establishing willow plantations on marginal lands. -
Plant health studies based on multispectral images in autumn cereal crops
95-108Views:69The development of precision agriculture and digitalisation has brought significant changes in agricultural technology and data-driven decision-making. Unmanned aerial vehicles (UAVs) and multispectral imaging technologies are effective tools for monitoring plant populations and detecting stress conditions (abiotic, biotic). Vegetation indices (NDVI, GNDVI, NDRE, LCI) provide detailed information about the physiological state of plants and the spatial distribution of stress factors. In the research conducted at the University of Debrecen, the ’MV Nádor’ winter wheat variety was examined in combinations of different tillage methods (autumn ploughing, strip tillage) and different nutrient supply treatments. During the multispectral data collection, high-resolution UAV images were used, which were analysed using the QGIS GIS software. The application of nutrients (nitrogen, phosphorus, potassium) and tillage methods had a significant impact on vegetation indices, which reflected the health status and homogeneity or heterogeneity of the plants. The results mean that higher nutrient levels showed more favorable growth and homogeneous plant stand. During the statistical analyses, we infer the spatial effects of stress factors based on the standard deviation and variance values. The values of NDVI and GNDVI indices showed an increasing trend with increasing nutrient levels, especially in the case of the 160 kg/ha nitrogen treatment, which ensures more uniform development. Based on the LCI and NDRE values, we obtained a much higher variance and SD value for the 160 kg N/ha treatment applied in autumn ploughing than for strip cultivation. Based on the data, precision technologies enable more sustainable and predictable crop production. -
Application of MALDI-TOF MS in scientific research and agricultural practice
105-132Views:165Today, MALDI-TOF MS is a key tool in proteomics, microbiology, medical diagnostics, and is widely used in food safety and materials science. This technology has undergone revolutionary developments in the recent years: new matrix materials have been developed that improve ionisation efficiency, and more advanced data processing algorithms have been developed to increase the accuracy of analyses. Due to its speed, cost-effectiveness, and reliability, this technology is increasingly emerging as an alternative to traditional identification methods.
MALDI-TOF MS is an innovative and versatile tool that may be important in plant breeding and food quality control. Not only does it provide reliable results in genetic purity testing and toxin analysis, but it also contributes to plant physiology research and the development of plant protection strategies, as well as the detailed mapping of plant protein expression patterns. The method enables the identification of new stress response proteins that may play an important role in future plant breeding programs. Advances in proteomics research are opening up opportunities to explore protein changes in response to different environmental influences in greater detail.
The combination of MALDI-TOF MS and intelligent data analysis may open new horizons in disease diagnostics, the development of precision medicine, food safety, agriculture, and environmental analytics. -
Harvest time evaluation of sweet maize (Zea mays L. convar. saccharata Koern) hybrids based on dry matter and sucrose yield dynamics
53-68Views:145Hungarian sweet maize production, in demand worldwide, averages 500,000 tonnes over several years, thanks to well-chosen precision farming technology. In agriculture, the success of sweet maize production is influenced by many factors, and therefore we are constantly faced with practical challenges. Limited data are available on the dynamics of sugar accumulation in plants, especially under abiotic stress. We investigated a sweet maize hybrid for public cultivation in an experiment set up on the campus of the University of Debrecen, Faculty of Agricultural and Food Sciences and Environmental Management. Quality parameters were determined from grain samples taken at harvest under laboratory conditions at the Agricultural Instrument Centre of the Faculty. In our sweet maize field experiment, dry matter content and sucrose content were measured in grain samples taken at four sampling times. Based on our measurement results, we found that the dry matter gain dynamics of the four examined sweet maize hybrids were different, all with linear increasing trends. Based on our research results, we demonstrated that dry matter and sucrose yields of all four hybrids were most favourable for harvesting at the third sampling time. Compared to the first sampling date, in two weeks, dry matter yield increased by 46% and sucrose content tripled in a tonne of sweet maize grain yield. Thereafter, dry matter and sucrose gains slowed down.
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