Weed management has become a critical agricultural practice, as weeds compete with crops for nutrients, host pests and diseases, and cause major economic losses. The invasive weed Ambrosia artemisiifolia (common ragweed) is particularly problematic in Hungary, endangering crop productivity and public health through its fast proliferation and allergenic pollen. This review examines the current challenges and impacts of A. artemisiifolia while exploring sustainable approaches to its management through precision agriculture. Recent advancements in unmanned aerial vehicles (UAVs) equipped with advanced imaging systems, remote sensing, and artificial intelligence, particularly deep learning models such as convolutional neural networks (CNNs) and Support Vector Machines (SVMs), enable accurate detection, mapping, and classification of weed infestations. These technologies facilitate site-specific weed management (SSWM) by optimizing herbicide application, reducing chemical inputs, and minimizing environmental impacts. The results of recent studies demonstrate the high potential of UAV-based monitoring for real-time, data-driven weed management. The review concludes that integrating UAV and AI technologies into weed management offers a sustainable, cost-effective, mitigate the socioeconomic impacts and environmentally responsible solution, emphasizing the need for collaboration between agricultural researchers and technology developers to enhance precision agriculture practices in Hungary.

Research conducted in the Erbil Province, Kurdistan Region, Iraq from 1998 to 2017 aims to determine the frequency and intensity of drought, highlighting its diverse effects on society, encompassing the economy, agriculture, environment, and local community. Indicators, for instance, the Land Surface Temperature (LST) obtained from satellite data (Landsat) and the Vegetation Health Index (VHI), were employed to evaluate the severity of droughts. The outcomes indicated that Erbil encountered severe droughts in 1999, 2000, and 2008, which resulted in a significant decrease in crop outcomes. Additionally, 2008 was marked by escalating drought conditions, as measured by VHI values exceeding 40; as a result, the percentages reached (86.5%, 67.6%, and 53.7%), respectively. Significant relationships were revealed, with a confidence level of 0.9, between VHI and various factors such as precipitation, LST, and crop yield, with corresponding degrees of (-0.612, 0.615, 0.613, and 0.635). The study also disclosed that alterations and decreases in precipitation occur as the growing season progresses, whereas the following years (2000, 2008, and 2012) saw a pronounced decline in yield, failing to meet the lower limit of water demands for crops. Moreover, the most affected locations in Erbil Province were found to be in the central and southwestern parts of the province

Modern agriculture faces increasing pressure to maintain productivity while reducing soil degradation, chemical inputs, and ecological footprint, making biologically based soil-improvement strategies highly relevant. This study examined whether microbial inoculation, combined with conservation tillage practices (loosening and no-tillage), can enhance soil physical quality during pea (Pisum sativum) cultivation in an agroecological market garden in Hungary. A 2 × 2 factorial field experiment was established, testing tillage (loosening vs. no-tillage) and microbial inoculation (with vs. without) in a randomized design with three replications per treatment (12 plots total). A single microbial application was performed prior to planting using a consortium of Rhizobium spp., Ensifer spp., Pseudomonas spp., and Bacillus spp. The research focused on (I) soil penetration resistance, (II) soil moisture dynamics, and (III) infiltration capacity, with most parameters measured before and after planting. Microbial inoculation significantly reduced penetration resistance under both tillage systems and influenced soil moisture behavior, indicating improved soil structure and water retention. Infiltration rate did not change significantly within the study period. Overall, the results demonstrate that microbial amendments can rapidly improve key soil physical properties, offering a practical, nature-based strategy for resilient, low-input farming systems.

Light quantity and spectral quality regulate plant growth and may influence crop–weed interactions by altering plant morphology, biomass allocation, and physiology. This study evaluated the combined effects of photosynthetic photon flux density (PPFD) and far-red (FR) radiation on the morphological, biomass, physiological, and hyperspectral responses of Pisum sativum L. and Datura stramonium L. grown separately under controlled hydroponic conditions. A 2 × 2 × 2 factorial experiment was conducted using two species, two PPFD levels, and two FR treatments. Morphological traits, biomass production, root mass fraction, SPAD chlorophyll content, hyperspectral reflectance (350–2500 nm), and vegetation indices were analyzed using multivariate statistics, principal component analysis, and Random Forest classification. Species identity explained most of the variation in morphology, biomass allocation, and spectral characteristics. FR supplementation increased mean shoot height in D. stramonium by 62.5% under high PPFD and 101.2% under low PPFD, whereas shoot height in P. sativum was not significantly affected. Red wavelengths between 649 and 668 nm provided the strongest discriminatory information, while short-wave infrared wavelengths contributed complementary biochemical information. The Random Forest model achieved an overall accuracy of 75.7%, an AUC of 0.839, and a Cohen’s κ of 0.489. These findings demonstrate that D. stramonium exhibits greater FR-induced morphological plasticity than P. sativum and that hyperspectral sensing provides useful, although not yet field-validated, crop–weed discrimination.