Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013–2018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018–2019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E₁ = 0.716 for the model from NDVI and for SAVI E₁ = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41–71) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.

The main aim of our research was to develop a methodology of chlorophyll content in the leaves of apple trees non-invasive assessment in apple orchards and its adaptation to Early Gold and Golden Reinders based on spectral characteristics of chlorophyll content in the canopy. In each measurement period, 30 samples were collected from each of the two apple cultivars studied. For spectral data collection of leaf samples, an AvaSpec 2048 spectrometer was used in the wavelength range 400–1000 nm in three replicates. Principal component analysis (PCA) with varimax rotation was used to identify the wavelength with the highest factor weight to identify the chlorophyll-sensitive wavelength. The models were calibrated with 2/3 of the values in the database and validated with the remaining 1/3. The simple linear regression method generated the model for estimating chlorophyll. The coefficient of determination (R²) was used to compare the strength of the regression models, and the Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) functions were used to measure the accuracy of the estimator models. These metrics help to quickly assess how reliable and accurate a model’s predictions are. Nine indices were obtained based on the precision values, and CHL_apple1 performed best (R² = 0.633, RMSE = 298.28 µg/g, NRMSE = 9.61%, NSE = 0.60, MBE = 84.59, and MAE = 243.39).

The active biomass of cultivated plants and average yield decreases as a result of biotic and abiotic stress effect. The extent of the reduction can be quantified on the basis of remotely sensed data. The aim of this research is to evaluate the suitability of Landsat 8 data for a wheat yield estimation. We processed Landsat 8 recordings for the period 2013–2019 and generated NDVI data. Time series NDVI data were calibrated and validated with observed wheat yield averages. The agricultural plots around Karcag, Hungary, were our research area. The relation between Landsat NDVI data and yield was strongest and highest in the total biomass period ( R ² = 0.53–0.54) and the estimation error based on RMSE is between 0.48–0.7 t.ha ⁻¹ .

Mycotoxin contamination of cereal crops poses escalating food safety risks across the Central and Balkan European (CBE) corridor under climate change, yet no PRISMA 2020-compliant synthesis of remote sensing (RS) and machine learning (ML) evidence for this region exists. We conducted an AI-augmented systematic review applying a four-stage automated pipeline—PICO domain scoring, SBERT semantic deduplication, and Thompson-sampling reinforcement learning—to 36,038 corpus records (2010–2025), yielding 156 included studies (inter-rater κ = 0.81 (95% CI: 0.74–0.88)). Logistic growth modelling identified a 56-fold corpus expansion with inflection at t₀ = 2024.8 (R² = 0.981). Satellite multispectral imaging dominated the literature (91.7% of studies); random forest and gradient boosting models achieved R² = 0.74–0.80 for aflatoxin B1 and deoxynivalenol prediction in CBE maize and wheat when integrating vegetation indices, land surface temperature, and precipitation covariates. Deep learning surpassed classical ML in annual study count from 2021, reaching ~60% relative share by 2025, though the performance advantage narrows at field scale relative to laboratory hyperspectral benchmarks (98–99% accuracy). A five-percentage-point CBE–global performance gap is largely consistent with differences in sample size and multi-toxin design scope rather than algorithmic access. The country × mycotoxin gap matrix identifies zero eligible studies for four CBE nations and for T-2/HT-2 toxins across the Balkan states. Climate-driven satellite mycotoxin prediction emerges as the field’s active research frontier.

Composting represents an efficient technology that enables the effective utilization of by-products and waste materials. Moreover, it proves to be highly suitable for processing raw materials and converting them into fertilizers that would not be recommended for direct application without undergoing pre-processing. This is particularly crucial in the case of poultry manure, which possesses potentially hazardous properties and necessitates pre-treatment. One increasingly prevalent form of compost is known as compost tea, which involves the immersion of compost in water. In this experiment, compost tea or compost solution were created using a product called composted and pelletized poultry litter (CPPL). Four compost:water ratio (CWR) (1/2.5, 1/5, 1/10, 1/20) were applied, along with three different extraction durations (24, 48, and 72 hours) and three distinct extraction temperatures (20 °C, 35 °C, and 50 °C). Since the 1/10 and 1/20 ratios were found to be the best for subsequent applicability and spreadability, their content parameters were measured further. After elimination of the experiment, the most important nutrients (nitrogen content (nitrate and ammonium), phosphorus and potassium) were determined. The results showed that the nutrient content was highest for all four parameters at the extraction temperature of 35 °C. For example, while at 20 and 50°C the NO3 – content ranged from 263 to 768 mg/l and from 210 to 534 mg/l, at 35 °C it ranged from 498.33 to 2636.67 mg/l, irrespective of the mixing ratio and extraction time. If the extraction temperature is not taken into account, the nutrient content increased with the increase of the extraction time, so that the highest values were measured at 72 hours extraction time obviously. The data measured in the present experiment will serve as a basis for subsequent experiments with different indicator plants, investigating the effect of compost when applied as a solution.

Nowadays, the development of irrigation is increasingly recognized as a necessary factor in agriculture, primarily because of global warming. Depending on the field conditions, the most commonly used method is sprinkler irrigation. The spray uniformity of sprinklers installed on the field irrigation equipment can be characterized by the Christiansen-uniformity coefficient (CUc%) and the distribution uniformity coefficient (DU%). Our investigations were carried out on the lateral moving irrigation equipment of University of Debrecen, Institutes for Agricultural Research and Educational Farm and Nyírbátor’s company in Hungary in 2019. Variable rate irrigation (VRI) is used in Nyírbátor. In contrast, the VRI has given positive results, making an irrigation equipment with the VRI a safer and more uniform method than a conventional linear irrigation equipment.