Leaf area index (LAI) indicates the leaf area per ground surface area occupied by a crop. Various methods are used to measure LAI, which is unitless and varies according to species and environmental conditions. This experiment was carried out in three different nitrogen ranges (control, 120 kg N ha−1, and 300 kg N ha−1) + PK nutrient levels, with five replications used for leaf area measurement on seven different maize hybrids. Hybrids had different moisture, protein, oil, and starch contents. N (1, 2) + PK treatments had a desirable effect on protein, starch, and yield. P0217 LAI had a minimal response at these fertiliser levels. LAI for Sushi peaked at different dates between control and fertiliser treatments. This result showed that Sushi has an excellent capacity for LAI. LAI values on 15 June 2020 showed minimum average values for all hybrids, and it had a maximum average values on 23 July 2020. LAI had maximum performance between the average values treatments in Sushi, Armagnac, Loupiac, and DKC4792 on 15 June 2020. This study also provides insights for examining variably applied N doses using crop sensors and UAV remote-sensing platforms.
Corn smut fungus (Ustilago maydis [DC.] Corda) is a globally widespread pathogen affecting both forage and sweet maize hybrids, with higher significance in sweet maize. Remote sensing technologies demonstrated favorable results for disease monitoring on the field scale. The study focused on the changes in vegetation index (VI) values influenced by the pathogen. In this study, four hybrids, two forage maize and two sweet maize hybrids were examined. Artificial infection was carried out at three different doses: a low (2500 sporidium number/mL), medium (5000 sporidium number/mL) and high dose (10,000 sporidium number/mL) with a non-infected control plot for each hybrid. The experimental plots were monitored using a multispectral UAV sensor of five monochrome channels on three different dates, i.e., 7, 14 and 21 days after infection. Five different indices (NDVI, GNDVI, ENDVI, LCI, and NDRE) were determined in Quantum GIS 3.20. The obtained results demonstrated that the infection had a significant effect on the VI values in sweet maize hybrids. A high-dose infection in the Dessert R 73 hybrid resulted in significantly lower values compared to the non-infected hybrids in three indices (NDVI, LCI and GNDVI). In the case of the NOA hybrids, GNDVI and ENDVI were able to show significant differences between the values of the infection levels.
Over the last few years, warming of the atmospheric layer near Earth's surface is increasingly experienced and researchers have also established that concentration of numerous greenhouse gases have risen over the past two centuries value. Change is basically a legitimate process - considering atmospheric concentration as well - but the change experienced during the past centuries could not have become this critical without the contribution of human activity. Due to the nature of the greenhouse effect, the result of a very fragile, complex process is experienced currently on Earth, which can be significantly unbalanced even by a slight change. Carbon dioxide emitted from the soil is involved in the global cycle and has an impact on the greenhouse effect. The rise in soil respiration may result in the further intensification of warming. In the scope of the present study, it was examined how carbon dioxide emissions of the soil evolve over a day. The results have been established based on the comparison of the effects of different parts of the day, tillage methods and irrigation.
We managed to create a self-developed sensor system, which is based on the simultaneous reflectance measurements at a 660 and 940 nm wavelength. The ratio of the reflectance refers to the concentration of the soil organic carbon (SOC). This instrument has a calibration range of 1.19 to 6.05 SOC%. The SOC content of twenty-six soil samples was measured by the self-developed system and a standard spectrophotometric method and we found that the SOC estimation in the self-developed system had a good approximation and the differences ranged from –27.72% ~ + 6.99%. We found a strong correlation between the data of the reference measurements (R<sup>2</sup> = 0.73) and the values indicated by our self-developed sensor system (Reference (SOX%) =1.4857 × E (SOC%) – 0.7393). This measurement system is easy to use and displays and records the data in real time. This allows one to map an agricultural production area based on the SOC concentration using its built-in GPS unit.
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