Measurement of chlorophyll fluorescence of Epipremnum aureum by Chlorophyll Fluorometer.

Measurement of chlorophyll fluorescence of Epipremnum aureum by Chlorophyll Fluorometer.

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Epipremnum aureum (pothos) is an herbaceous species and is originated to tropical or subtropical South East Asia and Solomon islands. This study investigated the effect of excessive moisture on chlorophyll content and photosynthesis efficiency on E. aureum. The chlorophyll a, b, and total (chlorophyll a + b) contents were measured by using spectrop...

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... As such, insufficient soil moisture has been noted to substantially correlate with non-photochemical quenching and steady-state fluorescence in light-adapted leaves of crops [35]. The inability of crops to regulate sufficient water in their leaves leads to stomatal closure, subsequently limiting the photosynthetic potential of crops [36][37][38]. ...
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A timely irrigation schedule for small-scale farms is imperative for ensuring optimum crop production in the wake of drought and climate change. Owing to the large number of irrigated small-scale farms that grow different crops across all seasons in the Mutale River catchment, this study sought to develop irrigation scheduling for these crops for sustainable water utilization without compromising crop yields. Unmanned aerial vehicle (UAV) images were utilized as the base from which crop water content patterns were derived. A total of four (4) spectral vegetation indices, viz, the Greenness Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Optimized Soil-Adjusted Vegetation Index (OSAVI), were generated to characterize crop types and water content in this study. Crop water content data, in the form of the relative water content (RWC), were measured in the field for each type of crop. Crop water content was modelled based on the empirical relationships between spectral indices and field-measured RWC. The linear regression analysis revealed a significant association between the GNDVI and the water content of sweet potato, maize, sugar beans, and Florida broadleaf mustard, with r2 values of 0.948, 0.995, 0.978, and 0.953, respectively. The NDVI revealed a strong association with the water content of Solanum retroflexum, pepper, and cabbage, with r2 values of 0.949, 0.956, and 0.995, respectively. The NDRE, on the other hand, revealed a strong relationship with water content in peas and green beans, with r2 values of 0.961 and 0.974, respectively. The crop water content patterns simulation revealed that Solanum retroflexum, sweet potato, maize, sugar beans, and Florida broadleaf mustard reached their respective wilting points on day four after irrigation, implying that irrigation of these crops should be scheduled after every four (4) days. Peas, green beans, pepper, and cabbage reached their respective wilting points on day five after irrigation, implying that irrigation of these crops should be scheduled after every five days. The results of this study highlight the significance of considering crop water content derived from spectral bands of UAV imagery in scheduling irrigation for various types of crops. This study also emphasized the on-going significance of remote sensing technology in addressing agricultural issues that impede hunger alleviation and food security goals.