High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data.

BACKGROUND: Biomass accumulation as a growth indicator can be significant in achieving high and stable soybean yields. More robust genotypes have a better potential for exploiting available resources such as water or sunlight. Biomass data implemented as a new trait in soybean breeding programs could be beneficial in the selection of varieties that are more competitive against weeds and have better radiation use efficiency. The standard techniques for biomass determination are invasive, inefficient, and restricted to one-time point per plot. Machine learning models (MLMs) based on the multispectral (MS) images were created so as to overcome these issues and provide a non-destructive, fast, and accurate tool for in-season estimation of soybean fresh biomass (FB). The MS photos were taken during two growing seasons of 10 soybean varieties, using six-sensor digital camera mounted on the unmanned aerial vehicle (UAV). For model calibration, canopy cover (CC), plant height (PH), and 31 vegetation index (VI) were extracted from the images and used as predictors in the random forest (RF) and partial least squares regression (PLSR) algorithm. To create a more efficient model, highly correlated VIs were excluded and only the triangular greenness index (TGI) and green chlorophyll index (GCI) remained. RESULTS: More precise results with a lower mean absolute error (MAE) were obtained with RF (MAE = 0.17 kg/m2) compared to the PLSR (MAE = 0.20 kg/m2). High accuracy in the prediction of soybean FB was achieved using only four predictors (CC, PH and two VIs). The selected model was additionally tested in a two-year trial on an independent set of soybean genotypes in drought simulation environments. The results showed that soybean grown under drought conditions accumulated less biomass than the control, which was expected due to the limited resources. CONCLUSION: The research proved that soybean FB could be successfully predicted using UAV photos and MLM. The filtration of highly correlated variables reduced the final number of predictors, improving the efficiency of remote biomass estimation. The additional testing conducted in the independent environment proved that model is capable to distinguish different values of soybean FB as a consequence of drought. Assessed variability in FB indicates the robustness and effectiveness of the proposed model, as a novel tool for the non-destructive estimation of soybean FB.

Boron uptake by the root cortex symplast of tomato and pea plants: evidence for low-boron-induced active transport.

The objective of this research was to test the hypothesis of the existence of an active boron (B) uptake into the cortical cells induced by low B supply. The uptake of B was characterised in two tomato (Lycopersicon esculentum Mill.) genotypes: B-efficient FER and B-inefficient mutant T3238. In addition, pea (Pisum sativum L.) was used as an anatomically appropriate model for obtaining intact root cortex. Time course uptake studies in tomato indicate that the B-inefficient mutant was defective by the absence of an active low-B-induced uptake system in the cortex. Pea roots showed up to 10-fold higher accumulation of B into the cortex symplast at low (0.5 µm) external B supply in comparison to adequate B (10 µm) supply. Also, low-B-induced uptake of B was strongly inhibited by 2,4-dinitrophenol, indicating a metabolic energy-derived active component of B uptake at low external supply. Uptake of B by the cortical cells of tomato and pea plants appears to be a combination of both passive and active components, with a passive component prevailing at higher external B. An active component of B uptake suppressed by either adequate or high B supply might indicate a downregulation of plasma membrane-associated B transporter(s) in root cortical cells.

Uniparental chromosome elimination at mitosis and interphase in wheat and pearl millet crosses involves micronucleus formation, progressive heterochromatinization, and DNA fragmentation.

Complete uniparental chromosome elimination occurs in several interspecific hybrids of plants. We studied the mechanisms underlying selective elimination of the paternal chromosomes during the development of wheat (Triticum aestivum) x pearl millet (Pennisetum glaucum) hybrid embryos. All pearl millet chromosomes were eliminated in a random sequence between 6 and 23 d after pollination. Parental genomes were spatially separated within the hybrid nucleus, and pearl millet chromatin destined for elimination occupied peripheral interphase positions. Structural reorganization of the paternal chromosomes occurred, and mitotic behavior differed between the parental chromosomes. We provide evidence for a novel chromosome elimination pathway that involves the formation of nuclear extrusions during interphase in addition to postmitotically formed micronuclei. The chromatin structure of nuclei and micronuclei is different, and heterochromatinization and DNA fragmentation of micronucleated pearl millet chromatin is the final step during haploidization.