Vang Le

and 5 more

N-trans-cinnamoyltyramine (NTCT) has been identified from an allelopathic Vietnamese rice accession OM 5930. The study employed a rigorous analysis of NTCT’s effects on shoot and root growth across multiple plant species. Notably, barnyardgrass and red sprangletop exhibited significant reductions in shoot and root growth with increasing NTCT concentrations, indicating a dose-dependent response (from 0.024 μM to 24 μM). Weedy rice accessions PI 653426 and PI 653431 also display dose-dependent effects, with notable declines in both shoot and root growth (from 0.25 μM to 15.6 μM and from 0.44 μM to 85.8 μM, respectively). Additionally, NTCT demonstrates potent inhibitory effects on palmer amaranth, timothy, canola, cress, and lettuce, with increasing concentrations leading to substantial reductions in growth across all species (Average from 2.4 μM). Linear regression analysis reveals the ED50 values for NTCT, providing critical insights into the concentration required for 50% growth inhibition in each species. These values range from 0.19 to 166.1 μM for shoots and from 1.74 to 33.07 μM for roots, highlighting the varying sensitivities among the test plant species. The findings underscore NTCT’s efficacy in suppressing the growth of a wide range of weeds, including both grasses and broadleaf species. The compound shows promise for sustainable weed management practices, particularly in addressing herbicide-resistant weeds in diverse ecological settings. By elucidating NTCT’s inhibitory and species-specific responses, this study contributes valuable insights to the development of eco-friendly herbicidal agents for effective weed control

Supria Sarkar

and 3 more

Predicting the composition of soybean seeds while the plants are growing in the field is very important to understand how different genotypes, field condition and environment influence different seed composition parameters. Knowing this information at global scale is even more important to understand the dynamics of food insecurity and the interaction of seed composition with global environmental changes. This study aims to develop a machine learning-based soybean seed composition model from the fusion of PlanetScope, Sentinel and Landsat satellite images. Although satellite images provide global coverage throughout the year, it suffers from coarser spatial resolution. However, PlanetScope provides four-band (i.e., red, green, blue, and near infrared) multispectral imageries at approximately 3m spatial resolution daily. Alternatively, Sentinel-2B and Landsat-8 have coarser spatial resolution (10 - 30m), they provide enriched spectral resolution. Therefore, the objectives of this study are to 1) fuse the PlanetScope image with corresponding Landsat and Sentinel images, 2) evaluate several machine learning algorithms (e.g., partial least squares, support vector machine, random forest, and deep neural network) to predict protein and oil content of soybean seeds from the fused satellite images. Two soybean fields were established in 2020 and 2021 at Bradford, MO to perform the experiment. Corresponding PlanetScope, Sentinel, and Landsat images were downloaded and processed for the entire growth seasons. Current results indicate that deep neural network provide the best performance in predicting both protein and oil content of soybean. Future step is to assess different fusion algorithms and predict seed composition at regional or global scale.

Li Zhang

and 12 more