Food production heavily depends on ammonia-containing fertilizers to improve crop yield and profitability. However, ammonia production is challenged by huge energy demands and the release of ~2% of global CO₂. To mitigate this challenge, many research efforts have been made to develop bioprocessing technologies to make biological ammonia. This review presents three different biological approaches that drive the biochemical mechanisms to convert nitrogen gas, bioresources, or waste to bio-ammonia. The use of advanced technologies—enzyme immobilization and microbial bioengineering—enhanced bio-ammonia production. This review also highlighted some challenges and research gaps that require researchers’ attention for bio-ammonia to be industrially pragmatic.

Plants have been recognized as renewable and sustainable sources of proteins. However, plant protein extraction is challenged by the plant’s recalcitrant cell wall. The conventional extraction methods make use of non-reusable strong alkali chemicals in protein-denaturing extraction conditions. In this study, soy protein was extracted using NH₄OH, a weak, recoverable, and reusable alkali. The extraction conditions were optimized using response surface methodology (RSM). A central composite design (CCD) with four independent variables: temperature (25, 40, 55, 70, and 85 °C); NH₄OH concentration (0.5, 1, and 1.5%); extraction time (6, 12, 18, and 24 h) and solvent ratio (1:5, 1:10, 1:15 and 1:20 w/v) were used to study the response variables (protein yield and amine concentration). Amine concentration indicates the extent of protein hydrolysis. The RSM model equation for the independent and response variables was computed and used to create the contour plots. A predicted yield of 64.89% protein and 0.19 mM amine revealed a multiple R-squared value of 0.83 and 0.78, respectively. The optimum conditions to obtain the maximum protein yield (65.66%) with the least amine concentration (0.14 Mm) were obtained with 0.5% NH₄OH concentration, 12 h extraction time, and a 1:10 (w/v) solvent ratio at 52.5 °C. The findings suggest that NH₄OH is suitable to extract soybean protein with little or no impact on protein denaturation.

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R² (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R² (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

Food production heavily depends on ammonia-containing fertilizers to improve crop yield and profitability. However, ammonia production is challenged by huge energy demand, the release of ~2% of global CO2, and political instability. To mitigate this challenge, many research efforts have developed bioprocessing technologies to make biological ammonia. This review presents three different biological approaches that drive the biochemical mechanisms to convert nitrogen gas, bioresources, or waste to biological ammonia. The use of advanced technologies— enzyme immobilization and microbial bioengineering – enhanced bio-ammonia production. This review also highlighted some challenges and research gaps that require researchers’ attention for bio-ammonia to be industrially pragmatic.

This study developed and optimized a sustainable polyvinyl alcohol (PVA)/carnauba wax (CW) film formulation with improved water resistance using a D-optimal mixture–process design. PVA, CW, glycerol (GLY), stearic acid (SA), and polysorbate-20 were evaluated as formulation components, while water was considered a process variable. Twenty-three experimental formulations were generated within the predefined design space, and film water solubility was experimentally evaluated. The resulting model adequately described the relationship between formulation composition and water solubility (R² = 0.887), demonstrating the applicability of statistical mixture design for sustainable polymer formulation. PVA exhibited a positive effect on water solubility, whereas CW reduced solubility through its hydrophobic contribution. Significant interactions between CW–SA and CW–polysorbate-20 further demonstrated the importance of component compatibility in controlling film water resistance. Numerical optimization identified a formulation containing 0.44 PVA, 0.2513 CW, 0.09 GLY, 0.09 SA, and 0.1287 polysorbate-20 with 75 mL of water. Experimental validation yielded a water solubility of 72.21%, compared with the predicted value of 66.72%. The findings demonstrate how mixture-based formulation optimization can support the development of functional sustainable polymer films while reducing reliance on trial-and-error approaches in materials development.