CONTEXT: Precision weeding, a sector of agricultural technology in which drones and/or automated weeders use chemical, mechanical, or thermal means to eradicate weeds, has moved from academic research settings to commercialization. Because of labor shortage pressures, the push to gain competitive advantages, and the environmental impacts of excessive chemical inputs, many California growers have been interested in adopting precision weeding technologies in their operations. OBJECTIVE: Using semi-structured qualitative interviews, this study investigated the viewpoints of three key stakeholder groups involved in the diffusion and adoption of precision weeding technologies: California growers, precision weeding startups, and agricultural technology venture capital firms. With the supplemental viewpoints of large agricultural firms and their corporate venture capital arms and government agencies, this study seeks to understand the compatible motivations between stakeholders, current collaborative models between stakeholders and their limitations, and the user journey for growers adopting precision weeding technology. METHODS: We conducted 17 semi-structured qualitative interviews with diverse stakeholders in the precision weeding sector to gather textual data and gain high data saturation. Data collection balanced rigorous criteria for participant selection with adaptive interview questions to ensure depth and relevance. The analysis procedure involved coding and thematic framework development, complemented by grounded theory for iterative data examination and stakeholder map creation for distilling cross-organizational insights and stakeholder dynamics. RESULTS AND CONCLUSIONS: The results indicated that compatible motivations include addressing current labor issues, reducing costs, and the potential of precision weeding to transform agriculture. Less cited but still popular motivations included having more weeding options, meeting specific field needs, and environmental sustainability. The individual and stakeholder group cognitive maps demonstrated that concerns such as startup longevity, the high expenses of precision weeding machinery, and some startups lacking a direct connection to growers commonly limit the growers' adoption of precision weeding technologies. SIGNIFICANCE: The procedure and cognitive mapping presented in this study can be applied to other emerging agtech technologies and ecosystems.

Trace amounts of Cu (II), Pb (II), and Cd (II) in a wastewater sample were preconcentrated with a novel cross-linked magnetic chitosan modified with a new synthesised methionine-glutaraldehyde Schiff's base (MG-Chi/Fe₃ O₄ ) as a dispersive solid-phase extraction (DSPE) adsorbent. The adsorbed metal ions were then eluted with a specific volume of suitable solution and determined by flame atomic absorption spectrometry (FAAS). Various parameters affecting the extraction efficiency of the metal ions were investigated and optimised, including pH, amount of adsorbent, extraction time, type and volume rate of eluent, elution time, sample volume, and effect of interfering ions. The adsorption kinetics are more consistent with the pseudo-second order model. The results were statistically interpreted and the analytical performance of the proposed method was found to have preconcentration factors of 55, 60, and 50 μg L^-1 for Cu(II), Pb(II), and Cd(II), respectively, limits of detection were 0.22, 0.24, and 0.10 μg L^-1 for Cu(II), Pb(II), and Cd(II), respectively, with a relative standard deviation (1.5%-2.8 %), and the liner range was 5-1000 for Cu(II) and Pb(II) and 2.5-1000 for Cd(II). It was concluded that this method was suitable for successful simultaneous determination of Cu(II), Pb(II), and Cd(II) in industrial wastewater samples.

Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200 mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesian method. The rankings of both methods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner.