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Achieving accurate and efficient target deposition of pesticide droplets is the principal factor in minimizing environmental risk. For hydrophobic surfaces, adding tank-mix adjuvants containing surfactants to modulate interfacial behavior is warranted, which lacks common laws to guide practical applications directly. Machine learning is developing rapidly and makes many data-based decisions in various industrial processes. Hence, according to machine learning-based analysis of fundamental physical quantities, proposing quantitative sustainability metrics to improve interface behavior is essential. Comparing the interfacial behavior of five adjuvants, the common denominator is that droplets in the Wenzel state with higher adhesion tension and lower contact angles can generate the pinning force that causes energy dissipation, reduces pesticide losses, and weakens environmental pollution. Simultaneously, the interfacial behavior of pesticide droplets including adjuvants on citrus leaves is verified, while the phytotoxicity experiment under high temperature and the laboratory bioassay are carried out. The results show that the eco-friendly alkyl polyglycoside (APG) as the glycosidic surfactant has nontarget biosafety and better mite control, which can be exploited as a commercial tank-mix adjuvant for promotion. This study provides a new insight into guiding adjuvants added to pesticides on account of quantitative sustainability metrics, which has important implications for food safety and agricultural green development.

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Glyphosate, one of the most widely used herbicides, plays an important role in controlling weeds and ensuring crop production. While using glyphosate, adjuvants are commonly added to improve its deposition on weeds and control efficacy. However, changes in weed leaf surface characteristics may reduce glyphosate penetration and contribute to evolved glyphosate resistance. Therefore, it is significant to introduce an improved method for regularizing leaf surface characterization and guide adjuvant selection to improve glyphosate efficacy. In this work, surface characteristics of typical weed leaves have been systematically investigated by 3D surface analysis and scanning electron microscopy, finally quantified by apparent surface free energy (ASFE) due to its comprehensive and quantitative evaluation of leaf surfaces. Moreover, the relationship between the weed leaf surface characteristics and the retention of glyphosate on weeds was established, further related to the control efficacy against weeds. To maximize the utilization rate of glyphosate, the types and concentrations of adjuvants should be regulated according to the ASFE of weeds. Our findings not only regularize the surface properties of weed leaves but also reveal their influencing mechanism on the deposition and biological activity of glyphosate, which provide effective guidance for the use of glyphosate.

No abstract