Bioscience Evidence 2026, Vol.16, No.4, 221-234 http://bioscipublisher.com/index.php/be 230 factor productivity of nitrogen fertilizer. Environmental assessment further showed that the optimized fertilization strategy reduced several environmental impacts, including: (1) reactive nitrogen losses; (2) greenhouse gas emissions; (3) soil acidification risk; and (4) the risk of water eutrophication. 6 Future Directions for Orchard Management and Peach Quality Improvement 6.1 Precision orchard management The main goal of precision orchard management is to replace experience-based decisions with real-time, non-destructive, and spatial monitoring. In-field fruit quality assessment can directly support irrigation, fertilization, thinning, and harvest decisions (Figure 4). This concept has now developed into a combination of different technologies, including portable devices, wearable sensors, non-contact sensing platforms, flexible robots, and multi-source data integration systems. However, field reliability and cost-effectiveness are still the main barriers to large-scale commercial use (Wang et al., 2026). Figure 4 Conceptual framework for precision peach orchard management In peach orchards and other stone fruit orchards, mobile sensing platforms can already estimate fruit number, fruit size, and fruit color with good accuracy and generate spatial distribution maps. These data can support thinning, pruning, spraying, and harvest planning (Islam et al., 2022). The same systems can also predict fruit size, quality, and yield at an early stage, helping connect orchard management with postharvest logistics, yield forecasting, and fruit grading. For harvest maturity, prototype electronic nose systems have been able to monitor changes in volatile organic compounds before harvest and identify fruit maturity with relatively low error. This suggests that future harvest decisions may shift from experience-based picking to sensor-based harvesting (Voss et al., 2020). Impedance sensors and multi-electrode array sensors have also shown strong relationships between the electrical properties of fruit tissues and internal quality traits. Correlation coefficients can exceed 0.85, and prediction R² values can be higher than 0.80, indicating that internal fruit quality may also be evaluated directly in the orchard (Huang et al., 2025). Future precision management will increasingly focus on monitoring the entire orchard rather than individual trees. Multi-drone imaging combined with deep learning can already detect and count fruits in complex orchard environments in real time, improving both management efficiency and data accuracy. Sensors and data analysis
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