Mobile Sensing and Life-Cycle-Assessment-Based Quantitative Model for Synergistic Pesticide–Carbon Reduction and Income Growth in Mulberry Orchard Protection: A Pilot Study

Addressing the dual challenges of green agricultural transformation and the national carbon neutrality targets, the traditional pest control systems in the mulberry plantations of Nantong, Jiangsu Province, face concurrent problems, including excessive pesticide use, high direct carbon emissions, and low economic returns. This study establishes a comprehensive evaluation framework integrating technical, environmental, and economic dimensions. Utilizing a lightweight mobile sensing system, this research enables the early identification of white powdery mildew on mulberry trees and facilitates precise spatial pesticide management. Unlike traditional life cycle assessment (LCA) studies that rely on static data, this case study uses real-time field monitoring data as dynamic input to drive the standardized life cycle assessment model. In this pilot-scale validation (n = 3 pairs, one growing season), the proposed model reduced pesticide usage by an average of 28.7% (±3.1%), achieved a carbon emission reduction of 23.1 (±2.7) g/m², and increased net income by 0.199 (±0.018) yuan/m². Precision pest control driven by mobile sensing effectively enhances the synergy between ecological and economic benefits in specialty crop systems. Consequently, the study proposes a data-driven framework that shows promise for pesticide–carbon–income synergy, pending further validation across more sites and seasons. © 2026 by the authors.