Neural-Network Surrogate Framework for Rapid LCA Impact Screening of Potato Production: Manual Management vs. Drone-Assisted Technification

Potato cultivation in the Ecuadorian Andes is largely manual and relies on intensive agrochemical inputs. We introduce a reproducible workflow that couples life cycle assessment (LCA) with a neural-network surrogate to enable rapid multi-impact screening of two potato management scenarios in Ecuador: (i) conventional manual management and (ii) Unmanned aerial vehicle (UAV)-based field monitoring to identify hotspots for targeted ground-based input application. Multi-category impacts are computed in OpenLCA using the environmental footprint method (EF 3.0) per kilogram of potatoes and scaled to annual national totals using reported national production data. UAV operation is parameterized as 0.51 kg CO2 eq·h−1, equivalent to 0.225 kg CO2 eq·ha−1 at a coverage rate of 2.27 ha·h−1. For 2024, the UAV-informed scenario reduces climate change from 4.29 × 107 to 3.75 × 107 kg CO2 eq (−12.7%), resource use, fossils from 5.09 × 108 to 4.54 × 108 MJ (−10.7%), and freshwater eutrophication from 3.33 × 104 to 2.83 × 104 kg P eq (−15.0%), while land use remains nearly unchanged at ~4.73 × 109 Pt (−0.1%). To avoid repeated LCA recalculations, a multi-output artificial neural network (ANN) surrogate (29 outputs) was trained in Python (TensorFlow/Keras) and evaluated using leave-one-year-out (LOYO) cross-validation (2015–2024), showing strong agreement with the LCA results. This framework enables scalable what-if analysis and efficient evaluation of UAV-enabled precision monitoring strategies in resource-constrained settings.