UAS-Based Spectral and Phenological Modeling for Sustainable Mechanization and Nutrient Management in Horticultural Crops

Abstract

Potatoes are an economically important crop in Virginia, USA, where growers must balance planting dates, nitrogen (N) management, and variable crop prices. Early planting exposes crops to low temperatures that limit growth, whereas late planting increases pest pressure and nutrient inefficiency. This study evaluated the effects of planting dates, N rates, and application timing on potato growth, yield, and pest incidence. We also assessed whether soil physicochemical properties could predict the presence of wireworms and plant-parasitic nematodes (PPNs) using complementary on-farm samples collected across Eastern Virginia between March and July 2023. Three planting dates (early-March, late-March, and early-April) were combined with five N rates (0, 146, 180, 213, and 247 kg N·ha−1) under early- and late-application regimes. We collected data on plant emergence, flowering time, soil nitrate, biomass, tuber yield, pest damage, and UAS-derived metrics. Results showed that late-March planting with 180 kg N·ha−1 achieved the highest gross profit while maintaining competitive yields (25.06 Mg·ha−1), representing 24% and 6% improvements over traditional practices, respectively. Early-April planting produced the largest tubers, with a mean tuber weight 19% higher than the other planting dates. The Normalized Difference Red Edge Index (NDRE) was strongly correlated with N content in plant tissue (R2 = 0.81; r ≈ 0.90), and UAS-derived plant area accurately predicted tuber yield 4–6 weeks before harvest (R2 = 0.75). Wireworm damage was significantly higher in early-March plantings due to delayed insecticide application, while soil nitrate concentration and percent H saturation were identified as key predictors of wireworm presence. Although less effectively modeled due to limited sample size, PPN occurrence was influenced by potassium saturation and soil pH. Aligning planting dates and nitrogen applications with crop phenology, using growing degree days (GDD), enhanced nitrogen management, and yield prediction.