Conservation tillage is a crucial method for the sustainable development of agricultural arable land and has been adopted worldwide. The quantity of straw mulch is determined not just by its presence but by its amount, serving as a key indicator for detecting straw return to the field. In this study, aerial remote sensing data from the spring and autumn seasons were captured using a UAV equipped with a multi-spectral camera, while the corn straw mulch quantity was measured simultaneously. Spectral reflectance was first extracted, and spectral indices were constructed from the remote sensing data. The correlation coefficient method was then used to identify the band variables and spectral variables sensitive to the straw mulch quantity, which served as model input variables. Subsequently, machine learning algorithms such as support vector machine (SVM), random forest (RF), BP neural network (BPNN), and extreme learning machine (ELM) were employed to establish the inversion model for straw mulch quantity. The accuracy of these models was compared across different time periods and study areas. To address the significant impact of model parameters on predictive performance, genetic algorithm (GA) and particle swarm optimization (PSO) were introduced, culminating in the proposed genetic-particle swarm optimization hybrid algorithm (GA-PSO). This hybrid approach leveraged their complementary strengths to enhance model performance and complete the estimation of straw coverage in the region. The results indicated that the RF algorithm optimized by GA-PSO achieved the best inversion effect for corn straw mulch quantity, with an R² value of 0.74. Comparative analysis of different data sets consistently reflected the straw mulch quantity in the region accurately. The accuracy of estimating the corn straw mulch quantity in the field reached 91.36%, demonstrating that result estimation can be effectively achieved through model optimization. This study provides a scientific reference for detecting straw return in conservation tillage and offers a reliable model inversion method for estimating straw mulch quantity in other crops.