Wheat maturity identification based on improved RT-DETR model

Abstract

As a key global cereal crop, wheat undergoes distinct growth phases and developmental stages that are critical 
for informing agricultural management strategies. Accurate assessment of wheat maturity, a crucial determinant 
of yield potential, is vital for optimizing harvest timing, ensuring consistent grain quality, and maximizing economic returns in production systems. Traditional maturity evaluation methods, which primarily rely on manual field inspections and subjective visual scoring, have inherent limitations, such as these being labor-intensive and time-consuming, requiring expert knowledge, exhibiting inconsistent inter-observer reproducibility, and lacking scalability for large-scale monitoring. Recent advances in artificial intelligence (AI) have revolutionized agricultural monitoring by addressing these constraints. Deep learning-based computer vision techniques now enable automated maturity assessment frameworks, utilizing algorithmic pattern recognition of spectral and morphological features in crop imagery. These systems outperform human experts in terms of efficiency—processing thousands of images per hour—and accuracy, with error margins of less than 2% in controlled trials. Moreover, these AI-driven systems facilitate data-driven decision-making for precision irrigation, nutrient management, and yield forecasting. Their integration with unmanned aerial vehicles and internet of things-enabled edge devices allows for real-time, field-deployable solutions that minimize operational costs and resource inputs. This technological convergence is fostering the development of precision agriculture ecosystems, where AI-based maturity analytics drive sustainable intensification practices, ranging from genotype selection to post-harvest logistics, thereby advancing global food security initiatives.