Abstract:Maize is one of the most important staple crops. Early-stage management during the seedling period is crucial for its yield formation. Accurate and rapid monitoring of maize seedling growth is essential for early-stage interventions such as replanting and water-fertilizer management. Traditional seedling monitoring methods rely heavily on manual field surveys, which are often inefficient and subject to strong observer bias. Leveraging RGB imagery and computer vision techniques to perform large-scale, rapid, and accurate crop monitoring has become a key trend in smart agriculture. An automated method for evaluating field crop uniformity was proposed based on seedling counting and leaf age estimation results from RGB images. The method firstly performed image-based row detection and missing seedling detection to extract six indicators: seedling missing rate, plant spacing, row spacing, leaf age, missing plant rate, plant bounding box area, and plant coverage. The seedling missing rate and the coefficient of variation (CV) of the other five variables form the six key indicators for seedling uniformity assessment. The entropy weight method was employed to determine the weight of each indicator, and the TOPSIS multi-criteria decision-making model was used to calculate the overall uniformity score. Based on expert knowledge, the uniformity was then classified into three discrete levels. Validation results showed that the classification results of the proposed evaluation system were highly consistent with expert grading, achieving an overall classification accuracy (OA) of 0.92. The method also achieved high accuracy (OA was 0.94 and 0.96) in two independent datasets, demonstrating its adaptability and generalizability. The research result can provide a technical foundation for the standardized and automated evaluation of crop uniformity in the field.
