基于RGB图像分析与熵权TOPSIS的田间玉米苗期整齐度评价方法

Uniformity Assessment of Maize Seedlings Based on RGB Imaging and Entropy-weighted TOPSIS

摘要:

玉米是重要的主粮作物之一，其苗期的管理对产量的影响至关重要。准确、快速的玉米苗情监测对于早期植株的补缺、水肥运筹等田间管理工作具有重要意义。传统的苗情监测方法依赖于人工田间调查，存在主观性强、效率低下等问题。利用RGB图像和计算机视觉技术对作物进行大规模、快速和准确的监测，已成为智慧农业的一个重要发展趋势。本研究在幼苗计数与叶龄估算的基础上，提出一种客观的田间作物整齐度自动化评价方法。首先进行图像行检测和缺苗检测，提取株距、行距、叶龄、植株冠层面积、植株外接框面积的变异系数及缺苗率共6项关键整齐度指标；采用熵权法确定各指标权重；并运用TOPSIS多指标综合评价模型计算整齐度综合得分；结合专家经验，将整齐度划分为整齐、比较整齐、不整齐3个等级。结果表明，该评价体系划分的整齐度等级与人工定级结果具有较高一致性，总体分类精度达0.92。在两个独立验证数据集中总体分类精度分别达到0.94和0.96，显示了该方法在不同图像来源和不同试验地点的适应性与泛化能力。本研究为田间作物整齐度的标准化、自动化评价提供了技术支撑。

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.