The leaf area index (LAI) is a biophysical crop parameter of great interest for agronomists and plant breeders. Direct methods for measuring LAI are normally destructive, while indirect methods are either costly or require long pre- and post-processing times. In this study, a novel deep learning-based (DL) model was developed using RGB nadir-view images taken from a high-throughput plant phenotyping platform for LAI estimation of maize. The study took place in a commercial maize breeding trial during two consecutive growing seasons. Ground-truth LAI values were obtained non-destructively using an allometric relationship that was derived to calculate the leaf area of individual leaves from their main leaf dimensions (length and maximum width). Three convolutional neural network (CNN)-based DL model approaches were proposed using RGB images as input. One of the models tested is a classification model trained with a set of RGB images tagged with previously measured LAI values (classes). The second model provides LAI estimates from CNN-based linear regression and the third one uses a combination of RGB images and numerical data as input of the CNN-based model (multi-input model). The results obtained from the three approaches were compared against ground-truth data and LAI estimations from a classic indirect method based on nadir-view image analysis and gap fraction theory. All DL approaches outperformed the classic indirect method. The multi-input_model showed the least error and explained the highest proportion of the observed LAI variance. This work represents a major advance for LAI estimation in maize breeding plots as compared to previous methods, in terms of processing time and equipment costs.

叶面积指数 (LAI) 是农学家和植物育种者非常感兴趣的生物物理作物参数。测量 LAI 的直接方法通常具有破坏性，而间接方法要么成本高昂，要么需要较长的预处理和后处理时间。在这项研究中，使用从高通量植物表型平台获取的 RGB 天底视图图像开发了一种新的基于深度学习 (DL) 的模型，用于玉米 LAI 估计。该研究是在连续两个生长季节的商业玉米育种试验中进行的。Ground-truth LAI 值是使用异速生长关系非破坏性获得的，该异速生长关系是从其主要叶片尺寸（长度和最大宽度）计算单个叶片的叶片面积。使用 RGB 图像作为输入，提出了三种基于卷积神经网络 (CNN) 的 DL 模型方法。测试的模型之一是使用一组 RGB 图像训练的分类模型，这些图像标记有先前测量的 LAI 值（类）。第二个模型提供基于 CNN 的线性回归的 LAI 估计，第三个模型使用 RGB 图像和数值数据的组合作为基于 CNN 的模型（多输入模型）的输入。将三种方法获得的结果与基于天底图像分析和间隙分数理论的经典间接方法的地面实况数据和 LAI 估计值进行比较。所有 DL 方法都优于经典的间接方法。multi-input_model 显示出最小的误差并解释了观察到的 LAI 方差的最高比例。与以前的方法相比，这项工作代表了玉米育种地块 LAI 估计的重大进步，