Department of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea.
Integrated Major in Global Smart Farm, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea.
Sensors (Basel). 2022 Jul 23;22(15):5499. doi: 10.3390/s22155499.
Growth indices can quantify crop productivity and establish optimal environmental, nutritional, and irrigation control strategies. A convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera. Data from an online autonomous greenhouse challenge (Wageningen University, June 2021) were employed in this study. The data were collected using an Intel RealSense D415 camera. The developed model has a two-stage CNN architecture based on ResNet50V2 layers. The developed model provided coefficients of determination from 0.88 to 0.95, with normalized root mean square errors of 6.09%, 6.30%, 7.65%, 7.92%, and 5.62% for fresh weight, dry weight, height, diameter, and leaf area, respectively, on unknown lettuce images. Using red, green, blue (RGB) and depth data employed in the CNN improved the determination accuracy for all five lettuce growth indices due to the ability of the stereo camera to extract height information on lettuce. The average time for processing each lettuce image using the developed CNN model run on a Jetson SUB mini-PC with a Jetson Xavier NX was 0.83 s, indicating the potential for the model in fast real-time sensing of lettuce growth indices.
生长指数可用于量化作物生产力，并制定最佳的环境、营养和灌溉控制策略。本文提出了一种基于卷积神经网络（CNN）的模型，可利用立体相机获取的红、绿、蓝和深度（RGB-D）数据来估算温室生菜四个品种的各种生长指数（即鲜重、干重、高度、叶面积和直径）。本研究使用了来自在线自主温室挑战赛（瓦赫宁根大学，2021 年 6 月）的数据。数据由英特尔 RealSense D415 相机采集。所开发的模型采用基于 ResNet50V2 层的两级 CNN 架构。该模型在未知生菜图像上的鲜重、干重、高度、直径和叶面积的决定系数分别为 0.88 到 0.95，归一化均方根误差分别为 6.09%、6.30%、7.65%、7.92%和 5.62%。由于立体相机能够提取生菜的高度信息，因此在 CNN 中使用红、绿、蓝（RGB）和深度数据可以提高所有五个生菜生长指数的确定精度。使用 Jetson SUB mini-PC 上运行的开发的 CNN 模型处理每个生菜图像的平均时间为 0.83 秒，表明该模型在快速实时感知生菜生长指数方面具有潜力。
