Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea.
Department of Agriculture, Forestry and Bioresources, Seoul National University, Seoul 08826, Korea.
Sensors (Basel). 2022 Oct 12;22(20):7728. doi: 10.3390/s22207728.
Crop fresh weight and leaf area are considered non-destructive growth factors due to their direct relation to vegetative growth and carbon assimilation. Several methods to measure these parameters have been introduced; however, measuring these parameters using the existing methods can be difficult. Therefore, a non-destructive measurement method with high versatility is essential. The objective of this study was to establish a non-destructive monitoring system for estimating the fresh weight and leaf area of trellised crops. The data were collected from a greenhouse with sweet peppers ( var. ); the target growth factors were the crop fresh weight and leaf area. The crop fresh weight was estimated based on the total system weight and volumetric water content using a simple formula. The leaf area was estimated using top-view images of the crops and a convolutional neural network (ConvNet). The estimated crop fresh weight and leaf area exhibited average R values of 0.70 and 0.95, respectively. The simple calculation was able to avoid overfitting with fewer limitations compared with the previous study. ConvNet was able to analyze raw images and evaluate the leaf area without additional sensors and features. As the simple calculation and ConvNet could adequately estimate the target growth factors, the monitoring system can be used for data collection in practice owing to its versatility. Therefore, the proposed monitoring system can be widely applied for diverse data analyses.
鲜重和叶面积被认为是无损生长因子，因为它们与营养生长和碳同化直接相关。已经提出了几种测量这些参数的方法；然而，使用现有方法测量这些参数可能很困难。因此，需要一种具有高通用性的无损测量方法。本研究的目的是建立一个用于估计蔓生作物鲜重和叶面积的无损监测系统。数据是从一个带有甜椒（var.）的温室中收集的；目标生长因子是作物鲜重和叶面积。作物鲜重根据总系统重量和体积含水量使用简单公式估算。叶面积使用作物的顶视图图像和卷积神经网络（ConvNet）估算。估计的作物鲜重和叶面积的平均 R 值分别为 0.70 和 0.95。与之前的研究相比，简单的计算可以避免过度拟合，限制更少。ConvNet 可以分析原始图像并评估叶面积，而无需额外的传感器和特征。由于简单的计算和 ConvNet 可以充分估计目标生长因子，因此该监测系统可以因其通用性而在实际中用于数据收集。因此，所提出的监测系统可以广泛应用于各种数据分析。
