Islam Md Parvez, Nakano Yuka, Lee Unseok, Tokuda Keinichi, Kochi Nobuo
Agricultural AI Research Promotion Office, RCAIT, National Agriculture and Food Research Organization (NARO), Tsukuba, Japan.
Institute of Vegetable and Flower Research, NARO, Tsukuba, Japan.
Front Plant Sci. 2021 Jul 1;12:630425. doi: 10.3389/fpls.2021.630425. eCollection 2021.
The real challenge for separating leaf pixels from background pixels in thermal images is associated with various factors such as the amount of emitted and reflected thermal radiation from the targeted plant, absorption of reflected radiation by the humidity of the greenhouse, and the outside environment. We proposed TheLNet270v1 (thermal leaf network with 270 layers version 1) to recover the leaf canopy from its background in real time with higher accuracy than previous systems. The proposed network had an accuracy of 91% (mean boundary F1 score or BF score) to distinguish canopy pixels from background pixels and then segment the image into two classes: leaf and background. We evaluated the classification (segment) performance by using more than 13,766 images and obtained 95.75% training and 95.23% validation accuracies without overfitting issues. This research aimed to develop a deep learning technique for the automatic segmentation of thermal images to continuously monitor the canopy surface temperature inside a greenhouse.
在热图像中，将叶片像素与背景像素分离的真正挑战与多种因素相关，例如目标植物发出和反射的热辐射量、温室湿度对反射辐射的吸收以及外部环境。我们提出了TheLNet270v1（270层版本1的热叶网络），以比以前的系统更高的精度实时从背景中恢复叶冠层。所提出的网络在区分冠层像素与背景像素方面具有91%的准确率（平均边界F1分数或BF分数），然后将图像分割为两类：叶片和背景。我们使用超过13,766张图像评估了分类（分割）性能，获得了95.75%的训练准确率和95.23%的验证准确率，且没有过拟合问题。本研究旨在开发一种深度学习技术，用于热图像的自动分割，以持续监测温室内冠层表面温度。
