Li Mengcheng, Liao Yitao, Lu Zhifeng, Sun Mai, Lai Hongyu
College of Engineering, Huazhong Agricultural University, Wuhan, China.
College of Resources and Environment, Huazhong Agricultural University, Wuhan, China.
Front Plant Sci. 2023 Jul 18;14:1163700. doi: 10.3389/fpls.2023.1163700. eCollection 2023.
Leaves are important organs for photosynthesis in plants, and the restriction of leaf growth is among the earliest visible effects under abiotic stress such as nutrient deficiency. Rapidly and accurately monitoring plant leaf area is of great importance in understanding plant growth status in modern agricultural production.
In this paper, an image processing-based non-destructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring (rapeseed) leaf area is proposed. A total of 1,080 rapeseed leaf image areas from five nutrient amendment treatments were continuously collected using the automatic leaf acquisition device and the commonly used area measurement methods (manual and stretching methods).
The average error rate of the manual method is 12.12%, the average error rate of the stretching method is 5.63%, and the average error rate of the splint method is 0.65%. The accuracy of the automatic leaf acquisition device was improved by 11.47% and 4.98% compared with the manual and stretching methods, respectively, and had the advantages of speed and automation. Experiments on the effects of the manual method, stretching method, and splinting method on the growth of rapeseed are conducted, and the growth rate of rapeseed leaves under the stretching method treatment is considerably greater than that of the normal treatment rapeseed.
The growth rate of leaves under the splinting method treatment was less than that of the normal rapeseed treatment. The mean intersection over union (mIoU) of the UNet-Attention model reached 90%, and the splint method had higher prediction accuracy with little influence on rapeseed.
叶片是植物进行光合作用的重要器官，在诸如养分缺乏等非生物胁迫下，叶片生长受限是最早可见的影响之一。在现代农业生产中，快速准确地监测植物叶面积对于了解植物生长状况至关重要。
本文提出了一种基于图像处理的无损监测装置，该装置包括图像采集设备和用于获取（油菜）叶面积的图像过程深度学习网络。使用自动叶片采集设备和常用面积测量方法（手动和拉伸方法），连续收集了来自五种养分改良处理的总共1080个油菜叶片图像面积。
手动方法的平均误差率为12.12%，拉伸方法的平均误差率为5.63%，夹板法的平均误差率为0.65%。自动叶片采集设备的准确性分别比手动和拉伸方法提高了11.47%和4.98%，并且具有速度和自动化优势。进行了手动方法、拉伸方法和夹板法对油菜生长影响的实验，拉伸方法处理下油菜叶片的生长速率明显高于正常处理的油菜。
夹板法处理下叶片的生长速率低于正常油菜处理。UNet-Attention模型的平均交并比（mIoU）达到90%，夹板法具有较高的预测准确性，对油菜影响较小。
