Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning - Li, Mengcheng; Liao, Yitao; Lu, Zhifeng; Sun, Mai; Lai, Hongyu (Frontiers in Plant Science, 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 to understand plant growth status in modern agricultural production. In this paper, an image processingbased nondestructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring Brassica napus (rapeseed) leaf area is proposed, A total of 1080 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 method). The average error rate of manual method is 12.12%, the average error rate of stretching method is 5.63% and the average error rate of splint method is 0.65% . The accuracy of the automatic leaf acquisition device was improved by 11.47% and 4.98% compared with manual and stretching method, 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 mIoU (mean intersection over union) of U-Net-Attention model reached 90%, and the splint method had higher prediction accuracy with little influence on rapeseed.