Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning.

Abstract

Introduction 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. Method 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 Brassica napus (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). Results 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. Discussion 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.

Plant phenotyping relevance

画像取得装置と深層学習によってナタネの葉面積を非破壊・自動推定する手法を開発し、既存法との誤差比較および精度評価を行っており、植物表現型取得が研究の中心である。

Code and data availability

The supplied blocks describe a rapeseed leaf-area phenotyping study (1,080 leaf images, U-Net-Attention segmentation), but contain no data availability statement, no public dataset, image, code, or model deposit, and no authors' public URL. The only URL present is the article DOI itself, which is the paper, not a paper

No evidence-backed public reproduction asset is currently recorded.