Automatic stomatal phenotyping of lettuce leaves for plant factory: An improved U-network approach

Abstract

Lettuce, a vegetable rich in nutritional and medicinal value, is commonly analyzed as a modern industrial crop through macroscopic factors such as temperature, humidity, and light, but its microscopic characteristics are still underexplored. At the microscopic scale, stomatal characteristics are the most indicative of lettuce growth status and serve as crucial pathways for plant gas exchange and carbon-water cycle regulation. Therefore, stomatal research is an important area in crop breeding and stress analysis, and stomatal feature detection is a key step in this field. Current traditional methods for stomatal feature measurement are inefficient, imprecise, and labor-intensive. This study proposes a method for stomatal feature extraction of lettuce leaves based on an improved U-net network to improve measurement efficiency and accuracy. To this end, a dual symmetric path structure was designed, incorporating two independent decoding paths to separately extract global contextual information and local detail features, effectively integrating multi-scale information during the decoding phase via feature concatenation and convolutional fusion modules. To mitigate edge information loss caused by repeated down-sampling in the U-Net network, a hybrid dilated convolution module was incorporated into the encoding phase, with overlapping pooling replacing standard pooling to enhance the network's precision in recognizing small objects. Furthermore, the network incorporates a CBAM attention mechanism module to strengthen its capacity for extracting effective features. To optimize network performance, the NAdam optimization function was employed to speed up convergence and minimize computational resource consumption. The MFe (Measurement Feature) visualization and interaction system developed using OpenCV enables precise measurement of stomatal major and minor axes, area, and density for lettuce leaves. Experimental results indicate that the improved U-Net network achieved enhancements of 3.31 %, 6.55 %, and 4.08 % in IoU, PA, and MPA metrics, respectively. This confirms the effectiveness of the network modifications, offering a valuable reference for microscopic studies of plant stomata.

Plant phenotyping relevance

レタス葉の気孔を画像から自動抽出し、面積・軸長・密度を測定する改良U-Net手法と測定システムを開発・評価しており、植物フェノタイピング手法が研究の中心である。

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