Low-Cost Lettuce Height Measurement Based on Depth Vision and Lightweight Instance Segmentation Model

Yiqiu Zhao, Xiaodong Zhang, Jingjing Sun, Tingting Yu, Zongyao Cai, Zhi Zhang and Hanping Mao ()
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Yiqiu Zhao: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xiaodong Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Jingjing Sun: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Tingting Yu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zongyao Cai: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zhi Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Hanping Mao: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2024, vol. 14, issue 9, 1-19

Abstract: Plant height is a crucial indicator of crop growth. Rapid measurement of crop height facilitates the implementation and management of planting strategies, ensuring optimal crop production quality and yield. This paper presents a low-cost method for the rapid measurement of multiple lettuce heights, developed using an improved YOLOv8n-seg model and the stacking characteristics of planes in depth images. First, we designed a lightweight instance segmentation model based on YOLOv8n-seg by enhancing the model architecture and reconstructing the channel dimension distribution. This model was trained on a small-sample dataset augmented through random transformations. Secondly, we proposed a method to detect and segment the horizontal plane. This method leverages the stacking characteristics of the plane, as identified in the depth image histogram from an overhead perspective, allowing for the identification of planes parallel to the camera’s imaging plane. Subsequently, we evaluated the distance between each plane and the centers of the lettuce contours to select the cultivation substrate plane as the reference for lettuce bottom height. Finally, the height of multiple lettuce plants was determined by calculating the height difference between the top and bottom of each plant. The experimental results demonstrated that the improved model achieved a 25.56% increase in processing speed, along with a 2.4% enhancement in mean average precision compared to the original YOLOv8n-seg model. The average accuracy of the plant height measurement algorithm reached 94.339% in hydroponics and 91.22% in pot cultivation scenarios, with absolute errors of 7.39 mm and 9.23 mm, similar to the sensor’s depth direction error. With images downsampled by a factor of 1/8, the highest processing speed recorded was 6.99 frames per second (fps), enabling the system to process an average of 174 lettuce targets per second. The experimental results confirmed that the proposed method exhibits promising accuracy, efficiency, and robustness.

Keywords: RGB-D; plant height; machine vision; lightweight network; YOLOv8 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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