Seedling-lump integrated non-destructive monitoring for automatic transplanting with Intel RealSense depth camera

Abstract

Non-destructive plant growth parameters measurement is an important concern in automatic-seedling transplanting. Recently, several image-based monitoring approaches have been proposed and potentially developed for several agricultural applications. The presented study proposed and developed a RealSense-based machine vision system for the close-shot seedling-lump integrated monitoring. The strategy was based on the close-shot depth information. Further, the point cloud clustering and suitable algorithms were applied to obtain the segmentation of 3D seedling models. In addition, the data processing pipeline was developed to assess the different morphological parameter of 4 different seedling varieties. The experiments were carried out with 4 different seedling varieties (pepper, tomato, cucumber, and lettuce) and trained under different light conditions (light and dark). Moreover, analysis results showed that there was not significantly different (p < 0.05) found towards light and dark environments due to close-shot near-infrared detection. However, the results revealed that the stem diameter relationship between RealSense and the manual method was found for R2 = 0.68 cucumber, R2 = 0.54 tomato, R2 = 0.35 pepper, and R2 = 0.58 lettuce seedlings. Whereas, the seedling height relationship between RealSense and the manual method was found higher than R2 = 0.99, 0.99, 0.99, and 0.99 for pepper, tomato, cucumber, and lettuce, respectively. Based on the experiment results, it was concluded that the RGB-D integrated monitoring system with the purposed method could be practiced for nursery seedlings most promisingly without high labour requirements in terms of ease of use. The system revealed a good sturdiness and relevance for plant growth monitoring. Additionally, it has the perspective for future practical value to real-time vision servo operations for transplanting robots.