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Phenotyping robots have the potential to obtain crop phenotypic traits on a large scale with high throughput. Autonomous navigation technology for phenotyping robots can significantly improve the efficiency of phenotypic traits collection. This study developed an autonomous navigation method utilizing an RGB‐D camera, specifically designed for phenotyping robots in field environments. The PP‐LiteSeg semantic segmentation model was employed due to its real‐time and accurate segmentation capabilities, enabling the distinction of crop areas in images captured by the RGB‐D camera. Navigation feature points were extracted from these segmented areas, with their three‐dimensional coordinates determined from pixel and depth information, facilitating the computation of angle deviation (α) and lateral deviation (d). Fuzzy controllers were designed with α and d as inputs for real‐time deviation correction during the walking of phenotyping robot. Additionally, the method includes end‐of‐row recognition and row spacing calculation, based on both visible and depth data, enabling automatic turning and row transition. The experimental results showed that the adopted PP‐LiteSeg semantic segmentation model had a testing accuracy of 95.379% and a mean intersection over union of 90.615%. The robot's navigation demonstrated an average walking deviation of 1.33 cm, with a maximum of 3.82 cm. Additionally, the average error in row spacing measurement was 2.71 cm, while the success rate of row transition at the end of the row was 100%. These findings indicate that the proposed method provides effective support for the autonomous operation of phenotyping robots. [ABSTRACT FROM AUTHOR]

• Morphological characteristics and metabolite profiles of hemp exhibit distinct responses to varying light intensities. • Amino acids, soluble protein, carbohydrates, and plant growth influenced by light intensity. • Desired metabolites contents can be achieved by changing light intensity. • Optimum light intensity could be valuable in the pharmaceutical and nutritional field. Industrial hemp (Cannabis sativa L.) is a high-yielding cash crop with strong stress resistance, and its fibrous and metabolic profile holds significant development potential. In this study, we measured various agronomic traits, amino acids, saccharides, and their derivatives under four different light intensities (30 μmol·m−2·s−1, 80 μmol·m−2·s−1, 130 μmol·m−2·s−1, 180 μmol·m−2·s−1). Results showed that plant height decreased with increasing light intensity and reached a minimum at 180 μmol·m−2·s−1, which was 27 % less than the control treatment. Further, stem diameter increased by 52 %, leaf fresh weight by 130 %, stem fresh weight by 116 %, and root fresh weight by 264 % were at their maximum at 130 μmol·m−2·s−1. Plant dry weight of leaf (113 %), stem (214 %), and root (427 %) were also found to be maximum at 130 μmol·m−2·s−1. Metabolic results revealed that the free amino acid content was 10 %, 61 %, and 60 % higher under 30 μmol·m−2·s−1 compared to 80, 130, and 180 μmol·m−2·s−1. Additionally, soluble protein contents were 42 %, 81 %, and 77 % higher under 30 μmol·m−2·s−1 compared to 80, 130, and 180 μmol·m−2·s−1. In contrast, the soluble sugar contents were 42 %, 61 %, and 75 % higher than in the other three treatments. In conclusion, industrial hemp morphological characteristics and metabolite profiles exhibit distinct responses to different light intensities. To achieve desired outcomes, it is imperative to carefully choose an optimized light intensity specifically tailored to align with the defined objectives. These findings provide a foundation for a deeper understanding of the characteristics of industrial hemp, optimizing cultivation techniques, and investigating the impact of light intensity on industrial hemp vegetative period growth, amino acids, and saccharides. [ABSTRACT FROM AUTHOR]