Aroma is an important quality trait of grapes and often the focus of consumers, viticulturists and grapevine breeders. Kyoho is a hybrid between Vitis vinifera and Vitis labrusca with a strawberry-like scent, while 87-1 is an early-ripening mutant of Muscat hamburg, belonging to Vitis vinifera, with a rose scent. In this study, we compared their aroma compositions and concentrations during berry development by headspace-SPME combined with gas chromatography-mass spectrometry (GC-MS), and analyzed the expression differences of enzyme-encoding genes in the LOX-HPL, MEP and MVA metabolic pathways by qRT-PCR. Twelve esters were detected in Kyoho during the whole berry development and they were abundant after veraison, but no esters were detected in 87-1 berries. Linalool was the dominant terpene among the 14 terpenes detected in 87-1 berries, while limited amounts of terpenes were detected in Kyoho berries. qRT-PCR analysis indicated that the low expression of VvAAT might explain the low content of ester volatiles in 87-1 berries, and the low expression of coding genes in the MEP pathway, especially VvPNLinNer1, might be the reason for the low content of volatile terpenes in Kyoho berries. The results from this work will promote our understanding of aroma metabolic mechanisms of grapes, and offer some suggestions for grape aromatic quality improvement.

The accurate and efficient identification of pear varieties is paramount to the intelligent advancement of the pear industry. This study introduces a novel approach to classifying pear varieties by recognizing their leaves. We collected leaf images of 33 pear varieties against natural backgrounds, including 5 main cultivation species and inter-species selection varieties. Images were collected at different times of the day to cover changes in natural lighting and ensure model robustness. From these, a representative dataset containing 17,656 pear leaf images was self-made. YOLOv10 based on the PyTorch framework was applied to train the leaf dataset, and construct a pear leaf identification and classification model. The efficacy of the YOLOv10 method was validated by assessing important metrics such as precision, recall, F1-score, and mAP value, which yielded results of 99.6%, 99.4%, 0.99, and 99.5%, respectively. Among them, the precision rate of nine varieties reached 100%. Compared with existing recognition networks and target detection algorithms such as YOLOv7, ResNet50, VGG16, and Swin Transformer, YOLOv10 performs the best in pear leaf recognition in natural scenes. To address the issue of low recognition precision in Yuluxiang, the Spatial and Channel reconstruction Convolution (SCConv) module is introduced on the basis of YOLOv10 to improve the model. The result shows that the model precision can reach 99.71%, and Yuluxiang’s recognition and classification precision increased from 96.4% to 98.3%. Consequently, the model established in this study can realize automatic recognition and detection of pear varieties, and has room for improvement, providing a reference for the conservation, utilization, and classification research of pear resources, as well as for the identification of other varietal identification of other crops.

Rape is one of the most widely cultivated and highest-yielding fruit crops in the world. However, research on its precise nutrient diagnosis and fertilization theory is severely lacking, significantly restricting the development of the grape industry. In this study, an L₁₆(4⁵) orthogonal experimental design was applied to determine the effects of varying ratios of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) on the fruit quality of ‘87-1’ grape (Vitis vinifera L.) cultivated in a facility, aiming to optimize nutrient application rates and improve fruit quality. Among the treatments T5 (N2P1K2Ca3Mg4), T14 (N4P2K3Ca1Mg4), and T9 (N3P1K3Ca4Mg2), treatment T9 had the most significant effect on single fruit weight, total soluble solids (TSS) content, fruit firmness (FF), and fruit quality index (FQI) and was conducive to the positive accumulation of the above quality indicators. Based on a comprehensive multi-factor analysis of variance, the optimal fertilization combination for achieving a high FQI was N3P1K2Ca1Mg2, corresponding to application rates of 375.0, 0, 168.8, 0, and 70.5 kg·hm⁻² for N, P₂O₅, K₂O, CaO, and MgO, respectively. Furthermore, to establish standards for multivariate compositional nutrient diagnosis (CND) and define the nutrient sufficiency range for ‘87-1’ grape fruit cultivated in a facility, the nutrient concentrations in various plant tissues and the soil and the FQI were measured across 80 treatments over five consecutive years. The nutritional status of the grapes cultivated under these treatments was calculated using the Technique for Order Preference by Similarity to Ideal Solution and the CND method. Based on the optimal nutrient ranges for high FQI sub-populations, a precise fertilization model was developed to facilitate economic fertilizer savings, quality improvement, and standardized grape cultivation in a facility.