Non-destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning.

BACKGROUND: This paper proposes a novel method to improve accuracy and efficiency in detecting the quality of blueberry fruit, taking advantage of deep learning in classification tasks. We first collected 'Tifblue' blueberries at seven different stages of maturity (10-70 days after full bloom) and measured the pigments of the blueberry skin and the total sugar and the total acid of the pulp. We then established a skin pigment contents prediction network (SPCPN), based on the correlation between the pigments and blueberry pictures, and also a fruit intrinsic qualities prediction network (FIQPN), based on the correlation between the pigments and fruit qualities. Finally, the SPCPN and FIQPN were consolidated into the blueberry quality parameters prediction network (BQPPN). RESULTS: The results showed that the anthocyanins in the blueberry skin were significantly correlated with the total sugar, total acid, and sugar / acid ratio of the fruit. After verification, the results also indicated that, for the prediction of anthocyanins, chlorophyll, and the anthocyanin / chlorophyll ratio, the SPCPN network model was found to achieve higher R2 (RMSE) values of 0.969 (0.139), 0.955 (0.005), 0.967 (15.4), respectively. The FIQPN network model was also able to evaluate the value of total sugar (R2 = 0.940, RMSE = 4.905), total acid (R2 = 0.930, RMSE = 2.034), and the sugar / acid ratio (R2 = 0.973, RMSE = 0.580). CONCLUSION: The above results indicated the potential for utilizing deep learning technology to predict the quality indicators of blueberry before harvesting. © 2020 Society of Chemical Industry.

Physiological and transcriptome changes induced by exogenous putrescine in anthurium under chilling stress.

BACKGROUND: Chilling stress is the major factor limiting plant productivity and quality in most regions of the world. In the present study, we aimed to evaluate the effects of putrescine (Put) and polyamine inhibitor D-arginine (D-arg) on the chilling tolerance of anthurium (Anthurium andraeanum). RESULTS: Anthurium seedlings were pretreated with five different concentrations of Put solution or D-arg solution. Subsequently, the seedlings were subjected to chilling stress at 6 °C for 3 days, followed by a recovery at 25 °C for 1 day. Relative permeability of the plasma membrane, as well as physiological and morphologic parameters was assessed during the experiments. Additionally, transcriptome sequencing and patterns of differential gene expression related to chilling response were analyzed by qRT-PCR in 1.0 mM Put-treated and untreated anthurium seedlings. Results indicated that the supplementation of exogenous Put decreased the extent of membrane lipid peroxidation and the accumulation of malondialdehyde (MDA), promoted the antioxidant activities and proline content and maintained the morphologic performances compared with the control group. This finding indicated that the application of exogenous Put could effectively decrease the injury and maintain the quality of anthurium under chilling conditions. In contrast, the treatment of D-arg exhibited the opposite effects, which confirmed the effects of Put. CONCLUSIONS: This research provided a possible approach to enhance the chilling tolerance of anthurium and reduce the energy consumption used in anthurium production.