The detection methods of yellow dragon disease spread via wood lice transmission networks like social networks are very important for diverse citrus trees and farmers. Although current methods have some detection accuracy or low cost, the detection processes are relatively troublesome and the detection cycles are long, making it be a difficult problem to apply them in large-scale orange farms as practical scenarios to detect citrus yellow dragon disease in a timely manner. A new method toward detecting citrus yellow dragon disease spread utilizing Spark and deep learning is proposed for this problem. By obtaining citrus field video stream data through high-definition cameras, and transferring the stream data to the Spark cluster through Kafka like intelligent agents, it is practicable to use the structured streaming component under the Spark framework via big data ecosphere to process video or image stream data transmitted via monitoring. We construct a citrus yellow dragon disease detection model via YOLOv7, and use self-made citrus yellow dragon disease images as training and testing data sets. The preliminary experimental results show that the new methods achieved an accuracy of 83.14%. To reduce the occurrence of missed and false detections, the shallow detection heads are added to the feature fusion networks for improvement, extracting and fusing shallow network information to try to improve the detection effects of yellow dragon disease. Then replace the convolution operation in the ELAN (Effective Long-range Aggregation Network) module with deep separable convolution to reduce the number of model parameters. The preliminary experimental results show that compared to the original YOLOv7 model, our improved citrus yellow dragon disease detection model with YOLOv7 has an accuracy improvement of 2.43%, maintaining a higher detection accuracy with lower time than before.
Flexible bio-based polyurethane foams with different content of palm oil-based polyol were prepared. Various characterization techniques including Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), dynamic mechanical analysis (DMA), mechanical testing, and thermogravimetric analysis (TGA) were used to investigate the effects of bio-polyol dosage on microstructure, mechanical properties and thermal properties of the polyurethane foams. There are carbonyl groups in palm oil-based polyol and they can form hydrogen bonds with amino groups. The hydrogen bonds formed between -O- in soft segments and -NH- in hard segments can reduce the degree of microphase separation and the hydrogen bonding index increased from 66.90 to 89.21%, which is consistent to FTIR analysis The DMA results showed that the glass transition temperature ( T g) of the bio-based polyurethane increased with the increase of the content of bio-polyol. Regular cell structure was observed with different content of bio-polyol. When the content of bio-polyol increased from 0 to 45%, the cell size became smaller and decreased from 14.37 to 4.68 mm. The compressive strength of the bio-based polyurethane foams increased. The swelling ratio of the foams decreased from 417.55 to 219.96% as the bio-polyol content increased from 0 to 45%. The hydrogen bonds could provide physical crosslinking points, and improve the strength of the foam.
