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The ‘Huangguan’ pear disease spot detection and grading is the key to fruit processing automation. Due to the variety of individual shapes and disease spot types of ‘Huangguan’ pear. The traditional computer vision technology and pattern recognition methods have some limitations in the detection of ‘Huangguan’ pear diseases. In recent years, with the development of deep learning technology and convolutional neural network provides a new solution for the fast and accurate detection of ‘Huangguan’ pear diseases. To achieve automatic grading of ‘Huangguan’ pear appearance quality in a complex context, this study proposes an integrated framework combining instance segmentation, semantic segmentation and grading models. In the first stage, Mask R-CNN and Mask R-CNN with the introduction of the preprocessing module are used to segment ‘Huangguan’ pears from complex backgrounds. In the second stage, DeepLabV3+, UNet and PSPNet are used to segment the ‘Huangguan’ pear spots to get the spots, and the ratio of the spot pixel area to the ‘Huangguan’ pear pixel area is calculated and classified into three grades. In the third stage, the grades of ‘Huangguan’ pear are obtained using ResNet50, VGG16 and MobileNetV3. The experimental results show that the model proposed in this paper can segment the ‘Huangguan’ pear and disease spots in complex background in steps, and complete the grading of ‘Huangguan’ pear fruit disease severity. According to the experimental results. The Mask R-CNN that introduced the CLAHE preprocessing module in the first-stage instance segmentation model is the most accurate. The resulting pixel accuracy (PA) is 97.38% and the Dice coefficient is 68.08%. DeepLabV3+ is the most accurate in the second-stage semantic segmentation model. The pixel accuracy is 94.03% and the Dice coefficient is 67.25%. ResNet50 is the most accurate among the third-stage classification models. The average precision (AP) was 97.41% and the F1 (harmonic average assessment) was 95.43%.In short, it not only provides a new framework for the detection and identification of ‘Huangguan’ pear fruit diseases in complex backgrounds, but also lays a theoretical foundation for the assessment and grading of ‘Huangguan’ pear diseases.

Plants must reprogram gene expression to adapt constantly changing environmental temperatures. With the increased occurrence of extremely low temperatures, the negative effects on plants, especially on growth and development, from cold stress are becoming more and more serious. In this research, strand-specific RNA sequencing (ssRNA-seq) was used to explore the dynamic changes in the transcriptome landscape of Arabidopsis thaliana exposed to cold temperatures (4°C) at different times. In total, 7,623 differentially expressed genes (DEGs) exhibited dynamic temporal changes during the cold treatments. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that the DEGs were enriched in cold response, secondary metabolic processes, photosynthesis, glucosinolate biosynthesis, and plant hormone signal transduction pathways. Meanwhile, long non-coding RNAs (lncRNAs) were identified after the assembly of the transcripts, from which 247 differentially expressed lncRNAs (DElncRNAs) and their potential target genes were predicted. 3,621 differentially alternatively spliced (DAS) genes related to RNA splicing and spliceosome were identified, indicating enhanced transcriptome complexity due to the alternative splicing (AS) in the cold. In addition, 739 cold-regulated transcription factors (TFs) belonging to 52 gene families were identified as well. This research analyzed the dynamic changes of the transcriptome landscape in response to cold stress, which reveals more complete transcriptional patterns during short- and long-term cold treatment and provides new insights into functional studies of that how plants are affected by cold stress.