Research on Coal and Gangue Recognition Based on the Improved YOLOv7-Tiny Target Detection Algorithm.

The recognition technology of coal and gangue is one of the key technologies of intelligent mine construction. Aiming at the problems of the low accuracy of coal and gangue recognition models and the difficult recognition of small-target coal and gangue caused by low-illumination and high-dust environments in the coal mine working face, a coal and gangue recognition model based on the improved YOLOv7-tiny target detection algorithm is proposed. This paper proposes three model improvement methods. The coordinate attention mechanism is introduced to improve the feature expression ability of the model. The contextual transformer module is added after the spatial pyramid pooling structure to improve the feature extraction ability of the model. Based on the idea of the weighted bidirectional feature pyramid, the four branch modules in the high-efficiency layer aggregation network are weighted and cascaded to improve the recognition ability of the model for useful features. The experimental results show that the average precision mean of the improved YOLOv7-tiny model is 97.54%, and the FPS is 24.73 f·s-1. Compared with the Faster-RCNN, YOLOv3, YOLOv4, YOLOv4-VGG, YOLOv5s, YOLOv7, and YOLOv7-tiny models, the improved YOLOv7-tiny model has the highest recognition rate and the fastest recognition speed. Finally, the improved YOLOv7-tiny model is verified by field tests in coal mines, which provides an effective technical means for the accurate identification of coal and gangue.

Coronatine-Induced Maize Defense against Gibberella Stalk Rot by Activating Antioxidants and Phytohormone Signaling.

One of the most destructive diseases, Gibberella stalk rot (GSR), caused by Fusarium graminearum, reduces maize yields significantly. An induced resistance response is a potent and cost-effective plant defense against pathogen attack. The functional counterpart of JAs, coronatine (COR), has attracted a lot of interest recently due to its ability to control plant growth and stimulate secondary metabolism. Although several studies have focused on COR as a plant immune elicitor to improve plant resistance to pathogens, the effectiveness and underlying mechanisms of the suppressive ability against COR to F. graminearum in maize have been limited. We investigated the potential physiological and molecular mechanisms of COR in modulating maize resistance to F. graminearum. COR treatment strongly enhanced disease resistance and promoted stomatal closure with H2O2 accumulation, and 10 µg/mL was confirmed as the best concentration. COR treatment increased defense-related enzyme activity and decreased the malondialdehyde content with enhanced antioxidant enzyme activity. To identify candidate resistance genes and gain insight into the molecular mechanism of GSR resistance associated with COR, we integrated transcriptomic and metabolomic data to systemically explore the defense mechanisms of COR, and multiple hub genes were pinpointed using weighted gene correlation network analysis (WGCNA). We discovered 6 significant modules containing 10 candidate genes: WRKY transcription factor (LOC100279570), calcium-binding protein (LOC100382070), NBR1-like protein (LOC100275089), amino acid permease (LOC100382244), glutathione S-transferase (LOC541830), HXXXD-type acyl-transferase (LOC100191608), prolin-rich extensin-like receptor protein kinase (LOC100501564), AP2-like ethylene-responsive transcription factor (LOC100384380), basic leucine zipper (LOC100275351), and glycosyltransferase (LOC606486), which are highly correlated with the jasmonic acid-ethylene signaling pathway and antioxidants. In addition, a core set of metabolites, including alpha-linolenic acid metabolism and flavonoids biosynthesis linked to the hub genes, were identified. Taken together, our research revealed differentially expressed key genes and metabolites, as well as co-expression networks, associated with COR treatment of maize stems after F. graminearum infection. In addition, COR-treated maize had higher JA (JA-Ile and Me-JA) levels. We postulated that COR plays a positive role in maize resistance to F. graminearum by regulating antioxidant levels and the JA signaling pathway, and the flavonoid biosynthesis pathway is also involved in the resistance response against GSR.

Image feature extraction and recognition model construction of coal and gangue based on image processing technology.

Using image recognition technology to realize coal gangue recognition is one of the development directions of intelligent fully mechanized caving mining. Aiming at the problem of low accuracy of coal gangue recognition in fully mechanized caving mining, the extraction method of Coal and gangue images features is proposed, and the corresponding coal gangue recognition model is constructed. The illuminance value is an important factor affecting the imaging quality. Therefore, a multi-light source image acquisition system is designed, and the optimal illuminance value suitable for coal and gangue images acquisition is determined to be 17,130 Lux. There is a large amount of image noise in the gray-sc5ale image, so Gaussian filtering is used to eliminate the noise in the gray-scale image of coal and gangue. Then, six gray-scale features and four texture features are extracted from 900 coal and gangue images respectively. It is concluded that the three kinds of features of gray skewness, gray variance and texture contrast have the highest discrimination on coal and gangue images. Least squares vector machine has a strong ability to classify, so the use of least squares vector machine to achieve coal gangue identification, and build coal gangue identification model. The results show that the recognition accuracy of the model for coal gangue images is 92.2% and 91.5%, respectively, with gray skewness and texture contrast as indicators. This study provides a reliable theoretical support for solving the problem of low recognition rate of coal gangue in fully mechanized caving mining.