Advances in the Genetic Basis and Molecular Mechanism of Lesion Mimic Formation in Rice

Phenotypic Characteristics and Occurrence Basis of Leaf Necrotic Spots in Response of Weedy Rice to Imazethapyr

Weedy rice is the most challenging weed species to remove in rice production. We found a novel phenotype of seedling leaves which rapidly generates necrotic spots in response to imidazolinone herbicides in weedy rice, but its influencing factors and formation basis are still unknown. In this study, we used the leaf necrotic spot-producing type of weedy rice as the material. First, leaf necrotic spots were defined as physiological and vacuole-mediated cell necrosis by microscopic examination. The imazethapyr concentration was positively correlated with the degree of necrotic spots occurring, while the action site was in accordance with necrosis using herbicide stability tests combined with fluorescence parameters. Furthermore, transcriptome analysis revealed significant differences in the gene expression of endoplasmic reticulum stress and the lipid metabolism membrane structure damage pathway during necrosis, as confirmed by transmission electron microscopy. The light-temperature test also showed that high temperature and intense light could promote the appearance of necrotic spots. These experimental results are helpful in clarifying the process and basis of imazethapyr in inducing the rapid generation of necrotic spots in rice leaves and providing new insight into understanding the mechanism of response to imidazolinone herbicides and the control of weedy rice.

High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

Protecting crop yields is the most important aspect of agricultural production, and one of the important measures in preserving yields is the control of crop pests and diseases; therefore, the identification of crop pests and diseases is of irreplaceable importance. In recent years, with the maturity of computer vision technology, more possibilities have been provided for implementing plant disease detection. However, although deep learning methods are widely used in various computer vision tasks, there are still limitations and obstacles in practical applications. Traditional deep learning-based algorithms have some drawbacks in this research area: (1) Recognition accuracy and computational speed cannot be combined. (2) Different pest and disease features interfere with each other and reduce the accuracy of pest and disease diagnosis. (3) Most of the existing researches focus on the recognition efficiency and ignore the inference efficiency, which limits the practical production application. In this study, an integrated model integrating single-stage and two-stage target detection networks is proposed. The single-stage network is based on the YOLO network, and its internal structure is optimized; the two-stage network is based on the Faster-RCNN, and the target frame size is first clustered using a clustering algorithm in the candidate frame generation stage to improve the detection of small targets. Afterwards, the two models are integrated to perform the inference task. For training, we use transfer learning to improve the model training speed. Finally, among the 37 pests and 8 diseases detected, this model achieves 85.2% mAP, which is much higher than other comparative models. After that, we optimize the model for the poor detection categories and verify the generalization performance on open source datasets. In addition, in order to quickly apply this method to real-world scenarios, we developed an application embedded in this model for the mobile platform and put the model into practical agricultural use.