Bacterial blight (BB) of rice caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of the most serious bacterial diseases that hinder the normal growth and production of rice, which greatly reduces the quality and yield of rice. The effect of traditional methods such as chemical control is often not ideal. A series of production practices have shown that among the numerous methods for BB controlling, breeding and using resistant varieties are the most economical, effective, and environmentally friendly, and the important basis for BB resistance breeding is the exploration of resistance genes and their functional research. So far, 44 rice BB resistance genes have been identified and confirmed by international registration or reported in journals, of which 15 have been successfully cloned and characterized. In this paper, research progress in recent years is reviewed mainly on the identification, map-based cloning, molecular resistance mechanism, and application in rice breeding of these BB resistance genes, and the future influence and direction of the remained research for rice BB resistance breeding are also prospected.

Hyperspectral imaging technique combined with machine learning is a powerful tool for the evaluation of disease phenotype in rice disease-resistant breeding. However, the current studies are almost carried out in the lab environment, which is difficult to apply to the field environment. In this paper, we used visible/near-infrared hyperspectral images to analysis the severity of rice bacterial blight (BB) and proposed a novel disease index construction strategy (NDSCI) for field application. A designed long short-term memory network with attention mechanism could evaluate the BB severity robustly, and the attention block could filter important wavelengths. Best results were obtained based on the fusion of important wavelengths and color features with an accuracy of 0.94. Then, NSDCI was constructed based on the important wavelength and color feature related to BB severity. The correlation coefficient of NDSCI extended to the field data reached -0.84, showing good scalability. This work overcomes the limitations of environmental conditions and sheds new light on the rapid measurement of phenotype in disease-resistant breeding.