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Crop mapping at an earlier time within the growing season benefits agricultural management. However, crop spectral information is very limited at the early crop phenological stages, leading to difficulties for within-season crop identification. In this study, we proposed a deep learning-based fusion method for crop mapping within the growing season, which first learned a priori information (i.e., pre-season crop types) from historical crop planting data and then integrated the a priori information with the satellite-derived crop types estimated from spectral times-series data. We expect that preseason crop types provided by crop rotation patterns is an effective supplement to spectral information to generate reliable crop maps in the early growing season. We tested the proposed fusion method at three representative sites in the U.S. with different crop rotation intensities and one site with cloudy weather conditions in the Sichuan Province of China. The experimental results showed that the fusion method incorporated the strengths of pre-season crop type estimates and the spectral-based crop type estimates and thus achieved higher crop classification accuracy than the two estimates throughout the growing season. We found that pre-season crop estimates had a higher accuracy in the scenarios with either nearly continuous planting or half-time planting of the same crop. In addition, the historical crop type data strongly affected the performance of pre-season crop estimates, suggesting that high-quality historical crop planting data are particularly important for crop identification at earlier times in the growing season. Our study highlighted the great potential for near real-time crop mapping through the fusion of spectral information and crop rotation patterns. [ABSTRACT FROM AUTHOR]

Bacterial leaf blight is a devastating disease of rice worldwide. The resistant genes are routinely transferred from landraces to cultivated varieties through backcross breeding along with marker-assisted selection. In the present study, we use the gene-specific markers to screen the rice landraces in Yunnan Province of China. We collected 404 representative samples of 24 different rice landraces from Yunnan Province of China. The initial PCR-based screening suggested that the leaf blight resistance was not evenly distributed in Yunnan Province. Our results indicate that there is a complete loss of resistance for landraces based on xa5 and xa13 genes. On the other hand, landraces harboring Xa7 and Xa21 showed a high level of resistance. Using gene-specific PCR-based data, we were able to identify the resistant, susceptible and heterozygous populations across Yunnan Province. The widely used Xa21 gene alone showed a remarkable level of resistance throughout the province, indicating its potential to develop broad-spectrum resistance in rice germplasm. The key aspects of bacterial blight spread according to local sites in Yunnan Province and the resistance conferred by different landraces due to the presence of different resistance genes are discussed. [ABSTRACT FROM AUTHOR]

The rice blast disease (caused by Magnaporthe oryzae) is a devastating disease in China. Understanding the molecular mechanisms of interaction for the cognate avirulence (AVR) gene with host resistance (R) genes, as well as their genetic evolution is essential for sustainable rice production. In the present study, we conducted a high-throughput nucleotide sequence polymorphism analysis of the AVR-Pi9 gene that was amplified from the rice-growing regions of the Yunnan Province in China. We detected the presence of seven novel haplotypes from 326 rice samples. In addition, the sequences of AVR-Pi9 were also obtained from two non-rice hosts, Eleusine coracana and Eleusine indica. The sequence analysis revealed the insertions and deletions in the coding and non-coding regions of the gene. The pathogenicity experiments of these haplotypes on previously characterized monogenic lines showed that the newly identified haplotypes are virulent in nature. The breakdown of resistance was attributed to the development of new haplotypes. Our results suggest that the mutation in the AVR-Pi9 gene is an alarming situation in the Yunnan province and thus needs attention. [ABSTRACT FROM AUTHOR]