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"Candidatus Liberibacter solanacearum" (Lso), transmitted by the potato psyllid (Bactericera cockerelli), is the causal agent of potato zebra chip, but can also infect other solanaceous plants, including peppers. Studies were conducted to investigate whether Lso could be transmitted to the next generation of plants through seeds from infected pepper plants. In 2014, jalapeno pepper plants were infested with psyllids carrying a mixture of Lso A and B (AB) at the AgriLife Research Station at Bushland. The study was again conducted in 2019 and pepper plants were infested with psyllids carrying Lso B or Lso AB. In each of the studies, noninfested plants served as controls. At harvest, fruits were collected and tested for the presence of Lso using quantitative PCR. Seeds from infected fruits were then tested for Lso. Overall, the percentage of seeds that tested positive for Lso ranged from 33% to 70%. However, Lso detection in embryos ranged only from 0% to 8%. Seed samples from Lso‐positive fruits were planted in the greenhouse to determine the impact of Lso on emergence and the incidence of Lso in emerged plants. Although plant emergence differed between some of the seeds obtained from Lso‐positive and ‐negative fruits, the overall impact of Lso on plant emergence was not consistent. However, of the 182 plants that emerged from seeds collected from infected fruits, none was positive for Lso, suggesting that seeds are unlikely to serve as sources for new Lso infections and their impact on disease epidemiology is negligible. [ABSTRACT FROM AUTHOR]

Abstract Background Plasmodiophorids and chytrids are zoosporic parasites of algae and land plant and are distributed worldwide. There are 35 species belonging to the order Plasmodiophorales and three species, Polymyxa betae, P. graminis, and Spongospora subterranea, are plant viral vectors. Plasmodiophorid transmitted viruses are positive strand RNA viruses belonging to five genera. Beet necrotic yellow vein virus (BNYVV) and its vector, P. betae, are the causal agents for rhizomania. Results Evidence of BNYVV replication and movement proteins associating with P. betae resting spores was initially obtained using immunofluorescence labeling and well characterized antisera to each of the BNYVV proteins. Root cross sections were further examined using immunogold labeling and electron microscopy. BNYVV proteins translated from each of the four genomic and subgenomic RNAs accumulate inside P. betae resting spores and zoospores. Statistical analysis was used to determine if immunolabelling detected viral proteins in specific subcellular domains and at a level greater than in control samples. Conclusion Virus-like particles were detected in zoosporangia. Association of BNYVV replication and movement proteins with sporangial and sporogenic stages of P. betae suggest that BNYVV resides inside its vector during more than one life cycle stage. These data suggest that P. betae might be a host as well as a vector for BNYVV

Knowledge of crop abiotic and biotic stress is important for optimal irrigation management. While spectral reflectance and infrared thermometry provide a means to quantify crop stress remotely, these measurements can be cumbersome. Computer vision offers an inexpensive way to remotely detect crop stress independent of vegetation cover. This paper presents a technique using computer vision to detect disease stress in wheat. Digital images of differentially stressed wheat were segmented into soil and vegetation pixels using expectation maximization (EM). In the first season, the algorithm to segment vegetation from soil and distinguish between healthy and stressed wheat was developed and tested using digital images taken in the field and later processed on a desktop computer. In the second season, a wireless camera with near real-time computer vision capabilities was tested in conjunction with the conventional camera and desktop computer. For wheat irrigated at different levels and inoculated with wheat streak mosaic virus (WSMV), vegetation hue determined by the EM algorithm showed significant effects from irrigation level and infection. Unstressed wheat had a higher hue (118.32) than stressed wheat (111.34). In the second season, the hue and cover measured by the wireless computer vision sensor showed significant effects from infection (p = 0.0014), as did the conventional camera (p < 0.0001). Vegetation hue obtained through a wireless computer vision system in this study is a viable option for determining biotic crop stress in irrigation scheduling. Such a low-cost system could be suitable for use in the field in automated irrigation scheduling applications. [ABSTRACT FROM AUTHOR]

Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverage, complete instead of sub-sampled assessments and greater cost-effectiveness over ground-based methods. It is critical for locating, early mapping and controlling small infestations before they reach economically prohibitive or ecologically significant levels over larger land areas. This study was designed to explore the ability of hyperspectral imagery for mapping infestation of musk thistle (Carduus nutans) on a native grassland during the preflowering stage in mid-April and during the peak flowering stage in mid-June using the support vector machine classifier and to assess and compare the resulting mapping accuracy for these two distinctive phenological stages. Accuracy assessment revealed that the overall accuracies were 79% and 91% for the classified images at preflowering and peak flowering stages, respectively. These results indicate that repeated detection of the infestation extent, as well as infestation severity or intensity, of this noxious weed in a spatial and temporal context is possible using hyperspectral remote sensing imagery [ABSTRACT FROM AUTHOR]