Evidence of a trans-kingdom plant disease complex between a fungus and plant-parasitic nematodes

Time series prediction of insect pests in tea gardens

BACKGROUND

Tea-garden pest control is crucial to ensure tea quality. In this context, the time-series prediction of insect pests in tea gardens is very important. Deep-learning-based time-series prediction techniques are advancing rapidly but research into their use in tea-garden pest prediction is limited. The current study investigates the time-series prediction of whitefly populations in the Tea Expo Garden, Jurong City, Jiangsu Province, China, employing three deep-learning algorithms, namely Informer, the Long Short-Term Memory (LSTM) network, and LSTM-Attention.

RESULTS

The comparative analysis of the three deep-learning algorithms revealed optimal results for LSTM-Attention, with an average root mean square error (RMSE) of 2.84 and average mean absolute error (MAE) of 2.52 for 7 days' prediction length, respectively. For a prediction length of 3 days, LSTM achieved the best performance, with an average RMSE of 2.60 and an average MAE of 2.24.

CONCLUSION

These findings suggest that different prediction lengths influence model performance in tea garden pest time series prediction. Deep learning could be applied satisfactorily to predict time series of insect pests in tea gardens based on LSTM-Attention. Thus, this study provides a theoretical basis for the research on the time series of pest and disease infestations in tea plants. © 2024 Society of Chemical Industry.

Review of nematode interactions with hemp (Cannabis sativa)

The many decades during which the cultivation of Cannabis sativa (hemp) was strongly restricted by law resulted in little research on potential pathogenic nematodes of this increasingly important crop. The primary literature was searched for hemp-nematode papers, resulting in citations from 1890 through 2021. Reports were grouped into two categories: (i) nematodes as phytoparasites of hemp, and (ii) hemp and hemp products and extracts for managing nematode pests. Those genera with the most citations as phytoparasites were Meloidogyne (root-knot nematodes, 20 papers), Pratylenchus (lesion nematodes, 7) and Ditylenchus (stem nematodes, 7). Several Meloidogyne spp. were shown to reproduce on hemp and some field damage has been reported. Experiments with Heterodera humuli (hop cyst nematode) were contradictory. Twenty-three papers have been published on the effects of hemp and hemp products on plant-parasitic, animal-parasitic and microbivorous species. The effects of hemp tissue soil incorporation were studied in five papers; laboratory or glasshouse experiments with aqueous or ethanol extracts of hemp leaves accounted for most of the remainder. Many of these treatments had promising results but no evidence was found of large-scale implementation. The primary literature was also searched for chemistry of C. sativa roots. The most abundant chemicals were classified as phytosterols and triterpenoids. Cannabinoid concentration was frequently reported due to the interest in medicinal C. sativa. Literature on the impact of root-associated chemicals on plant parasitic nematodes was also searched; in cases where there were no reports, impacts on free-living or animal parasitic nematodes were discussed.

How Do Pathogens Evolve Novel Virulence Activities?

This article is part of the Top 10 Unanswered Questions in MPMI invited review series.We consider the state of knowledge on pathogen evolution of novel virulence activities, broadly defined as anything that increases pathogen fitness with the consequence of causing disease in either the qualitative or quantitative senses, including adaptation of pathogens to host immunity and physiology, host species, genotypes, or tissues, or the environment. The evolution of novel virulence activities as an adaptive trait is based on the selection exerted by hosts on variants that have been generated de novo or arrived from elsewhere. In addition, the biotic and abiotic environment a pathogen experiences beyond the host may influence pathogen virulence activities. We consider host-pathogen evolution, host range expansion, and external factors that can mediate pathogen evolution. We then discuss the mechanisms by which pathogens generate and recombine the genetic variation that leads to novel virulence activities, including DNA point mutation, transposable element activity, gene duplication and neofunctionalization, and genetic exchange. In summary, if there is an (epi)genetic mechanism that can create variation in the genome, it will be used by pathogens to evolve virulence factors. Our knowledge of virulence evolution has been biased by pathogen evolution in response to major gene resistance, leaving other virulence activities underexplored. Understanding the key driving forces that give rise to novel virulence activities and the integration of evolutionary concepts and methods with mechanistic research on plant-microbe interactions can help inform crop protection.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Verticillium Wilt of Mint in the United States of America

Verticillium wilt, caused by the fungus Verticillium dahliae, is the most important and destructive disease of mint (Mentha spp.) in the United States (U.S.). The disease was first observed in commercial mint fields in the Midwestern U.S. in the 1920s and, by the 1950s, was present in mint producing regions of the U.S. Pacific Northwest. Verticillium wilt continues to be a major limiting factor in commercial peppermint (Mentha x piperita) and Scotch spearmint (Mentha x gracilis) production, two of the most important sources of mint oil in the U.S. The perennial aspect of U.S. mint production, coupled with the soilborne, polyetic nature of V. dahliae, makes controlling Verticillium wilt in mint a challenge. Studies investigating the biology and genetics of the fungus, the molecular mechanisms of virulence and resistance, and the role of soil microbiota in modulating host-pathogen interactions are needed to improve our understanding of Verticillium wilt epidemiology and inform novel disease management strategies. This review will discuss the history and importance of Verticillium wilt in commercial U.S. mint production, as well as provide a format to highlight past and recent research advances in an effort to better understand and manage the disease.

Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases

Disease and pest alert models are able to generate information for agrochemical applications only when needed, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models to be used in disease and pest warning systems as a function of the weather in order to improve the efficiency of chemical control of pests of the coffee tree. Thus, we correlated the infection rates with the weather variables and also calibrated and tested machine learning algorithms to predict the incidence of coffee rust, cercospora, coffee miner, and coffee borer. We used weather and field data obtained from coffee plantations in production in the southern regions of the State of Minas Gerais (SO_MG) and from the region of the Cerrado Mineiro; these crops did not receive phytosanitary treatments. The algorithms calibrated and tested for prediction were (a) Multiple linear regression (RLM); (b) K Neighbors Regressor (KNN); (c) Random Forest Regressor (RFT), and (d) Artificial Neural Networks (MLP). As dependent variables, we considered the monthly rates of coffee rust, cercospora, coffee miner, and coffee tree borer, and the weather elements were considered as independent (predictor) variables. Pearson correlation analyses were performed considering three different time periods, 1-10 d (from 1 to 10 days before the incidence evaluation), 11-20 d, and 21-30 d, and used to evaluate the unit correlations between the weather variables and infection rates of coffee diseases and pests. The models were calibrated in years of high and low yields, because the biannual variation of harvest yield of coffee beans influences the severity of the diseases. The models were compared by the Willmott's 'd', RMSE (root mean square error), and coefficient of determination (R²) indices. The result of the more accurate algorithm was specialized for the SO_MG and Cerrado Mineiro regions using the kriging method. The weather variables that showed significant correlations with coffee rust disease were maximum air temperature, number of days with relative humidity above 80%, and relative humidity. RFT was more accurate in the prediction of coffee rust, cercospora, coffee miner, and coffee borer using weather conditions. In the SO_MG, RFT showed a greater accuracy in the predictions for the Cerrado Mineiro in years of high and low yields and for all diseases. In SO_MG, the RMSE values ranged from 0.227 to 0.853 for high-yield and 0.147 and 0.827 for low-yield coffee in the coffee borer forecasting.