Disease Grading Criterion and Assessment of Yield Loss Caused by Maize Rough Dwarf Disease

[Objective] The paper was to study the disease grading criterion and assess the yield loss caused by maize rough dwarf disease. [Method] The ear lengths and yields of each healthy and infected plant of 5 cultivars were measured during 2009 and 2010. The severity grading criterion was deduced according to the ear length ratios. [Result]When the ratios were 0.92-1.00, 0.67-0.91, 0.41-0.66, 0.10-0.40 and 0, its corresponding disease grading criterions were 0, 1, 3, 5 and 7, respectively. The severity grading criterion was closely correlated to the yield loss. By analyzing the data of disease indexes and yield loss rates of 27 cultivars with DPS （Data Processing System）, the regression equations were established respectively. According to the comparison with each other, the Weibull Model was proved to have the highest fitting degree. Validating with the disease indexes of 27 cultivars in 2010, the equation supported the feasibility of the equation to predict the yield loss caused by maize rough dwarf disease. [Conclusion] The paper provided theoretical basis for further study on maize rough dwarf disease.

An efficient tool for Parkinson's disease detection and severity grading based on time-frequency and fuzzy features of cumulative gait signals through improved LSTM networks

Parkinson's disease(PD)is a widespread neurodegenerative condition that affects many individuals annually.Early identification and monitoring of disease progression are crucial to effectively managing symptoms and preventing motor complications.This research proposes an automated PD diagnosis and severity-grading model based on time-frequency and fuzzy features using improved uni-directional and bi-directional long short-term memory networks with sensitive hyperparameters optimization.We utilize vertical ground reaction force signals collected from Physionet's publicly available dataset recorded during regular and dual-task clinical trials of walking measurements.Only the cumulative signal of both feet was then utilized and segmented into 30-s windows without further pre-processing.Subsequently,we extracted only four key time-frequency and fuzzy features from each segment,effectively capturing the signal's inherent uncertainty.Bayesian optimization is employed in both detection and grading approaches to fine-tune the two critical hyperparameters:the initial learning rate and the number of hidden units in the network.The detection phase yields an exceptional accuracy of 99.19%,surpassing state-of-the-art studies with the same dataset.In the grading phase,classification based on the unified PD rating scale values achieves an accuracy of 92.28%.The proposed study delves into the potential of cumulative gait signals as a powerful diagnostic tool for PD,aiming to extract precise and intricate information by implementing straightforward and minimal processing endeavors.This method demonstrates significant effi-ciency in terms of complexity,cost,and energy consumption by utilizing a single-dimensional signal,eliminating the need for pre-processing steps,and limiting the features used for training.