Search

Fusarium head blight (FHB) is one of the most important diseases in wheat worldwide. Evaluation and identification of effective fungicides are essential for control of FHB. However, traditional methods based on the manual disease severity assessment to evaluate the efficacy of fungicides are time-consuming and laborsome. In this study, we developed a new method to rapidly assess the severity of FHB and evaluate the efficacy of fungicide application programs. Enhanced red-green-green (RGG) images were processed from acquired raw red-green-blue (RGB) images of wheat ear samples; the images were transformed in color spaces through K-means clustering for rough segmentation of wheat ears; a random forest classifier was used with features of color, texture, geometry and vegetation index for fine segmentation of disease spots in wheat ears; a newly proposed width mutation counting algorithm was used to count wheat ears; and the disease severity of the wheat ears groups was graded and the efficacy of six fungicides was evaluated. The results show that the segmentation algorithm could segment wheat ears from a complex field background. And the counting algorithm could effectively solve the problem of wheat ear adhesion and occlusion. The average counting accuracy of all and diseased wheat ears were 93.00% and 92.64%, respectively, with the coefficients of determination (R2) of 0.90 and 0.98, and the root mean square error (RMSE) of 10.56 and 7.52, respectively. The new method could accurately assess the diseased levels of wheat eat groups infected by FHB and determine the efficacy of the six fungicides evaluated. The results demonstrate a potential of using digital imaging technology to evaluate and identify effective fungicides for control of the FHB disease in wheat and other crop diseases.

Nowadays, Heart disease is one of the crucial impacts of mortality in the country. In clinical data analysis, predicting cardiovascular disease is a primary challenge. Deep learning (DL) has been demonstrated to be effective in helping to determine and forecast a huge amount of data produced by the health industry. In this paper, the proposed Recursion enhanced random forest with an improved linear model (RFRF-ILM) to detect heart disease. This paper aims to find the key features of the prediction of cardiovascular diseases through the use of machine learning techniques. The prediction model is adding various combinations of features and various established methods of classification. it produces a better level of performance with precision through the heart disease prediction model. In this study, the factors leading to cardiovascular disease can be diagnosed. A comparison of important variables showed with the Internet of Medical Things (IoMT) platform, for data analysis. This indicates that coronary artery disease develops more often in older ages. Also important in this disease's outbreak is high blood pressure. For this purpose, measures must be taken to prevent this disease and Diabetes provides a further aspect that should be taken into consideration in the occurrence of coronary artery disease with 96.6 % accuracy,96.8% stability ratio and 96.7% F-measure ratio.

Anesthesia signal monitoring is a very important indicator in surgery, and the effective monitoring of anesthesia depth has been the goal of anesthesiologists and biomedical engineering experts in recent decades. First, the wavelet transform method is used to analyze the anesthesia monitoring EEG signals, and the extracted features are clustered by wavelet classifier to estimate the depth of anesthesia. Second, the characteristics of eigenvectors are constructed by a singular value decomposition based on wavelet transform coefficients. The extraction method extracts the characteristics of the mid-latency auditory evoked EEG under anesthesia. Finally, this paper collected a large amount of clinical data and established a clinical database of anesthesia depth. The experimental results show the effectiveness of the method.