Black gram Plant Leaf Disease (BPLD) dataset for recognition and classification of diseases using computer-vision algorithms.

This article introduces Black gram Plant Leaf Disease (BPLD) dataset, which is scientifically called as Vigna Mungo and is popularly known as Urad in India. It is widely considered to be one of the most significant pulse crops farmed in India. Anthracnose, Leaf Crinkle, Powdery Mildew and Yellow Mosaic diseases shown significant impact on the black gram production and causing financial loss to the farmers. A fusion of image processing and computer vison algorithms are widely used in recent years, for applications in the diagnosis and categorization of diseases that affect plant leaves. To detect and classify plant leaf diseases which degrades the quality of the black gram crop, in early stages, using computer vision algorithms, a Black gram Plant Leaf Disease (BPLD) dataset was created and briefly discussed in this article. The dataset holds a total of 1000 images belongs to five classes: four diseases and one healthy. The images in the presented dataset were captured under the real cultivation fields at Nagayalanka, Krishna, Andhra Pradesh, using camera and mobile phones. After the image acquisition, the images were categorized and processed with the help of agriculture experts. Researchers who utilize image processing, machine learning and particularly deep learning algorithms for automated diagnosis and classification of black gram plant leaf diseases in early stage to assist farmers could benefit from this dataset. The dataset is publicly and freely available at https://doi.org/10.17632/zfcv9fmrgv.3.

Design of tree structured matched wavelet for HRV signals of menstrual cycle.

An algorithm is presented for designing a new class of wavelets matched to the Heart Rate Variability (HRV) signals of the menstrual cycle. The proposed wavelets are used to find HRV variations between phases of menstrual cycle. The method finds the signal matching characteristics by minimising the shape feature error using Least Mean Square method. The proposed filter banks are used for the decomposition of the HRV signal. For reconstructing the original signal, the tree structure method is used. In this approach, decomposed sub-bands are selected based upon their energy in each sub-band. Thus, instead of using all sub-bands for reconstruction, sub-bands having high energy content are used for the reconstruction of signal. Thus, a lower number of sub-bands are required for reconstruction of the original signal which shows the effectiveness of newly created filter coefficients. Results show that proposed wavelets are able to differentiate HRV variations between phases of the menstrual cycle accurately than standard wavelets.

Adaptive correlation dimension method for analysing heart rate variability during the menstrual cycle.

Correlation dimension (CD) is used for analysing the chaotic behaviour of the nonlinear heart rate variability (HRV) time series. In CD, the autocorrelation function is used to calculate the time delay. However, it does not provide optimum values of time delays, which leads to an inaccurate estimation of the HRV between phases of the menstrual cycle. Thus, an adaptive CD method is presented here to calculate the optimum value of the time delay based upon the information content in the HRV signal. In the proposed method, the first step is to divide the HRV signal into overlapping windows. Afterwards, the time delay is calculated for each window based on the features of the signal. This procedure of finding the optimum time delay for each window is known as adaptive autocorrelation. Then, the CD for each window is calculated using optimum time delays. Finally, adaptive CD is calculated by averaging the CD of all windows. The proposed method is applied on two data sets: (i) the standard Physionet dataset and (ii) the dataset acquired using BIOPAC(®)MP150. The results show that the proposed method can accurately differentiate between normal and diseased subjects. Further, the results prove that the proposed method is more accurate in detecting HRV variations during the menstrual cycles of 74 young women in lying and standing postures. Three statistical parameters are used to find the effectiveness of adaptive autocorrelation in calculating time delays. The comparative analysis validates the superiority of the proposed method over detrended fluctuation analyses and conventional CD.