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Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamil Nadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U2-Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%.

Human gait is a behavioral characteristic which has received a large amount of consideration in recent times as a biometric identifier. The clothing variance is one of the most common covariate influences which can influence the performance of gait recognition approach in real-world scenarios. This paper proposes a gait recognition approach proficient in choosing information characteristics for individual identification under different clothing conditions. The proposed method constitutes of addressing the feature extraction technique by introducing a binary descriptor called as Modified Local Optimal Oriented Pattern (MLOOP). In the proposed approach, initially, the feature vectors such as histogram and horizontal width vector are extracted from MLOOP descriptor, and then the dimensionality of the feature vector is reduced to remove the irrelevant features. The performance of MLOOP was accessed against its predecessors. Obtained experimental results demonstrate that the MLOOP descriptor performs better than the previous binary descriptors. Furthermore, the performance analysis of the proposed approach was assessed on OU-ISIR B treadmill gait database and CASIA B gait database. Broad investigations demonstrate the viability of the proposed technique. [ABSTRACT FROM AUTHOR]

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