The conservation of biodiversity is crucial as many plant species are critically under extinction. The traditional medicinal system, an alternative to synthetic drugs, promote healthy living and mainly depends on the wide repository of plants. A vision-based automatic medicinal plant identification system is proposed using different neural network techniques in computer vision and deep learning. The challenge lies in the unavailability of the medicinal herb dataset. The paper showcases a novel medicinal leaf dataset entitled DeepHerb dataset comprising of 2515 leaf images from 40 varied species of Indian herbs. The efficacy of the dataset is revealed by comparing pre-trained deep convolution neural network architectures such as VGG16, VGG19, InceptionV3 and Xception. The work concentrates on adopting the transfer learning technique on the pre-trained models to extract features and classify using Artificial Neural Network (ANN) and Support Vector Machine (SVM). The SVM hyperparameters are tuned further by Bayesian optimization to achieve a better performance model. The proposed DeepHerb model learned from Xception and ANN outperformed by 97.5% accuracy. A cross-platform mobile application entitled HerbSnap developed integrating the DeepHerb model identifies the herb image with a prediction time of 1 second per image and reveals the pertinent details of herbs from the database. This research will further focus on expanding the dataset to benefit stakeholders and thus, enriches society with the knowledge of herbs and their medicinal properties.

In the modern era of virtual computers over the notional environment of computer networks, the protection of influential documents is a major concern. To bring out this motto, digital watermarking with biometric features plays a crucial part. It utilizes advanced technology of cuffing data into digital media, i.e., text, image, video, or audio files. The strategy of cuffing an image inside another image by applying biometric features namely signature and fingerprint using watermarking techniques is the key purpose of this study. To accomplish this, a combined watermarking strategy consisting of Discrete Wavelet Transform, Discrete Cosine Transform, and Singular Value Decomposition (DWT-DCT-SVD) is projected for authentication of image that is foolproof against attacks. Here, singular values of watermark1 (fingerprint) and watermark2 (signature) are obtained by applying DWT-DCT-SVD. Affixing both the singular values of watermarks, we acquire the transformed watermark. Later, the same is applied to cover image to extract the singular values. Then we add these values to the cover image and transformed watermark to obtain a final watermarked image containing both signature and fingerprint. To upgrade the reliability, sturdiness, and originality of the image, a fusion of watermarking techniques along with dual biometric features is exhibited. The experimental results conveyed that the proposed scheme achieved an average PSNR value of about 40 dB, an average SSIM value of 0.99, and an embedded watermark resilient to various attacks in the watermarked image.

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Ayurveda medicines uses herbs for curing many ailments without side effects. The biggest concern related to Ayurveda medicine is extinction of many important medicinal herbs, which may be due to insufficient knowledge, weather conditions, and urbanization. Another reason consists of lack of online facts on Indian herbs because it is dependent on books and experts. This concern has motivated in utilizing the machine learning techniques to identify and reveal few details of Indian medicinal herbs because, until now, it is identified manually, which is cumbersome and may lead to errors. Many researchers have shown decent results in identifying and classifying plants with good accuracy and robustness. But no complete framework and strong evidence is projected on Indian medicinal herbs. Accordingly, the chapter aims to provide an outline on how machine learning techniques can be adopted to enrich the knowledge of Indian herbs, which advantages both common man and the domain experts with wide information on traditional herbs.

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