The incorporation of Internet of Things (IoT) and Blockchain technologies in smart grids has completely transformed the energy industry, greatly improving efficiency, security, and dependability. IoT devices provide instantaneous monitoring and control, enhancing the efficiency of energy distribution and utilisation. Blockchain simultaneously guarantees the integrity, security, and transparency of data by utilising a decentralised ledger, which promotes trust and reduces the likelihood of cyber-attacks. This paper examines the collective influence of these technologies on smart grids, encompassing the possible use of fuzzy set theory to enhance decision-making in situations of ambiguity. This article examines the possibilities of IoT, Blockchain, and fuzzy logic in developing energy infrastructures that are both sustainable and resilient, based on an analysis of current advancements and global practices.

Agriculture is essential for living in the Indian Himalayan region (IHR), as it functions as the main occupation and source of income. Therefore, monitoring crops and detecting diseases at early stages becomes essential. Our research leverages deep learning (DL), a dynamically growing technology proficient in handling large datasets. In this work, convolutional neural networks (CNNs) are used to solve real‐world problems using the computer vision technique. This paper proposes a modified AlexNet architecture for detecting diseases through images at an early stage and to increase the production rate by cultivating good quality crops. This model combines steps such as tomato leaf image data collection, image data preprocessing, and classification and detection of disease. In this work, a total of 5500 tomato leaf images have been taken from Kaggle, and an additional 1650 photographs have been collected from a variety of field sites, thereby creating a comprehensive dataset that will facilitate the development and assessment of a robust model. In the classification and detection of tomato plant leaf diseases, our proposed model achieves an accuracy of 94.80%. Using DL technology, we hope to give farmers an accurate way to monitor and protect their crops, ultimately enhancing agricultural quality and output.