Agriculture in Maharashtra has immense importance in India, acting as the back-bone of the economy and a primary livelihood source for a significant population. Being the third largest state in India, Maharashtra has a high scale crop production in the country which also has an important impact on the economy. Initially the study focus on developing predictive models that guide farmers in selecting suitable crops for the divisions in the state of Maharashtra. This study presents a Crop Recommendation System (CRS) designed to support Maharashtra’s agricultural sector by utilizing a comprehensive dataset from 2001 to 2022 provided by the India Meteorological Department. This study helps in improvising technical efficiency and productivity of the farmers. Harvesting crops in optimal condition can help to produce efficient harvest hence the research concentrates on providing best crop recommendation system (CRS) with the help of Machine Learning and Deep Learning techniques. The data, enhanced for accuracy using expectation-maximization optimization, underpins predictive models that guide crop selection. EM contributes to a more robust and reliable dataset for subsequent analyses and modeling by iterative estimating and updating missing values based on probabilistic expectations. Key findings show that the Random Forest algorithm excels in predicting suitable crops with 92% accuracy. Further precision is achieved through a Long Short-Term Memory network forecasting weather patterns three months ahead, accommodating temporal data variations. Subsequently, the proposed system leverages these forecasts to recommend five ideal crops per division within Maharashtra, aiding farmers’ decision-making and adapting to regional climatic conditions. A supplementary crop calendar offers monthly district-specific planting guidance. An intuitive Graphical User Interface delivers this information effectively, ensuring practical and informed agricultural choices across the state. In essence, the study provides an innovative tool for enhancing economic stability and sustenance in Maharashtra through technology-driven agriculture recommendations aligned with future weather expectations.

The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, early detection and gas type identification are necessary. The main focus of this paper is multimodal gas data that were obtained simultaneously by using multiple sensors for gas detection and a thermal imaging camera. As the reliability and sensitivity of low-cost sensors are less, they are not suitable for gas detection over long distances. In order to overcome the drawbacks of relying just on sensors to identify gases, a thermal camera capable of detecting temperature changes is also used in the collection of the current multimodal dataset The multimodal dataset comprises 6400 samples, including smoke, perfume, a combination of both, and neutral environments. In this paper, convolutional neural networks (CNNs) are trained on thermal image data, utilizing variants such as bidirectional long–short-term memory (Bi-LSTM), dense LSTM, and a fusion of both datasets to effectively classify comma separated value (CSV) data from gas sensors. The dataset can be used as a valuable source for research scholars and system developers to improvise their artificial intelligence (AI) models used for gas leakage detection. Furthermore, in order to ensure the privacy of the client’s data, this paper explores the implementation of federated learning for privacy-protected gas leakage classification, demonstrating comparable accuracy to traditional deep learning approaches.

Smart cities driven by the Internet of Things (IoT) transform urban environments through data-driven service and intelligent automation. These systems remain subject to cyberattacks, data manipulation, and privacy breaches due to centralized IoT architectures and limited resources. In this paper, a lightweight two-tier blockchain framework is proposed which is designed specifically for secure and scalable smart city environments. The proposed model combines a local immutable ledger for low-overhead and fast processing at the edge, along with cluster-based overlay blockchain which ensures distributed trust and accountability. The key development is the separation of data flow and transaction control which enables parallelism and hereby reducing service latency. The simulations were conducted using Cooja simulator for resource-constrained IoT devices and NS3 for 40 nodes overlay which demonstrates the feasibility of the approach. The performance analysis of the proposed model presents that the model achieves lower packer overhead and end-to-end delay, when compared with existing models. Moreover, acceptable energy and processing overheads are introduced by the cryptographic operations. This paper also presents a threat model and explains how the model addresses confidentiality, integrity and availability concerns. The proposed model achieves 17% increase in processing time in comparison with baseline models but achieves 21% lesser packet overhead in comparison with secure monolithic models. These observations establish the proposed model as a practical and secure solution for next-generation smart city deployments.