Natural disasters are catastrophic events and cause havoc to human life. These events occur in the most unpredictable times and are beyond human control. The aftermath of the disasters is devastating ranging from loss of life to relocation of large groups of the population. With the development in the domains of computer vision (CV) and Image processing, machine learning and deep learning models can integrate images and perform predictions. Deep learning techniques employ many robust techniques and provide significant results even in the case of images. The detection of natural disasters without human intervention requires the help of deep learning techniques. The project aims to employ a multi-layered convolutional neural network (CNN) organization to classify the images related to natural disasters related to earthquakes, floods, cyclones, and wildfires.

Agricultural management is significant in a populous country like India. Farmers must have advance knowledge about predicted crop production and crop condition within particular area to make economic and farming decisions. To generate yield, we consider factors like temperature, humidity, pressure, NDVI values, Latitude, Longitude etc. When cultivating a particular crop on a specific type of soil, there are a number of factors to be considered. A crop recommender system considers soil properties such as N, P, and K, as well as other factors like rainfall, humidity, and pH levels, to choose the best crop for the farm. This paper presents a predictive algorithm that would estimate crop yield using deep neural networks with geographical data. A recommendation system was built using machine learning algorithm like Xgboost to recommend the suitable crop. A user interface named CROPUP has been developed to scale up crop productivity and efficiency using the proposed algorithms.

Acreage estimates are crucial for forecasting menthol mint acreage, as crop output figures fluctuate from year to year in response to fluctuations in the market price of menthol mint oil. Thus, there are yearly fluctuations in the maximum price that farmers can obtain. Since low production arises from low rates, and high production results from high prices, these acreage estimate studies may be useful in lowering uncertainty regarding menthol mints’ output. The widespread adoption of remote sensing technologies for assessing crop acreage at both the national and international levels can be attributed to their low cost, ease of use, and flexibility. The extent of an area planted with menthol mint in the Vishakhapatnam district of Andhra Pradesh, India, was estimated using Sentinel-2A satellite data for that year. After conducting a comprehensive ground survey, the area of the menthol mint crop was estimated using an adaptive maximum chance-type set of rules for taluk-level statistics. According to the research, the Bheemunipatnam taluk in the Vishakhapatnam district was the most productive in growing menthol mint. Using customer and manufacturer accuracies of 89.13% and 87.23%, along with the average accuracy (90.67%) and kappa rate (0.9), the total acreage of menthol mint crop in the study region was estimated to be around 58,000,284.70 ha (0.844). A further aim in this study was to estimate the acreage planted with early and late menthol mint. Around 26,123.50 ha and 29,911.40 ha were found to be home to early menthol mint and late menthol mint, respectively. This method shows promise for early- and late-stage crop acreage assessment of menthol mint using a localised degree of precision.

As the field of nanotechnology advances, there is an increasing need for green nanomaterial identification devices. Recently, a few new studies have reminded us that as nanotechnology gets better and better, so will natural phenomena. As we grow closer to and finally reach the nanoscale, it is feasible that new physical expertise will develop. Developments in the future may allow for new technical advancements. It is the ability of nanotechnology to construct human constructs at the nanoscale that distinguishes it from other fields of science and engineering. Various components, including high‐dissociation electron microscopy, centre‐ion beam milling tools, and scanner probes, have made this practical. Spectrometers, sometimes known as spectrometers, are used in fabric identification machines. To conclude this inquiry, a nanoparticle scatter spectrometer was devised and built artificially. This study focused on the visible spectrum of spectroscopy because there are a broad number of programmes available for visible optical instruments.

Climate change, as known, is the dangerous environmental effect we are going to face in the near future and electricity contributes the majority of its part in overcoming climate change as per the trends. Usage of electricity is widely increasing all over the world mainly as an alternative to the use of fossil fuels. In households the usage is rapidly increasing day by day, owing to the increase in the number of devices running on electricity. As we have observed mainly after the relaxation of the lockdown the bills received by households, especially in cities were unhappy and have left most of the people aghast. It is evident that users have no idea about the power they consume. In this work, a model to forecast the electricity bill of household users based on the previous trends and usage patterns by making use of machine learning techniques has been proposed. The historical data of the user is studied and the learning is done iteratively to improve the accuracy of the model. The model can then be used to forecast the consumption beforehand.