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India is an Agriculture based country and about 60% of the population depends on agriculture for their living. But there is a substantiable reduction in agricultural production has happened due to unstable climate conditions and global warming. Indian farmers are always lacking the support of advanced technologies for improving and maximizing their yield. If they get explicit information about soil type, nutrients, pH value, changes in climatic factors, previous year yield etc., they can optimize their work and yield. With the advancement of machine learning, big data analytics and cloud computing technologies, the climate and crop yield can be got predicted to the farmers. Prediction of yield in advance can help the farmers to take corrective decisions about fertilization, storage and marketing to increase their production and revenue. A huge number of studies have been conducted worldwide, on agriculture sector for the prediction of crop yield, plant diseases etc. This paper presents a brief study of different researches in India on crop yield prediction based on Machine Learning and deep learning. Most of the researchers used environmental parameters like temperature, rainfall and soil type as the main features for prediction. A number of machine learning algorithms are employed in this field and it is really a complex task to identify the best algorithm. The researchers tried to accurately predict the crop yield and thereby suggesting ways to improve production and efficient use of fertilizers. [ABSTRACT FROM AUTHOR]

India is a country of diversity. The primary occupation of the country is agriculture. In our country there is perfect weather conditions. There are significant challenges due to climatic changes that effects multiple sectors, which includes agriculture. According to food and agriculture organisation (FAO), the food production has to be increased by 70% by 2050 to meet food requirement of the population. The answer to this challenge is by introducing the new technology in the sector. In detailed introduction of IOT, UAVs and many technologies. Precision agriculture's tasks like smart spraying and aerial crop monitoring etc. Use of software development kit (SDKs) in UAVs will connect it to the mobile phones which makes farmer's ease of use. Introducing GPS in the UAV's makes discrete time broadcasts of its global coordinates, which also makes UAVs to fly at same altitude, autopilot, auto home return. By using sensors like collision avoidance sensors (CAS), gyroscope, accelerometer, magnetometer, barometer, range finder, helps the UAVs for anti-collision, balancing, determining speed, range respectively. By using thermal imaging camera, farmers can see which part of the field was affected by pests. By using the cameras farmers can monitor their crops, they can even survey their field. In this paper, we introduce the AI technology in the UAVs to help the farmers. Finally, we outline UAV based solutions for the challenges in precision agriculture. [ABSTRACT FROM AUTHOR]

The first mangrove forests appeared during the late cretaceous- Early tertiary period along Thethys Sea coasts. Since then, mangrove forests have faced and survived several catastrophic climate change events. The sea level is not and has not been constant. Studies carried out on mangrove peat prove mangroves extinction in various parts of the world due to abrupt sea-level rise. The response of mangrove in different geographic regions will depend on factors such as local rate of sea-level rise, coastal topography, and sediment supply rates. It is vital to understand mangrove floor dynamics to understand how mangroves will respond to rising sea-level. Rod surface elevation table is used worldwide to understand surface elevation or loss in wetlands. This paper deals with studying the work done using Rod Surface Elevation Table (RSET) technique to learn mangrove floor level changes worldwide. This literature review also reveals the gap in organised research studying mangrove surface elevation gain rates using RSET technique in India. [ABSTRACT FROM AUTHOR]

This paper mainly deals with the design and analysis of Agriculture data using Machine Learning (ML) techniques. Agriculture includes cultivating the soil and we can grow different types of crops. In India there are 28 states and 8 Union Territories. In each and every state people are farming and they are developing our agriculture in so many ways. Every state has its own farm for example: - Andhra Pradesh-Rice, Bazra, maize, ragi, pulses etc; the other name for agriculture is "Back Bone of India ". The sediment pressure is due to erosion and soil loss. Diffusion in nature occurs in the fine-grained sediments. Statistical analysis is being carried out for effective solution based on different parameters. This helps to get more information for farmers to make critical farming decisions. Researchers focused on agriculture and found an optimal option to increase production. Experimental data is analyzed and the production is optimized for agriculture such that it is more resilient to climatic change. [ABSTRACT FROM AUTHOR]

Countries across the globe are looking for some alternative sources of energy to cope up the depletion of fossil fuels. These alternative sources also help in minimizing the impact of climate change. To help the impact of climate change and to support the more renewable and electrical vehicle penetration, policies are designed. These policies encourage more renewable energy integration and promote electric vehicles. Net metering is a policy by the virtue of which unconsumed energy is fed into the distribution network and is utilized by the nearby consumers, connected to the same distribution network. It permits the renewable energy power producers of small scale to store or bank their electricity at the time of over-production and sent back to the grid. This paper throws some light on different net metering models available in the literature. Policy and regulatory barriers are investigated in the survey across all the states in the country. A review is presented on net metering policies across all states of India. [ABSTRACT FROM AUTHOR]

Nobody on this planet is going to be untouched by the impacts of climate change. The ability to cope with climate change varies from region to region, resulting the difference among its effects on fragility. In this paper, we focus on figuring out how the climate change influences the state’s fragility. First, we build Multilevel Fuzzy Mathematic Appraisement Model to assess the grade of the fragility. Then we identify the climate change as an indirect indicator through studying and discussing. Furthermore, after conducting Multiple Linear Regression Models between quantified climate index and twelve factors, we select the indicators that is relevant to climate change to measure the effect of climate change. Then, apply our model to the countries we choose--Sudan and India. We figure out climate change will increase the fragility index by affecting the economy, brain drain and state legitimacy of Sudan, then we quantize the growth by the multiple liner regression equation. For India, we find that when the temperature raise 1.6°C or the fragility index reach 90, India will become fragile. Then we predict by Back-Propagation neural network, finding that it is hard for India to be fragile within 100 years. Additionally, we apply our model to China, finding that its fragility index decreased with its intervention by decreasing most indicator index. Besides, we measure the total cost of intervention by Markal―Macro Model, getting that the total cost takes up 4% of GDP. Finally, because the indicators affecting the fragility of the cities is different from these affecting states, we modified it by altering the indicators we considered to apply in a small “state”, so it could be effective for cities to reflect the climate change impact on fragility. [ABSTRACT FROM AUTHOR]

Diversification of land use in the cultivation of various crops provides an alternative way to moderate the climate risk. By choosing alternative crops that are resilient to various weather parameters, farmers can reduce the crop damage and achieve optimum output from their limited land resources. Apart from other adaptation measures, crop diversity can reflect farmers' response towards changing climate uncertainty. This paper tries to examine the changing climatic condition through spatio-temporal variation of two important weather variables (precipitation and temperature) in the largest North-East Indian state, Assam, since 1950. It is examined by the variation in crop diversification index. We have used (1) Herfindahl Index for measuring degree of diversification and (2) locational quotient for measuring the changes in the regional crop concentration. The results show that, in almost all the districts, crop specialization has been taking place slowly and that happened mostly in the last phase of our study. The hilly and backward districts recorded more diversification but towards lower value crops. It goes against the normal feature of crop diversification where farmers diversify in favour of high value crops. Employing ordinary least squares method and/or Fixed Effect model, irrigation is found to have significant impact on crop diversification; while the flood plain zones and hill zones are found to have better progress in this regard, which has been due to the survival necessity of poor farmers living the zone. Thus crop diversity does not reflect very significant response from the farmers' side towards changing weather factors (except rainfall) though they have significant impact on the productivity of various crops, and thus profitability. The study thus suggests the necessity for rapid and suitable diversification as alternative climate change mitigation in the long run. [ABSTRACT FROM AUTHOR]

Atmospherics aerosols play a vital role in the field of study of Earth's radiation budget and their impact on climate change. The present study was carried out for the study of variation of aerosol characteristics during pre-monsoon season 2011 at different locations, (a) Jaipur (26.900 N, 75.900E), (b) Kanpur (26.40 N, 80.40 E) and (c) Gandhi College, Ballia (25.8° N, 84.2°E) over Indo Gangetic Basin (IGB) using AERONET level 1.5 data. Various interesting results are discussed in present paper in terms of aerosol optical and radiative properties. [ABSTRACT FROM AUTHOR]