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The vast majority of individuals in India rely on agriculture as their main source of income. Precision agricultural advancements, thus, become more significant as a result of their economic impact. The paper offers a novel smart roof-based irrigation system as an advancement in the field of precision agriculture. This system can be deployed over any agricultural land holding. The area of agricultural land and crops will be protected by the smart roof, which is equipped with a rain sensor, in the event of unexpected or heavy rain. The slanted roof would direct rainfall into a tank adjacent to the rain cover. A moisture sensor would monitor the crops and the soil around them, and stored rainfall would be sent to the plants as needed. The proposed arrangement would not only benefit the cultivators by preventing the agricultural produce from going to waste but would also reduce groundwater consumption through rainwater harvesting. With water supply automation, crops would receive the precise amounts of water they require, considering various aspects including breed, season, and moisture retentivity. Through the introduction of such an innovation, an improvement in crop quality, as well as crop yield, is anticipated. [ABSTRACT FROM AUTHOR]

Agriculture is an integral part of people's living in India, whose economy is mainly based on farming. People cultivating crops have a significant problem identifying environmental conditions and factors that require their attention. In this paper, the authors present system integration utilizing Internet of Things (IoT) modules that monitor the farms and control the temperature in the traditional cultivation method. In work, we included the sensors to monitor the crops to acquire the details of farming conditions using which the farmer can retain healthy crop cultivation. The proposed work includes sensors to monitor temperature, humidity, soil moisture, rain using a Peltier sensor to control the temperature according to the crop needs. [ABSTRACT FROM AUTHOR]

For the past few years, the South Peninsular India (SPI) is facing natural calamities in terms of rainfall extremes. With the advent of satellite datasets, the challenges in the prediction of rainfall have improved to some extent. In this paper, effort has been made to evaluate Climate Prediction Center (CPC)-South Asia data set, CPC Morphing Technique (CMORPH) against observed Indian Meteorological Department (IMD) daily precipitation data over SPI. Different statistical indices including 4 categorical indices were used to assess the capability of the product in estimating precipitation extent and rainfall occurrence prediction of monsoon season from 2014 – 2020. Results suggest a strong correlation (0.8), Index of Agreement (0.9) and Kling Gupta Efficiency Index (0.7) between the predicted and observed data. Higher value of Probability of Detection (POD), Critical Success Index (CSI) and a lower value of False Alarm Ratio (FAR) implies the strong capability of CMORPH in the precipitation estimation. [ABSTRACT FROM AUTHOR]

Indian economy is contributed heavily by agriculture. Most of the Indian farmers rely on their instincts to decide the crop to be sown at a particular time of year. They do not realize that the crop output is circumstantial, and depended heavily on the present-day weather and soil conditions. A single uninformed decision by the farmer can have undesirable consequences on the economic conditions of the region and also mental and financial impacts on the farmer himself. Applying systematic Machine Learning models will effectively help to alleviate this issue. The dataset used in the paper is built by adding up the datasets of India's rainfall, climate, and fertilizer. Machine learning models will be used on the dataset to get the highest accuracy model to recommend a crop for the farm's location. This recommendation will help the farmers in India to make learned decisions about the crops. The recommendation will take into account the parameters like the farm's location, sowing season, soil properties, and climate. [ABSTRACT FROM AUTHOR]

Water is a basic necessity of life. The primary naturally available source of water on earth is through precipitation. Precipitation has various forms such as, rainfall, snow, sleet, hail, glaze, etc. Among them rainfall is the main form. Water falling onto the surface in the form of rainfall either infiltrates into the earth’s surface or runs-off to form streams, rivers, lakes and other water bodies. When rainfall is excess, these water bodies overflow and this leads to flooding of the low-lying nearby areas. On the contrary, when the rainfall is low, the water bodies become dry and this leads to droughts and famines. Therefore, sustainability of living organisms primarily depends on rainfall. Therefore, it is of utmost importance to derive a rainfall-runoff correlation in order to detect these natural catastrophes, especially in a country like India, where the primary occupation is agriculture. There are a number of techniques of rainfall-runoff analysis, among which regression analysis is being used popularly. In this paper, the relationship between rainfall and runoff is modeled using regression analysis. [ABSTRACT FROM AUTHOR]

Identification of trend or persistence in the rainfall series is essential to present the hydrological information in the condensed form for decision-making in water resources planning of any region. This paper exemplifies a study involving non-parametric statistical method of Mann-Kendall test for identification of existence of trends in annual hourly maximum, monthly and annual, and seasonal rainfall series for Kanyakumari and Thiruvananthapuram regions. Sen’s estimator of slope method was used to compute the change per unit time in a time series having linear trend. The paper presents the downward trend in the annual rainfall series of the regions covered under study. The paper also presents the upward trend in winter and monsoon seasons of rainfall for Kanyakumari and Thiruvananthapuram regions. © 2007 American Institute of Physics [ABSTRACT FROM AUTHOR]

Statistical analysis of historical rainfall, involving fitting of an appropriate distribution for obtaining rainfall intensities of different interval, is very much useful for making resource allocation decisions concerning risks. This paper gives details of a study involving the use of six statistical distributions for rainfall estimation for Barh and Megharaj sites. Anderson Darling test statistic was used for checking the goodness of fit of the distributions to the recorded data. Diagnostic analysis, involving D-index statistic, was used for evaluating the suitability of the probability distribution for the data under study. The study shows the Log Pearson Type III to be the best suited, among six statistical distributions studied, for extreme value analysis of the rainfall for the data under study. © 2007 American Institute of Physics [ABSTRACT FROM AUTHOR]

The efficient design of reliable communication link requires the knowledge of rain fade margin to be provided in the system design to overcome the losses in the signal strength due to rain over the path. Hence there is need for the development of reliable and accurate propagation model for attenuation prediction at the operating frequency of the link at particular location. Therefore study of rain-induced effects on microwave propagation was undertaken at Guru Nanak Dev University, Amritsar. This paper describes the measurement of zenith path attenuation due to rain at Amritsar (31° 38′ N 74° 52′ E) India, using zenith looking radiometer operating at 20 GHz. The effective rain height has been obtained from the analysis of the radiometric data and point rainfall intensity. Specific attenuation for various rain rates in the present study has been evaluated from measured drop size data using lognormal drop size distribution. The average effective rain height at Amritsar has been estimated and compared to that of predicted by ITU-R model. © 2007 American Institute of Physics [ABSTRACT FROM AUTHOR]

Rain attenuation studies especially in tropical countries assume significance due to sparse measurements and variability in rain rates and drop size distributions. Conventional techniques are not very significant in dealing with the attenuation due to melting layer and vertical structure of rainfall. The MST radar located at Gadanki, India, is an excellent state of the art system for atmospheric studies. Earlier studies have demonstrated that VHF radars like MST can also be used for precipitation studies. The VHF Radar operating at 53 MHz with a peak power of 2.5 MW is able to provide details of melting layer and drop fall velocities. However, identification of precipitation echo from the clear echo is quite challenging, as 53 MHz is not attenuated by rain. Nevertheless studies have clearly demonstrated in identifying the two echoes. Present Paper deals with new algorithms based on the wavelet analysis techniques (time series data (I & Q)) to deal with the problem. The technique has been used for SNR improvement, and effective tracing of the Doppler profile even in low SNR regions. This is a definite advantage over the conventional algorithms based on single peak detection which usually fail in extracting the Doppler profile in the low SNR regions. Also, the conventional algorithms fail to trace the clear air echo during convection. The proposed algorithm has been tested on both the clear air and convection data sets. The results thus obtained have been compared with existing algorithms. Consistency of Doppler profile and better height coverage has been reported. © 2007 American Institute of Physics [ABSTRACT FROM AUTHOR]

In present days radio communication scenario, three issues are considered to be the most important. These are reliability, directivity and regulation of transmitted power. All such issues can be taken care provided the characterization of our tropospheric medium is done with good degree accuracy. The radio wave propagating above 10 GHz is affected severely by rain and cloud. In case of rain, the attenuation of radio wave is the main concerned but for cloud along with attenuation, noise temperature generated by cloud is also another issue. Cloud attenuation and cloud noise temperature can be deduced if cloud morphology in terms of cloud coverage, cloud height, cloud thickness, cloud liquid water content etc. is available. The meteorologists of India have studied cloud morphology as well as cloud dynamics for the weather forecasting purposes. But in this paper, an attempt has been made to deduce results on cloud parameters for radio wave propagation studies by utilizing the observations taken by the sophisticated computer controlled Doppler radar belonging to the India Meteorological Department over Kolkata. The Doppler radar operates in C-band at frequency ∼ 2700–2900 MHz with peak power 500 kW. Several cloud parameters viz. radar reflectivity, cloud thickness, cloud height, cloud vertical integrated liquid water content etc. have been studied in probability distribution scale. Based on cloud height, the temperature of the cloud has been determined. It is seen that over Kolkata, the cloud height ∼ 6.5 km occur for maximum percentage of time. The cloud temp has been estimated by using the lapse rate of the temperature with height, which is ∼ 6.5°c km. By using the cloud height, cloud temperature and cloud liquid water content, the attenuation of radio wave due to cloud at microwave and millimeter wave frequency bands, over Kolkata have been derived. It is seen that attenuation value varies from 0.2 dB / km to 30 dB / km over the frequency range from 10 GHz to 100 GHz. The noise temperature generated by cloud has been estimated and found to be from 8°K to 202°K at the frequency range from 10 GHz to 70 GHz with cloud thickness ∼ 1 km and at cloud temperature ∼ 260 °K. Such noise affects the signal to noise (S/N) ratio of the communication system. © 2007 American Institute of Physics [ABSTRACT FROM AUTHOR]