Smart Weather Data Management Based on Artificial Intelligence and Big Data Analytics for Precision Agriculture

There are many models used in global solar radiation prediction. However, these prediction models vary according to the climate and geographic characteristics of the region involved. When the prediction model determined for a region is used in a different region, the prediction model may not work. In order to gain absolute and stable results from these models, the studies should be either custom-made or tested. In this study, 7 different models were examined, comparison charts were created and statistical interpretations were made in order to determine the monthly average global solar radiation coming to the horizontal plane taking into account the climatic characteristics of Hatay province, located in the eastern 39.92º latitude and northern longitude 36.16º, covering an area of 5403 km, and has a humid climate with a value of 41.3, based on De Martonne-Gottmann’s drought index and 50.6 based on Erinç's rainfall efficiency index. Under the scope of this study, the most ideal global solar radiation prediction model was selected by making a statistical evaluation according to the parameters of determination coefficient (R²), mean percentage error (MPE), mean deviation error (MBE), root mean square error (RMSE). A general assessment of the graphics and statistical parameters of the predictions obtained by using the models and the measured data has indicated Model 4 as the most successful model by an RMSE value of 0.17306, and the least successful model was Model 3 by an RMSE value of 2.11851. When the models are interpreted according to the determination coefficient, it has been observed that other models are generally successful. However, this situation does not coincide with the comparison charts of the predicted and measured data. For this reason, it was seen that the determination coefficient was not sufficient in the statistical evaluation of the prediction models. In the interpretation of the models, it was observed that the interpretation of root mean square error, mean percentage error and mean deviation error values together gave more accurate results. In conclusion, it has been suggested that Model 4 should be preferred for the global solar radiation forecast of Hatay province.

Smart management of weather data is pivotal to achieving sustainable agriculture since weather monitoring is linked to crop water requirement estimation and consequently to efficient irrigation systems. Advances in technologies such as remote sensing and the Internet of Things (IoT) have led to the generation of this data with a high temporal resolution which requires adequate infrastructure and processing tools to gain insights from it. To this end, this paper presents a smart weather data management system composed of three layers: the data acquisition layer, the data storage layer, and the application layer. The data can be sourced from station sensors, real-time IoT sensors, third-party services (APIs), or manually imported from files. It is then checked for errors and missing values before being stored using the distributed database MongoDB. The platform provides various services related to weather data: (i) forecast univariate weather time series, (ii) perform advanced analysis and visualization, (iii) use machine learning to estimate and model important climatic parameters such as the reference evapotranspiration (ET0) estimation using the XGBoost model (R2 = 0.96 and RMSE = 0.39). As part of a test phase, the system uses data from a meteorological station installed in the study area in Morocco.

Abstract. In this paper, we evaluate the performance of two global horizontal solar irradiance (GHI) estimates, one derived from Meteosat Second Generation (MSG) and another from the 1-day forecast of the Regional Atmospheric Modeling System (RAMS) mesoscale model. The horizontal resolution of the MSG-GHI is 3 × 5 km2 over Italy, which is the focus area of this study. For this paper, RAMS has the horizontal resolution of 4 km.The performances of the MSG-GHI estimate and RAMS-GHI 1-day forecast are evaluated for 1 year (1 June 2013–31 May 2014) against data of 12 ground-based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate.Statistics for hourly GHI and daily integrated GHI are presented for the four seasons and the whole year for all the measurement sites. Different sky conditions are considered in the analysisResults for hourly data show an evident dependence on the sky conditions, with the root mean square error (RMSE) increasing from clear to cloudy conditions. The RMSE is substantially higher for Alpine stations in all the seasons, mainly because of the increase of the cloud coverage for these stations, which is not well represented at the satellite and model resolutions. Considering the yearly statistics computed from hourly data for the RAMS model, the RMSE ranges from 152 W m−2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W m−2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics computed from hourly data for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W m−2, 14 %), while the maximum is for Aosta (181 W m−2, 51 %). The mean bias error (MBE) shows the tendency of RAMS to over-forecast the GHI, while no specific behaviour is found for MSG-GHI.Results for daily integrated GHI show a lower RMSE compared to hourly GHI evaluation for both RAMS-GHI 1-day forecast and MSG-GHI estimate. Considering the yearly evaluation, the RMSE of daily integrated GHI is at least 9 % lower (in percentage units, from 31 to 22 % for RAMS in Cozzo Spadaro) than the RMSE computed for hourly data for each station. A partial compensation of underestimation and overestimation of the GHI contributes to the RMSE reduction. Furthermore, a post-processing technique, namely model output statistics (MOS), is applied to improve the GHI forecast at hourly and daily temporal scales. The application of MOS shows an improvement of RAMS-GHI forecast, which depends on the site considered, while the impact of MOS on MSG-GHI RMSE is small.

The irrigation systems require modernization and management by evaluating water system prerequisites precisely. A study was carried out at Srinagar during kharif 2022 to determine the crop water demands, irrigation scheduling and simulation of moisture movement under different irrigation regimes on tomato crop in open field conditions using CROPWAT and HYDRUS-1D models. The results revealed that the average crop water requirement at 100% ETC per plant per day was 0.24 l plant-1 day-1 during the initial stage, 0.37 l plant-1 day-1 during development stage, 0.85 l plant-1 day-1 during mid-stage and 0.74 l plant-1 day-1 during the end stage. Soil water content was simulated by HYDRUS-1D model in 0 to 30 cm of soil profile. Higher values (0.86 to 0.95) of coefficient of determination (R2) indicated that observed and simulated values of moisture content are highly correlated and the model predicts that lower values of mean absolute error (MAE) and root mean square error (RMSE) indicates that the HYDRUS-1D model is more accurate at simulating the movement of moisture under different irrigation regimes.

Air temperature (T, °C), relative humidity (RH, %), and global solar radiation (G, MJ.m-2 ) have been measured in the meteorological station at South Valley University (SVU) at Qena, Egypt, from 2000 to 2013, while the total column ozone (TCO, DU) is downloaded from Giovanni's website. T, RH, and TCO are important meteorological parameters, and they are useful to estimate the missing data of the global solar radiation (G), as global solar radiation is desirable for electricity generation applications and for agriculture. Qena is a subtropical region in Upper Egypt, as it's characterized by clear weather most days of the year, and it's very hot in the summer and cold in the winter. The meteorological station at South Valley University (SVU) stopped measuring the G. The linear regression equation and the most important statistical indices are used in this paper such as, the determination coefficient (R2 ), the mean absolute error (MAE), the mean absolute bias error (MABE), the mean square error (MSE), the root mean square error (RMSE), the mean percentage error (MPE), the mean bias error (MBE), the model efficiency (ME), and the agreement index (d). For verification of the empirical models' efficiency, the data of a new period has used, 2013, and the results of the models were excellent and valid for estimating the missing data, as R2 was more than 0.92 in all models but it was near one in models 1 and 4. MAE was close to zero for all models. MBE, MPE, and MABE were close to zero for all models except model 3. Model 1 was the best one, as, R2 , MAE, MABE, MSE, RMSE, MAPE, d, ME, and MPE were 0.9883, 0.0193, 0.383, 0.2303, 0.4799, 1.927, 0.9979, 0.992, and 0.2932, respectively

Accurate predictions of herbage biomass are important for efficient grazing management. Small‐scale farms face challenges using remote sensing technologies due to insufficient resources. This limitation hinders their ability to develop machine learning‐based prediction models. An alternative is to adopt less expensive measurement methods and readily available data such as weather data. This study aimed to examine how different temporal aggregations of weather data combined with compressed sward height (CSH) affect the prediction performance. We considered weather features based on different numbers of weather variables, statistical functions, weather events, and periods. Between 2019 and 2021, data were collected from 11 organic dairy farms in Germany. Herbage biomass exhibited high variability (coefficient of variation [CV] = 0.65). Weather data were obtained from on‐farm and nearby public stations. Prediction models were learned on a training set (n = 291) and evaluated on a test set (n = 125). Random forest models performed better than models based on artificial neural networks and support vector regression. Representing weather data by a single feature for leaf wetness reduced the root mean square error (RMSE) by 12.1% (from 536 to 471 kg DM ha−1, where DM is dry matter) and increased the R2 by 0.109 (from 0.518 to 0.627). Adding features based on multiple variables, functions, events, and periods resulted in a further reduction in RMSE by 15.9% (R2 = 0.737). Overall, different aggregations of weather data enhanced the accuracy of CSH‐based models. These aggregations do not cause additional effort for data collection and, therefore, should be integrated into CSH‐based models for small‐scale farms.

Information about the availability of solar radiation on horizontal surface is essential for the optimum design and study of solar energy systems. For a country like UAE, the efficient application of solar energy seems inevitable because of abundant sunshine available throughout the year. The traditional way of knowing the amount of global solar radiation (GSR) in a particular region is to install pyranometers at as many locations as possible in this region thus requiring daily maintenance and data recording, and consequently increasing cost of GSR data collection. Therefore, it is rather more economical to develop methods to estimate the GSR using climatological parameters (Akhlaque et al., 2009; Kassem et al., 2009; Falayi et al., 2008; El-Sebaii & Trabea, 2005). Many researchers estimated global solar radiation by using artificial neural networks. (Mohandes et al, 1998) applied ANN techniques to predict GSR using weather data from 41 stations in Saudi Arabia. Data from 31 stations was used in training the NN and the remaining data was used for testing. Input variables to the NN included 4 parameters: latitude, longitude, altitude and sunshine duration. Their sample data was not large enough to allow a credible comparison between the ANN models used and empirical regression models. (Lam et al., 2008) have used Artificial Neural networks ANNs to develop predication models for daily global solar radiation using measured sunshine duration for 40 cities covering 9 major thermal climatic zones and sub-zones in China. (Alam et al., 2009) used ANNs to estimate monthly mean hourly and daily diffuse solar radiation based on weather data from 10 Indian stations which have different climatic conditions. (Alawi & Hinai, 1998) applied ANNs to predict solar radiation in areas not covered by direct measurement instrumentation. The input data that was used for building the network were the location, month, mean pressure, mean temperature, mean vapor pressure, mean relative humidity, mean wind speed and mean duration of sunshine. (Tasadduq et al., 2002) have used neural networks for the prediction of hourly mean values of ambient temperature 24 hours in advance. Full year hourly values of ambient temperature are used to train a neural network model for a coastal location — Jeddah, Saudi Arabia. (Elminir et al., 2005) applied ANN modeling techniques to predict solar radiation data in different spectrum bands from data of meteorology for Helwan (Egypt) meteorology monitoring station. (Rehman & Mohandes, 2008) developed ANN-based estimation GSR for Abha city in Saudi Arabia; by

Based on the data provided from the new satellite constellation, the crop phonology can be mapped accurately with a high spatial and temporal resolution. The potential of Sentinel-2 (high spatial and temporal resolution, 10 m and 5 days) is investigated in order to monitor and forecast the crop growth over different olive groves located in Sfax, Tunisia, from the beginning of 2016 to the end of 2020. The normalized difference vegetation index (NDVI) is used as an indicator of vegetation health due to its high correlation with crop growth health and productivity, and it is particularly forecasted to predict the trends. The prediction is done using two different statistical methods, autoregressive (AR) and Markov chain (MC), based on historical data derived from Sentinel-2 data. The prediction is applied for three different areas having various vegetation types and density. Moreover, for an accurate and precise prediction, our study areas are divided into different homogeneous clusters using the well-known Gaussian mixture model. Furthermore, the performances of our approach are assessed by means of the root mean squared error (RMSE) between the predicted results and the actual data. Globally, the obtained results for all the clusters of each area show that the forecast, using both methods, is accurate where the error is less than 5%. Nevertheless, the MC model displays the highest performance where the predicted NDVI curves of the different study areas are the closest to the actual observation. MC (resp. AR) precision is of 97% with RMSE = 0.025 (resp. 95% and RMSE = 0.041) for all the clusters of Jbeniana and Limaya, and 99% with RMSE = 0.0073 (resp. 98% and RMSE = 0.0127) for the four clusters of Chaâl.

Authentic information of the availability of global solar radiation is significant to agro/hydro meteorologists, atmospheric Physicists and solar energy engineers for the purpose of local and international marketing, designs and manufacturing of solar equipment. In this study, five new proposed temperature dependent models were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperature meteorological data during the period of thirty one years (1980-2010). The new models were compared with three existing temperature dependent models (Chen et al., Hargreaves and Samani and Garcia) using seven different statistical validation indicators of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA) to ascertain the suitability of global solar radiation estimation in five different locations (Zaria, Bauchi, Jos, Minna and Yola) situated in the Midland climatic zone of Nigeria. In each location, the result shows that a new empirical regression model was found more accurate when compared to the existing models and are therefore recommended for estimating global solar radiation in the location and regions with similar climatic information where only temperature data are available. The evaluated existing Hargreaves and Samani and Garcia temperature based models for Jos were compared to those available in literature and was found more suitable for estimating global solar radiation for the location. The comparison between the measured and estimated temperature dependent models depicts slight overestimation and underestimation in some months with good fitting in the studied locations. However, the recommended models give the best fitting.  Â

This study aims to develop hybrid empirical models for estimating global solar radiation in selected locations across Ebonyi State, Nigeria, to enhance photovoltaic energy generation and support climate change mitigation and adaptation. The research is designed to create empirical models for calibrating and modeling global solar radiation using meteorological parameters at Alex Ekwueme Federal University Ndufu-Alike, Ebonyi State University Abakaliki and Akanu Ibiam Federal Polytechnic Unwana, all in Ebonyi State, Southeastern Nigeria. The long term monthly mean daily global solar radiation on the horizontal surface, sunshine hours, relative humidity, minimum and maximum temperature at 2 m height for the period of 1984-2019 for the selected stations were obtained from the National Aeronautics and Space Administration (NASA) atmospheric science data centre. A multi-parameter based model was used to estimate the global solar radiation in each of these locations using Angstrom-Prescott-Page Model. Model performance was evaluated using statistical metrics, including Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Mean Percentage Error (MPE). Additionally, the correlation coefficient (r) and coefficient of determination (r2) were calculated. Results indicate that the developed empirical models demonstrate a high level of accuracy in estimating daily global solar radiation. Comparisons with existing models in the literature show that the locally calibrated models perform better on monthly and yearly timescales. Therefore, these models can be applied for solar radiation forecasting across Ebonyi State. However, routine recalibration is recommended, as climate variability over time may affect model stability and performance.

Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia

Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices. The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values, leading to effective operation of power-aware systems. The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm. The root mean square error (RMSE) and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria (the performance score parameter and normalized geometric distance) were used for overall evaluation and comparison. The experiments showed that the novel DDSL method reaches significantly lower RMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm. The overall criteria performance score is 40% higher than the reference algorithm base on static confirmation settings.

Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman–Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.

Direct feature extraction and image co-registration of morphological structure from Bayer pattern raw planetary images

Most Internet-of-Things (IoT) applications via satellite depend on reasonably priced, energy-constrained, location-aware IoT sensors. In this letter, we present a brand technique that uses straightforward Doppler measurements to estimate the satellite visibility window on Internet of Things devices. We provide two cases where the IoT device does not initially know the geographic coordinates of the serving satellite: (i) we assume that the IoT device knows the coordinates, and (ii) we assume that the coordinates are unknown. As a result, we obtain the Doppler measurement likelihood function and reduce it to a problem of minimizing root mean square error (RMSE). We use a stochastic optimizer to minimize the root mean square error (RMSE) to predict the orbital parameters of the serving satellite from a series of Doppler observations. We then forecast the satellite visibility window (satellite pass) based on the orbital estimation. We compute the overlapping visibility window between the ground truth and the estimation using the intersection-over-union metric in order to assess the correctness of the window estimation. The results are based on extensive MATLAB simulations. In addition to the proposed technique utilizing Doppler measurements for estimating satellite visibility windows in IoT devices, the system is structured around two ground stations. These ground stations are interconnected through a process facilitated by Dijkstra’s Algorithm, which calculates the shortest path between them via visible satellites. This integration of Dijkstra’s Algorithm ensures optimal routing and connectivity between the ground stations, enhancing the efficiency and reliability of the IoT application over satellite communication.