The most advanced part of precision agriculture technology is yield MONITORING of grain and non-grain crops. In this study, the horizontal pressing force of baling plunger and the angular position of the plunger connecting rod were simultaneously measured by installing a load cell and a shaft encoder on the connecting rod and plunger flywheel of a small rectangular baler, respectively. The signals of these sensors were processed in an electronic board and the output data were recorded on a portable computer for MONITORING and further analysis. Before baling the harvested alfalfa from the test field, random samples were collected and weighted to obtain a referenced measure of the yield variation along the entire field. Comparing the yield data with the pressing ENERGY and angular position data indicated a good correlation between the throughput rate of the baler and the horizontal force imparted on the baler plunger. The estimated crop yield variations were geo-referenced by using a GPS receiver. By combining the output data of the installed sensors and the positioning data, the yield map of the test field was prepared.

This study aims to evaluate a cantilever beam type piezoelectric ENERGY harvester operating on train-induced vibrations for powering Wireless Sensor Networks (WSNs) used in railway track MONITORING SYSTEMs. Harvester's behaviors under different conditions are simulated in MATLAB using the analytical model. Natural frequency, maximum deflection, and stress are calculated with greater precision using eigen frequency and stationary analysis using COMSOL Multiphysics. At a base excitation of 2 g and a resonant frequency of 4.38 Hz, the simulated results showed that the developed ENERGY harvester prototype could generate up to 14 V of AC output voltage and 550 mW of output power. These findings highlight the promising potential of the proposed ENERGY harvester for transforming train mechanical ENERGY into electrical power. This ENERGY harvester's viability and dependability for real-world applications in MONITORING railway tracks are supported by developed analytical and simulation models.

The heating, ventilation, and air conditioning (HVAC) control SYSTEM is responsible for the efficient building ENERGY SYSTEM. Indoor ENERGY consumption patterns can be monitored and reduced intelligently. Occupancy information plays a vital role to save a reasonable amount of ENERGY. Traditional ENERGY MONITORING and control SYSTEMs can be improved with the installation of the occupancy MONITORING SYSTEM which will consist of a network of sensors and cameras. In this research work, we propose a new and revolutionary convolutional neural network (CNN) based on real-time camera occupancy detection and recognition techniques across different sorts of sensors that provide realistic low-cost ENERGY-saving solutions with robust graphical processing units (GPUs). This occupancy information will decide the ENERGY behaviour inside buildings. Decision-making tools can be used to select the appropriate occupancy detection and recognition alternative for indoor environment and ENERGY MONITORING and management. In this research work, we introduce and develop the "Fermatean fuzzy prioritized weighted average and geometric operator". These aggregation operators (AOs) are a modern approach to modelling complexities in decision-making. In the end, we give an algorithm for an intelligent decision support SYSTEM (IDSS) using proposed AOs to compare our CNN based method with other existing sensors techniques.

Iran, due to its geographical location, has low rainfall and is considered a dry land. As a result, different regions of the country grapple with drought. The presence of water management SYSTEMs, such as aqueducts and reservoirs, in most parts of the country, along with a variety of methods for conserving water for irrigation, may contribute to this claim. Considering that drought is an inherent phenomenon in Iran's climate, people have invented and used numerous methods to combat it and store water. The aim of this study is to monitor and evaluate drought in Iran. In order to realize this goal, precipitation data from synoptic, rain gauge, and climatology stations were extracted over a 51-year period, from 1970 to 2020. The results obtained from examining drought occurrences in five ten-year periods reveal that, with the exception of the third decade (1991 to 2000), drought has prevailed in the majority of Iran's regions compared to other decades. On the other hand, in the recent decades leading to 2020, the intensity of drought occurrences, especially in the Middle Zagros, has intensified, which has consistently been among the regions with the highest rainfall in Iran after the Caspian region. This situation can cause concern in Iran, a country where its agricultural production hub is established along the Zagros mountain range. Moreover, the fluctuating behavior of Iran's droughts, with return periods of 2 to 5 years, has complicated the management strategies for these types of hazards. These conditions appear to have created numerous issues in many areas of Iran, particularly in the agricultural sector of the western provinces, due to the lack of conformity with these types of occurrences.