Agricultural crop yield prediction is crucial for enhancing food security, optimizing resource use, and ensuring sustainable agricultural practices. This project focuses on enhancing food security and sustainable agricultural practices by predicting crop yields using machine learning techniques. This project leverages advanced data analysis techniques, including machine learning and statistical models, to accurately forecast crop yields. The study investigates the correlation between crop yield and crucial input variables such as nitrogen, phosphorous, potassium, rainfall, temperature, and fertilizer application. The primary objective is to develop accurate and reliable predictive models that enable farmers and agricultural stakeholders to anticipate crop yields, aiding in better planning and resource allocation. The research examines the correlation between crop yield and environmental factors such as nitrogen, phosphorus, potassium, rainfall, temperature, and fertilizer application. Multilinear Regression (MLR), Radial Basis Function (RBF), and Support Vector Machine (SVM) models are applied to predict yields, with SVM achieving the highest accuracy at 92.03%, followed by MLR at 88.56% and RBF at 75.36%. The data collection for this study includes pesticides usage, historical weather parameters, and fertilizer usage from the Peddapalli district, Telangana, India. MLR identifies linear relationships, RBF captures non-linear patterns, and SVM handles high-dimensional data to enhance prediction accuracy. The results indicate that while MLR and RBF provide valuable insights, SVM is the most robust tool for forecasting crop yields. This research holds significant potential for improving agricultural productivity and resource management, offering farmers crucial insights for better planning and allocation of resources.

To make it possible for computer vision to self-train and comprehend visual input, pattern recognition algorithms are mainly used. Advanced measurements are required every time for the early detection of armed threats because of decreasing accidents and terrorist attacks. Weapon detection systems are mostly used in public spaces such as stadiums, airports, key squares, and battlefields, whether they are in urban or rural settings for better security objectives. Based on cloud architecture, DL, and ML algorithms are used by contemporary closed-circuit television surveillance and control systems to detect weapons. Using the Raspberry Pi as an edge device and the Efficient model to construct the weapons detection system, edge computing can be used to address these problems. The text report, including the image processing results, is sent to the cloud platform so that the operator can review it further. Soldiers can outfit themselves with the recommended edge node, headphones, and augmented reality glasses for visual data output to receive alerts about armed threats. Furthermore, we can improve our method's performance by adding more training data and changing the network architecture. The primary object of this paper is to build a model for detecting weapons such as pistols and rifles. The data will be taken from the Kaggle dataset. Our results and recommendations will help new researchers and related organisations.

In recent years, the adoption of wireless sensor systems has significantly grown in many different kind of applications, like environmental monitoring, chemical analysis, food safety, health monitoring and quality analysis in industrial environments, and contributed to the development of the Internet of Things (IoT) paradigm.
Typically, sensors are interfaced with computing devices (e.g., microcontrollers, FPGAs, etc.) that acquire sensor data using an analog-to-digital converter (ADC). Since individual sensor nodes are usually powered by batteries, power consumption is a critical aspect that significantly impact the sensor node lifetime. In order to reduce power consumption, sensors can be interfaced with computing devices by using the sensor-to-microcontroller direct interface (SMDI), without the need to use an ADC which requires higher power. The SMDI exploits Schmitt trigger circuits that are typically integrated in the general purpose input output (GPIO) interface of a microcontroller. SMDI can be applied to many different kind of sensors, such as resistive and capacitive sensors, as well as any other sensors producing analog output voltage, and can allow sensor measurements with lower cost and power consumption than the traditional ADC based data acquisition.
In this study, we investigate the application of SMDI technique when it is employed to acquire data from a non-linear negative temperature coefficient (NTC) thermistor. We evaluated the accuracy of temperature measurements by means of electrical level simulations, considering real operating conditions and two well know models from literature (Steinhart-Hart model and polynomial model) to estimate the accuracy of temperature measurements. The results have shown that the temperature estimation using data obtained by SMDI measurements provides good accuracy. In particular, the Steinhart-Hart model provides more accurate results (average error 0.078 °C) than the polynomial model, that features an average error of 0.28 °C.

The growing world population necessitates more efficient food production, particularly in agriculture. The traditional irrigation techniques usually result in overwatering or underwatering, which wastes energy and water and reduces agricultural productivity. The smart agriculture optimizes food production, resource management, and labor. This study introduces an intelligent irrigation and monitoring system that uses the Internet of Things (IoT) to automate water pump management and monitor sun light, temperature, and humidity levels without human interaction. The system's hardware components include a soil moisture sensor, sun light sensor, temperature and humidity (DHT11) sensor, ESP32 microcontroller, and pump motor. The sensors are in charge of gathering the information that the ESP32 microcontroller needs in order to properly operate the pump motor.To operate and monitor data from the sensors remotely, the ESP32 is also integrated with the well-known Ubidots server via a wireless sensor network. Initially, sensors such as DHT11, soil moisture, and sunlight level collect data from the field and send it to the ESP32 microcontroller. The microcontroller then compares the received data to the previously stored data. If the values are greater than the threshold, the associated devices turn on and update the sensor value and pump motor condition to the Ubidots server.