Precision agriculture is a modern technology that focuses on the crop by meeting the specific needs of the field. This research presents the Wireless Soil Health Beacons design that can be used in precision agriculture to enhance the production and real-time monitoring of the soil and field parameters. The proposed system integrates bio and physical sensors into an IoT-enabled Wireless Soil Health Beacons (WSHB) to provide detailed and real-time soil health parameters. The beacons are compact and are powered by solar, which is weather-resistant and interconnected via wireless nodes. A set of beacons will be implanted to capture biological and environmental data. The biosensor module detects key soil microbiological parameters such as nitrogen-fixing microbial activity, soil pathogen presence, and general microbial population shifts indicative of soil fertility and disease conditions. The physical sensor module continuously measures soil moisture levels, temperature, and salinity. The data is passed from the nodes to a processing module, which collects and analyses the critical parameters directly related to plant growth, water management, and fertiliser optimisation. A mobile interface assists the farmers and stakeholders with the required information, such as field maps, real-time soil health indicators, and critical alerts related to drought, salinity stress, or pathogen hotspots. The proposed system forms as a multi-dimensional soil profiling tool capable of supporting precision agriculture. Most existing soil monitoring systems rely on environmental parameters, while the proposed system allows the continuous tracking of ecological and microbial dynamics in the area. The mesh network architecture helps the system to be redundant and enhances the outcomes. The proposed system helps with sustainable agriculture and improves the yields with minimal environmental degradation, enabling an adaptive and precise farm management system.

Plant disease has a tremendous impact on global food security, and Colletotrichum spp. caused bitter rot is a greater challenge to post-harvest quality. Conventional diagnosis is precise but invasive and therefore inappropriate for real-time purposes. This study investigates optical coherence tomography (OCT) as a high-resolution, non-invasive imaging method to detect internal structural changes from disease progression. The developed OCT-based image analysis framework stages diseases by assessing morphological degradation. The discovery of unique oval-shaped internal features, invisible to other non-invasive methods, demonstrates OCT’s potential for early detection, accurate monitoring, and real-time application in precision agriculture.

UAV-mounted multispectral sensors are widely used to study crop health. Utilising the same cameras to capture close-up images of crops can significantly improve crop health evaluations through multispectral technology. Unlike RGB cameras that only detect visible light, these sensors can identify additional spectral bands in the red-edge and near-infrared (NIR) ranges. This enables early detection of diseases, pests, and deficiencies through the calculation of various spectral indices. In this work, the ability to use UAV-multispectral sensors for close-proximity imaging of crops was studied. Images of plants were taken with a Micasense Rededge-MX from top and side views at a distance of 1 m. The camera has five sensors that independently capture blue, green, red, red-edge, and NIR light. The slight misalignment of these sensors results in a shift in the swath. This shift needs to be corrected to create a proper layer stack that could allow further processing. This research utilised the Oriented FAST and Rotated BRIEF (ORB) method to detect features in each image. Random sample consensus (RANSAC) was used for feature matching to find similar features in the slave images compared to the master image (indicated by the green band). Utilising homography to warp the slave images ensures their perfect alignment with the master image. After alignment, the images were stacked, and the alignment accuracy was visually checked using true colour composites. The side-view images of the plants were perfectly aligned, while the top-view images showed errors, particularly in the pixels far from the centre. This study demonstrates that UAV-mounted multispectral sensors can capture images of plants effectively, provided the plant is centred in the frame and occupies a smaller area within the image.

Nowadays, agriculture is facing significant challenges, including climate change. Precision agriculture might address these issues by optimizing resource use and promoting sustainability. In this work, a case study of tomato crop monitoring is presented, employing the large amount of gas sensor data collected over three years (2020–2022) to develop models for phenological phase classification. A k-NN classifier achieved accuracies above 99% across multiple train/test splits, with AUC, sensitivity, specificity, precision and F1-score above 98%. Results demonstrate the feasibility of low-computational-cost systems capable of real-time detection of the transition point between plants’ developmental stages.

Agriculture 4.0 integrates smart technologies to optimize agricultural management. This work proposes the development of a low-cost remote sensing module for small producers in the north of Paraná, Brazil, using the STM32F411CEU6 microcontroller and the nRF24L01+ module for mesh communication. The system measures temperature, humidity, and pressure using DS18B20, BME280, and capacitive soil moisture sensors via I²C, SPI, and ADC. Powered by a solar cell and Li-Po battery, along with a charge controller, the module acts as a transceiver, sending data to a gateway where it can be stored and analyzed, democratizing access to technology and supporting decision-making in crop management.

Efficient grain storage is critical for ensuring food security, particularly in regions with hot and humid climates where environmental fluctuations can accelerate spoilage. This study presents the development of a low-cost, Arduino-based Smart Grain Monitoring System designed to continuously monitor key storage parameters. The system integrates sensors to measure temperature, relative humidity, air quality, and the weight of stored grains—factors essential for the early detection of microbial activity, fermentation, or structural degradation. Data is transmitted wirelessly in real time to a mobile application via the Blynk IoT platform, allowing for remote access, alerts, and trend analysis. The system is designed to be affordable, scalable, and easy to deploy in agricultural settings with limited infrastructure. To enhance mechanical performance and usability, the sensor system is housed in a reflective glass silo enclosure that provides both thermal insulation and visual grain access. A 3D CAD model was developed to optimize the placement of electronics and ensure structural integrity. Key features include custom mounts for sensors and electronics, a top lid for grain refill and hygiene, and a stable base for load cell installation. This integrated framework offers a reliable, real-time monitoring solution that supports proactive grain management and reduces post-harvest losses in rural storage environments.

Phased array radar, with its electronic scanning, high reliability, and multifunctionality, has become a core equipment for unmanned aerial vehicle detection, modern air defense, meteorological monitoring, and satellite communication. The T/R module is the core equipment of active phased array radar, and its performance largely determines the performance of the phased array. At the same time, the application scenario requires relatively high transmission gain and transmission power, so attention should be paid to its heating situation. In addition, the overall size requirements for components are gradually becoming stricter, and miniaturization has become a trend in the development of T/R modules. This paper presents a four channel T/R module in X-band based on LTCC technology. In order to reduce weight and have high-density electronic devices, this module uses the latest technologies such as low-temperature cofired ceramic substrate (LTCC), Monolithic Microwave Integrated Chip (MMIC), MIC assembly process, and are hermetically sealed. The transmission channel of this module has high gain and high power, and the RF signal is transmitted through an eight layers LTCC board to reduce interference between adjacent signal transmission lines and reduce the module size at the same time. The method of dividing the transmission and reception channels using a metal shell frame reduces crosstalk between the input and output ports of the transmission channel. Good heat dissipation design ensures the thermal management of the module. The test results show that the size of the TR module is 70 mm * 55 mm * 10 mm, the transmission power is ≥ 39 dBm, the reception gain is >28 dB, and the noise figure is <3 dB.

This study investigates the machining challenges of fiber-reinforced composite materials (FRCMs), focusing on carbon fiber-reinforced polymer (CFRP) plates, which exhibit high abrasiveness, delamination tendency, and accelerated tool wear. Two solid carbide helical end mills, designed for composite machining, were evaluated through helical interpolation drilling. Acoustic emission signals were continuously acquired via a piezoelectric sensor during standardized cycles, and tool wear was assessed using confocal microscopy and a digital altimeter. Signal processing played a central role, combining energy-based metrics and damage indices to identify the onset of wear and early delamination, enhancing the understanding of tool degradation and improving machining reliability.

Monitoring humidity is essential for the protection and long-term preservation of historical monuments and cultural heritage structures, particularly those made of stone, marble, or iron. Excess moisture can accelerate material degradation and compromise structural integrity. This paper presents an alternative, low-cost method for enhancing the sensitivity of a raindrop sensor, aiming to detect micro-droplets such as early morning dew—an important factor in environmental monitoring around such sensitive sites. The proposed method involves covering the sensor’s surface with a fine nylon mesh, such as a stocking, which allows tiny water droplets to accumulate and spread more effectively across the sensor. This modification improves the electrical conductivity between the copper tracks when droplets are present, enabling the sensor to detect moisture levels that would otherwise go unnoticed. Experimental results demonstrate that the modified sensor performs significantly better than the original, unaltered version, offering greater sensitivity and consistency in its readings. The sensor responds more reliably to low volumes of moisture without requiring internal changes to its circuitry, making it both practical and cost-effective. The outcomes of this work are encouraging, suggesting that the approach is suitable for moisture detection in both research and real-world conservation scenarios. It provides a simple and scalable solution for integrating humidity monitoring into broader environmental sensing systems.

Infrared (IR) proximity sensors measure distance using either time-of-flight (ToF) or reflection intensity methods. While ToF offers higher precision, it requires costly, specialized components. Reflection based sensors use simpler circuits, enabling lower-cost designs. This study presents a multi-emitter reflection-intensity IR sensor as an economical alternative to near field object positioning. Six IR LEDs, sequentially driven, surround a central photodiode that captures backscattered signals. A machine-learning pipeline estimates object coordinates, cross section and height. Tested on 20 objects and 13,750 labeled data, the system achieved <1 cm mean positioning error, competitive to multi-zone ToF accuracy with reduced cost.