Efficient water management is crucial for sustainable sugarcane farming, particularly in regions with water scarcity. This study investigated the implementation of an AIoT-based water management system designed to optimize irrigation in sugarcane fields. By integrating Artificial Intelligence (AI) with Internet of Things (IoT) technology, the system leverages real-time data from sensors monitoring soil moisture, weather conditions, and plant health. The AI component employs Long Short-Term Memory (LSTM) algorithms to predict optimal irrigation schedules, ensuring precise water delivery tailored to the crop's growth stages. Additionally, the system utilizes solar-powered pumps to enhance sustainability by reducing reliance on non-renewable energy sources. Preliminary results indicated that the AloT system significantly improved water use efficiency, crop yields, and operational cost-effectiveness. This research highlights the potential of AIoT solutions in addressing water management challenges in agriculture, promoting both productivity and environmental conservation.

Rapid and reliable detection of pesticide residues in vegetables is essential for food safety and sustainable agriculture. This work presents a four-port closed-loop ring resonator (CLRR) sensor for quantitative detection of carbamate residues in leafy vegetables. Operating through the S₃₁ transmission path, the sensor enhances electric-field coupling and sensing resolution in the high-field region. Four resonance modes were identified at 1.05, 2.10, 3.12, and 4.11 GHz, with the third mode (3.12 GHz) showing the most stable and linear response. Vegetable extracts of Chinese kale and Choy sum were prepared with carbamate concentrations of 0–8% (w/v). Increasing concentration caused a red-shift in resonance frequency corresponding to a reduction in dielectric constant. Regression analysis revealed a strong linear correlation between frequency shift and concentration (R² = 0.9855–0.9988). The CLRR achieved average normalized sensitivities of 6.39% and 6.54% per unit dielectric variation, outperforming most planar and metamaterial sensors. Fabricated on a single-layer FR-4 substrate, the sensor combines high sensitivity, low cost, and excellent repeatability, offering a practical, label-free, non-destructive tool for on-site monitoring of pesticide contamination in leafy vegetables.

Liver fluke infections, especially those induced by Opisthorchis viverrini, pose considerable health and economic difficulties in aquaculture, particularly in Southeast Asia. Traditional approaches for parasite elimination, including chemical treatments and freezing, exhibit constraints regarding efficacy, environmental sustainability, and practicality. This research investigates an improved dielectric heating system utilizing a 2.45 GHz horn antenna for the selective thermal targeting of parasite-associated regions in Cirrhinus microlepis (small-scale mud carp). The dielectric characteristics of fish tissues, encompassing scales, skin, and muscle, were analyzed utilizing an open-ended coaxial probe technique. Simulation and experimental evaluations were performed to improve energy absorption, heating uniformity, and a particular absorption rate to enable precise thermal localization while preserving the integrity of fish tissue. The findings demonstrate that dielectric heating can specifically elevate the temperature of fish scales, where parasites predominantly inhabit, to levels beyond 70 degrees Celsius, while reducing thermal impact on the underlying muscle tissue. The application of a salt coating on fish scales markedly increased their dielectric loss, exceeding that of muscle tissue, thus enhancing selective heating efficiency and supporting targeted thermal treatment. The ideal distance from the antenna to the sample was established as ranging from 6 to 9 cm, ensuring a balance between energy efficiency and homogeneous heating. This work illustrates the efficacy of dielectric heating as a novel and non-chemical approach for thermal management of parasite-prone tissues in aquaculture, providing a sustainable and viable substitute for traditional treatments.

This work presents a study of the ring resonator circuit, the objective was to develop a microwave sensor that would detect pesticide residues. A closed-ring structure with two transmission lines is presented. It concentrates on the transmission coefficient's resonance frequency response analysis when tested with organic vegetable juice. The results indicated that resonance frequency had changed to a lower frequency as a resultant shift in the electrical permittivity of the test materials and the characterization of the resonance frequency to a narrow band. It was a sensor property that qualifies it for its application as a detection device.

In this study, we present the design and analysis of high-gain InP-based hybrid plasmonic nanoantennas incorporating a SiO 2 -Graphene-Au stacked waveguide. The proposed nanoantenna leverages the superior optical properties of Indium Phosphide (InP), integrated with a hybrid waveguide structure consisting of SiO 2 , graphene, and gold (Au). The inclusion of SiO 2 serves as a low-loss dielectric layer, graphene enhances light-matter interactions, and Au provides strong plasmonic effects. Our simulation results, obtained using CST, demonstrate that the proposed design achieves a significant enhancement in gain and directivity compared to traditional configurations. Specifically, the proposed nanoantenna exhibits a remarkable gain of 13.27 dB at a resonant frequency of 193.45 THz (1550 nm), highlighting its potential for high-performance photonic applications.