Search

This paper presents a novel methodology for plant disease detection using YOLOv8 (You Only Look Once version 8), a state-of-the-art object detection model designed for real-time image classification and recognition tasks. The proposed approach involves training a custom YOLOv8 model to detect and classify various plant conditions accurately. The model was evaluated using a testing subset to measure its performance in detecting different plant diseases. To ensure the model’s robustness and generalizability beyond the training dataset, it was further tested on a set of unseen images sourced from Google Images. This additional testing aimed to assess the model’s effectiveness in real-world scenarios, where it might encounter new data. The evaluation results were auspicious, demonstrating the model’s capability to classify plant conditions, such as diseases, with high accuracy. Moreover, the use of YOLOv8 offers significant improvements in speed and precision, making it suitable for real-time plant disease monitoring applications. The findings highlight the potential of this methodology for broader agricultural applications, including early disease detection and prevention.

Abstract Hematite (Fe2O3) is one of the best candidates for photoelectrochemical water splitting due to its abundance and suitable bandgap. However, its efficiency is mostly impeded due to the intrinsically low conductivity and poor light absorption. In this study, we targeted this intrinsic behavior to investigate the thermodynamic stability, photoconductivity and optical properties of rhodium doped hematite using density functional theory. The calculated formation energy of pristine and rhodium doped hematite was − 4.47 eV and − 5.34 eV respectively, suggesting that the doped material is thermodynamically more stable. The DFT results established that the bandgap of doped hematite narrowed down to the lower edge (1.61 eV) in the visible region which enhanced the optical absorption and photoconductivity of the material. Moreover, doped hematite has the ability to absorb a broad spectrum (250–800) nm. The enhanced optical absorption boosted the photocurrent and incident photon to current efficiency. The calculated results also showed that the incorporation of rhodium in hematite induced a redshift in optical properties.

Magnetic molecularly imprinted polymers (MMIPs) contain the predesigned specialized recognition capability that can be chosen to build credible functional materials, that are easy to handle and have a good degree of specificity. Hence, the given piece of work is intended to design a novel electrochemical sensor incorporating magnetite-based molecularly imprinted polymers. The building materials consisted of a cross-linker (EGDMA), reaction-initiator (AIBN), monomer (methylene succinic acid-MSA), and template molecule (Sunset Yellow-SY dye). MMIPs exhibited a diameter of 57 nm with an irregular shape due to the presence of cavities based on SEM analysis. XRD patterns exhibited crystallinity, as well as amorphous peaks that are attributed to polymeric and non-polymeric frameworks of MMIPs. The crystallite size of the MMIPs from XRD analysis was found to be 16.28 nm based on the Debye-Scherrer’s equation. Meanwhile, the FTIR bands showed the synthesis of MMIPs using monomer and methylene succinic acid. The sorption data at the optimized operating conditions (pH 2, sorbent dosage 3 mg, time 18 min) showed the highest sorption capacity of 40 mg/g. The obtained data best fitted to the Langmuir sorption isotherm and followed the pseudo-second-order kinetics. The magneto-sensors were applied for ultrasensitive, rapid, and simple sensing of SY dye. The electrochemical experiments were run at the operating condition range of (scan rate 10–50 mV/s, tads 0–120 s, pH 5–9, potential range 1–1.5 V for CV and 1–1.3 V for SWAdASV). The linear range of detection was set to 1.51 × 10−6 M to 1.51 × 10−6 M posing LOD and LOQ values of 8.6242 × 10−5 M and 0.0002874 M, respectively. The regression analysis value for the calibration was found to be 0.950. Additionally, high adsorption efficiency, selectivity, reusability, and strong structural stability of the magneto-sensors showed potential use for SY detection in real samples. These characteristics make MMIPs a viable electrochemical substrate for the detection of chemical contaminants in the environment and in health-related products.

Carbon nanotubes (CNTs) are known as nano-architectured allotropes of carbon, having graphene sheets that are wrapped forming a cylindrical shape. Rolling of graphene sheets in different ways makes CNTs either metals or narrow-band semiconductors. Over the years, researchers have devoted much attention to understanding the intriguing properties CNTs. They exhibit some unusual properties like a high degree of stiffness, a large length-to-diameter ratio, and exceptional resilience, and for this reason, they are used in a variety of applications. These properties can be manipulated by controlling the diameter, chirality, wall nature, and length of CNTs which are in turn, synthesis procedure-dependent. In this review article, various synthesis methods for the production of CNTs are thoroughly elaborated. Several characterization methods are also described in the paper. The applications of CNTs in various technologically important fields are discussed in detail. Finally, future prospects of CNTs are outlined in view of their commercial applications.