Heavy metal ions, such as Cd, Hg, Pb, and As, tend to persist in soil without natural degradation and can be absorbed by crops, leading to the accumulation of agricultural products that pose a significant threat to human health. However, the development of a rapid and efficient technique for identifying heavy metals in agricultural products is essential to ensure health and safety. With the knowledge of the extent of damage caused by heavy metals, it becomes imperative to detect the presence of cadmium in the soil, water, and the environment. This study introduces a novel plate approach for quick and precise colorimetric detection of cadmium ions using the Cd(II)-Chrome Azurol S CAS-2,2 '-dipyridyl dipy-Cetylpyridinium Bromide CPB quaternary complex. Our innovative method has shown that at a reaction solution pH of 11, the optimal concentration ratio is CAS (5 x 10-3 M): dipy (0.1 M): CPB (1.0 x 10-3 M) = 4 mL: 1 mL: 1 mL. The most significant fading alert was observed when the ethylenediaminetetraacetic acid (EDTA) chelator was added dropwise to the CAS detection plate, indicating strong chelation of Cd by EDTA. This laboratory-based study established a foundation for future applications in real environmental sample analysis.

Background: Herbicides are chemical agents that promote plant and crop growth by killing weeds and other pests. However, unconsumed and excessively used herbicides may enter groundwater and agricultural areas, damaging water, air, and soil resources. Mesotrione (MT) is an extensively used herbicide to cultivate corn, sugarcane, and vegetables. Excessive consumption of MT residues pollutes the soil, water, and environmental systems. Methods: Henceforth, the potential electrocatalyst of the tungsten trioxide nanorods on the carbon microsphere (WO3/C) composite was synthesized for nanomolar electrocatalytic detection of MT. The electrocatalysts of WO3/C were synthesized hydrothermally, and the WO3/C composite was in-situ constructed by using the reflux method. Significant findings: Remarkably, the as-prepared WO3/C composite displayed a fantastic sensing platform for MT, characterized by an astonishingly nanomolar detection limit (10 nm), notable sensitivity (1.284 mu A mu M-1 cm-2), exceptional selectivity, and amazing stability. The actual sample test was carried out using MT added in food and environmental samples of corn, sugar cane, sewage water, and river water. The minimum MT response recovery in vegetable and water samples was determined to be approximately 97 % and 99 %, respectively. The results indicate that the WO3/C composite is an effective electrode material for real-time MT measurement in portable devices.

Plant-parasitic nematodes pose a silent yet devastating threat to global agriculture, causing significant yield losses and economic damage. Traditional detection methods such as soil sampling, microscopy, and molecular diagnostics are slow, labor-intensive, and often ineffective in early-stage infestations. Nano biosensors: cuttingedge analytical tools that leverage nanomaterials like carbon nanotubes, graphene, and quantum dots to detect nematode-specific biochemical markers such as volatile organic compounds (VOCs) and oesophageal gland secretions, with unprecedented speed and accuracy. The real breakthrough lies in the fusion of artificial intelligence (AI) and nano-biosensor technology, forging a new frontier in precision agriculture. By integrating AI's powerful data analysis, pattern recognition, and predictive capabilities with the extraordinary sensitivity and specificity of nano-biosensors, it becomes possible to detect biomolecular changes in real-time, even at the earliest stages of disease progression. AI-driven nano biosensors can analyze real-time data, enhance detection precision, and provide actionable insights for farmers, enabling proactive and targeted pest management. This synergy revolutionizes nematode monitoring, paving the way for smarter, more sustainable agricultural practices. This review explores the transformative potential of AI-powered nano-biosensors in advancing precision agriculture. By integrating these technologies with smart farming systems, we move closer to real-time, costeffective, and field-deployable solutions, ushering in a new era of high-tech, eco-friendly crop protection.

Ultrasonic guided waves are widely used in the nondestructive testing (NDT) of aboveground pipelines. However, their application in buried pipeline inspection is significantly hindered by severe soil-induced attenuation. This study proposes a method for detecting defects in buried pipelines using nonlinear chirp signals encoded with orthogonal complementary Golay code pairs. By adjusting the proportion of low-frequency and high-frequency components in the excitation signal, the attenuation of guided waves in buried pipelines is effectively reduced. Meanwhile, the use of coded sequences increases the energy of the excitation signal, and the excellent autocorrelation properties of broadband signals enhance the time-domain resolution of defect echoes. The fundamental principles of coded excitation based on nonlinear chirp signals and pulse compression methods are first introduced. MATLAB simulations are then employed to verify the approach's effectiveness in the characterization of defect echoes under various conditions and signal-to-noise ratios (SNR). A subsequent comparative analysis, using finite element (FE) simulations for buried pipelines, demonstrates that nonlinear chirp signals with a higher proportion of low-frequency components exhibit better resistance to attenuation. By fine-tuning the chirp parameters, higher defect reflectivity can be achieved than with conventional tone bursts for various defect types in buried pipelines. FE simulations further illustrate the superiority of the proposed method over tone bursts in terms of excitation signal amplitude, defect echo reflectivity, and defect location accuracy. Finally, defect detection experiments on buried pipelines with multiple defects confirm that the nonlinear chirp signals with carefully selected parameters exhibit lower attenuation rates. In the same testing environment, the coded nonlinear chirp signals outperform tone bursts by providing higher excitation amplitudes, greater defect echo reflectivity with an increase of up to 81.45 percent, and enhanced time-domain resolution. The proposed method effectively reduces ultrasonic guided wave attenuation in buried pipelines while increasing defect echo reflectivity and extending the effective detection range.

Arsenic (As) contamination of soil and groundwater poses a huge threat to world health by polluting food systems and causing major health problems, such as cancer, cardiovascular disease,skin lesions,kidney damage and other serious health problems. In recent years, there has been a lot of effort into designing, synthesizing, and developing chemosensors for arsenic species. Chemosensors containing heteroatoms such as oxygen, nitrogen, and sulfur provide coordination sites for metal ion detection. This study investigates the study of organic compounds for the fluorimetric and colorimetric detection of As ions in biological, agricultural, and environmental samples. These chemosensors are based on the skeleton of Schiff bases, thiourea, and pyridine. By comparing their identification capabilities, we hope to guide the development of future arsenic chemosensors that are efficient, sensitive, and selective, leading to more accessible methods for arsenic monitoring in a variety of real-world applications.

Background: The detection of metal ions represents a critical analytical challenge due to their persistent environmental accumulation and severe toxic effects on ecosystems and human health. Even at trace concentrations, toxic metal ions can cause irreversible biological damage, necessitating the development of sensitive, selective, and rapid monitoring platforms. Advanced detection systems are urgently needed for environmental surveillance, industrial effluent control, and food/water safety applications where regulatory compliance and early warning capabilities are paramount. Results: This work presents an etching-based sensor array to identify and discriminate Pb2+, Hg2+, Cu2+, NO2-, Cr6+, and As3+ as hazardous ions. Au@Ag core@shell nanorods were utilized as sensing elements in different pH values in the presence of thiosulfate and thiourea as key elements in the oxidation of nanoparticles. Analytes' response patterns in the range of 1.0-30 mu M were analyzed via various methods, including heatmap, bar plot, and linear discriminant analysis (LDA), showing perfect discrimination. To ensure the sensor's applicability in real samples, we conducted meticulous testing on different sources, including tap water, well water, tilapia pond water, tomato soil extract, and urine samples. Significance: The sensor demonstrated excellent performance in classifying mixture samples and providing precise and accurate detection in real samples. This innovation offers a promising future for etching-based sensor arrays by utilizing core-shell nanoparticles as sensitive sensing elements and a significant contribution to global efforts in safeguarding public health and the environment from the threat of pollutants.

Solar panels are essential for converting sunlight into electricity. Still, environmental factors can significantly compromise their efficiency and performance, particularly the accumulation of soiling on their surfaces or damage. This study proposes a hybrid model comprising an ensemble of deep-learning models to distinguish between soiled and damaged solar panels and their corresponding conditions. Our approach utilizes pretrained deep learning models, fine-tuned for detecting soiling or damage on photovoltaic (PV) panels, to extract relevant features and build efficient classifiers. Introducing a post-processing ensemble model improves classification metrics compared to a single deep-learning model. Combining Convolutional Neural Networks and Vision Transformers in an ensemble model achieves the highest accuracy, with 96.3% for damage and soiling detection and 91.8% for damage and soiling type identification. These results significantly outperform one-tier deep learning models, which attain an accuracy of 87.7% when classifying all possible damage and soiling categories.

In this paper, a comprehensive overview was conducted on machine vision in potato cultivation, harvesting, and storage. Common weeds and diseases encountered during potato cultivation were summarized, and the advantages and disadvantages of various detection methods were compared. Additionally, methods for soil clod separation and tuber damage detection during harvesting were reviewed, along with a comparative analysis of their strengths and weaknesses. Furthermore, the defect grading and sprouting detection methods during storage were discussed. While machine vision technology shows good detection ability in potato cultivation, harvesting, and storage, further research is still needed to enhance the accuracy and adaptability of these methods, ultimately promoting the development of the potato industry.

Accurate determination of potassium ion (K+) concentration in fingertip blood, soil pore water, pipette solution, and sweat is crucial for performing biological analysis, evaluating soil nutrients levels, ensuring experimental precision, and monitoring electrolyte balance. However, current electrochemical K+ sensors often require large sample volumes and oversized reference electrodes, which limits their applicability for the aforementioned small-volume samples. In this paper, a K+ sensor integrated with a glass capillary and a spiral reference electrode was proposed for detecting K+ concentrations in small-volume samples. A K+-selective membrane (K+-ISM)/ reduced graphene oxide-coated acupuncture needle (working electrode) was spirally wrapped with a chitosangraphene/AgCl-modified Ag wire (reference electrode). This assembly was then inserted into a glass capillary, forming an anisotropic diffusion region of an annular cylindrical gap with width 410 mu m and height 20 mm. It was found that the capillary action of the glass capillary results in a raised liquid level of the sample inside it compared to that in the container, which promotes efficient contact between the small-volume sample and the K+ sensor. Besides, the formed anisotropic diffusion region limits the K+ diffusion from the bulk solution to the K+ISM, which leads to a larger potentiometric response of the K+-ISM. The glass capillary-assembled K+ sensor displays high performance, including a sensitivity 58.3 mV/dec, a linear range 10_ 5-10_ 1 M, and a detection limit 1.26 x 10_6 M. Moreover, it reliably determines K+ concentrations in artificial sweat of microliter volume. These results facilitate accurate detection of K+ concentration in fingertip blood, soil pore water, and pipette solution.

Key messageIntegrating airborne laser scanning and satellite time series data across the forest rotation enhances decision-making in precision forestry. This review supports forest managers by illustrating practical applications of these remote sensing technologies at different stages of intensive forest plantation management-such as site assessment, monitoring, and silviculture-helping improve productivity, sustainability, and operational efficiency.ContextIntensively managed forest plantations depend on high-resolution, timely data to guide silviculture and promote sustainability.AimsThis review explores how airborne laser scanning (ALS) and satellite time series data support precision forestry across key stages, including site assessment, establishment, monitoring, inventory updates, growth tracking, silvicultural interventions, and harvest planning.ResultsThe review highlights several key applications. ALS-derived digital elevation models and canopy metrics improve site productivity estimation by capturing micro-topographic variables and soil formation factors. Combining ALS with multispectral data enhances monitoring of seedling survival and health, although distinguishing seedlings from non-living components remains a challenge. ALS-based Enhanced Forest Inventories provide spatially detailed forest metrics, while satellite time series and vegetation indices support continuous monitoring of growth and early detection of drought, fire, and pest stress. ALS individual tree detection models offer insights into competition, stand structure, and spatial variability, informing thinning and fertilization decisions by identifying trees under stress or with high growth potential. These models also help mitigate drought and wind damage by guiding density and canopy structure management. ALS terrain data further support harvest planning by optimizing machinery routes and reducing environmental impacts.ConclusionDespite progresses, challenges remain in refining predictive models, expanding remote sensing applications, and developing tools that translate complex data into field operations. A major barrier is the technical expertise needed to interpret spatial data and integrate remote sensing into workflows. Continued research is needed to improve accessibility and operational relevance. High-resolution data still offer strong potential for adaptive management and sustainability.