Search Results (496)

Groundwater plays a crucial role in sustaining agriculture and livelihoods in the arid Middle Draa Valley (MDV) of southeastern Morocco. However, increasing groundwater extraction, declining rainfall, and the absence of effective floodwater harvesting systems have led to severe aquifer depletion. This study applies and compares six machine learning (ML) algorithms—decision trees (CART), ensemble methods (random forest, LightGBM, XGBoost), distance-based learning (k-nearest neighbors), and support vector machines—integrating GIS, satellite data, and field observations to delineate zones suitable for groundwater recharge. The results indicate that ensemble tree-based methods yielded the highest predictive accuracy, with LightGBM outperforming the others by achieving an overall accuracy of 0.90. Random forest and XGBoost also demonstrated strong performance, effectively identifying priority areas for artificial recharge, particularly near ephemeral streams. A feature importance analysis revealed that soil permeability, elevation, and stream proximity were the most influential variables in recharge zone delineation. The generated maps provide valuable support for irrigation planning, aquifer conservation, and floodwater management. Overall, the proposed machine learning–geospatial framework offers a robust and transferable approach for mapping groundwater recharge zones (GWRZ) in arid and semi-arid regions, contributing to the achievement of Sustainable Development Goals (SDGs))—notably SDG 6 (Clean Water and Sanitation), by enhancing water-use efficiency and groundwater recharge (Target 6.4), and SDG 13 (Climate Action), by supporting climate-resilient aquifer management. Full article

Free-space optical (FSO) communications have emerged as a promising complement to conventional radio-frequency (RF) systems due to their high bandwidth, low interference, and license-free spectrum. Visible-light FSO communication, using laser diodes or LEDs, offers potential for short-range data links, but performance is highly wavelength-dependent under varying atmospheric conditions. This study presents an experimental evaluation of three visible laser diodes at 650 nm (red), 532 nm (green), and 405 nm (violet), focusing on their optical output power, quantum efficiency, and modulation behavior across a range of driving currents and frequencies. A custom laboratory testbed was developed using an Atmega328p microcontroller and a Visual Basic control interface, allowing precise control of current and modulation frequency. A silicon photovoltaic cell was employed as the optical receiver and energy harvester. The results demonstrate that the 650 nm red laser consistently delivers the highest quantum efficiency and optical output, with stable performance across electrical and modulation parameters. These findings support the selection of 650 nm as the most energy-efficient and versatile wavelength for short-range, cost-effective visible-light FSO communication. This work provides experimentally grounded insights to guide wavelength selection in the development of energy-efficient optical wireless systems. Full article

Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems. Full article

Sources of renewable energy have attracted considerable attention. Their expanded use will have a substantial impact on both the cost of energy production and climate change. Solar energy is one efficient and safe option; however, solar energy harvesting sites, irrespective of the location, can impact the ecosystem. This experimental study explores the energy available inside and outside of novel miniature energy harvesting cages by measuring light intensity and power generated. Varying light intensity outside the cage has been utilized to study the remaining energy inside the cage of a flexible design, where the heights of the harvesting panels are parameters. Cages are built from custom photovoltaic panels arranged in a staircase manner to provide access to growing plants. The balance between power generation and biological development is investigated. Two different structures are presented to explore the variation of illumination intensity inside the cages. The experimental results show a substantial reduction in energy inside the cages. The experimental results showed up to 24% reduction in illumination inside the cages in winter. The reduction is even larger in summer, up to 57%. The results from the models provide a framework to study the possible impact on a biological system residing inside the cages, paving the way for practical farming with sustainable energy harvesting. Full article

This Review examines up-to-date advancements in the integration of biomolecules and solar energy technologies, with a particular focus on biohybrid photovoltaic systems. Biomolecules have recently garnered increasing interest as functional components in a wide range of solar cell architectures, since they offer a huge variety of structural, optical, and electronic properties, useful to fulfill multiple roles within photovoltaic devices. These roles span from acting as light-harvesting sensitizers and charge transport mediators to serving as micro- and nanoscale structural scaffolds, rheological modifiers, and interfacial stabilizers. In this Review, a comprehensive overview of the state of the art about the integration of biomolecules across the various generations of photovoltaics is provided. The functional roles of pigments, DNA, proteins, and polysaccharides are critically reported improvements and limits associated with the use of biological molecules in optoelectronics. The molecular mechanisms underlying the interaction between biomolecules and semiconductors are also discussed as essential for a functional integration of biomolecules in solar cells. Finally, this Review shows the current state of the art, and the most significant results achieved in the use of biomolecules in solar cells, with the main scope of outlining some guidelines for future further developments in the field of biohybrid photovoltaics. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

Over efficient photocatalysts, CO₂ photoreduction typically converts CO₂ into low-carbon chemicals, which serve as raw materials for downstream synthesis processes. Here, an efficient composite photocatalyst heterojunction (Cu₂O-u/g-C₃N₄) has been fabricated to reduce CO₂. Graphitic carbon nitride (g-C₃N₄) was synthesized via thermal polymerization of urea at 550 °C, while pre-dispersed Cu₂O derived from urea pyrolysis (Cu₂O-u) was prepared by thermal reduction of urea and CuCl₂·2H₂O at 180 °C. The heterojunction Cu₂O-u/g-C₃N₄ was subsequently constructed through hydrothermal treatment at 180 °C. This heterojunction exhibited a bandgap of 2.10 eV, with dual optical absorption edges at 485 nm and above 800 nm, enabling efficient harvesting of solar light. Under 175 W mercury lamp irradiation, the heterojunction catalyzed liquid-phase CO₂ photoreduction to formic acid, acetic acid, and methanol. Its formic acid production activity surpassed that of pristine g-C₃N₄ by 3.14-fold and TiO₂ by 8.72-fold. Reaction media, hole scavengers, and reaction duration modulated product selectivity. In acetonitrile/isopropanol systems, formic acid and acetic acid production reached 579.4 and 582.8 μmol·h⁻¹·g_cat⁻¹. Conversely, in water/triethanolamine systems, methanol production reached 3061.6 μmol·h⁻¹·g_cat⁻¹, with 94.79% of the initial conversion retained after three cycles. Finally, this work ends with the conclusions of the CO₂ photocatalytic reduction to formic acid, acetic acid, and methanol, and recommends prospects for future research. Full article

Recently, solar energy technologies are a cornerstone of the global effort to transition towards cleaner and more sustainable energy systems. However, in many rural areas of developing countries, unreliable electricity severely impacts healthcare delivery, resulting in reduced medical efficiency and increased risks to patient safety. This review explores the transformative potential of solar energy as a sustainable solution for powering healthcare facilities, reducing dependence on fossil fuels, and improving health outcomes. Consequently, energy harvesting is a vital renewable energy source that captures abundant solar and thermal energy, which can sustain medical centers by ensuring the continuous operation of life-saving equipment, lighting, vaccine refrigeration, sanitation, and waste management. Beyond healthcare, it reduces greenhouse gas emissions, lowers operational costs, and enhances community resilience. To address this issue, the paper reviews critical solar energy technologies, energy storage systems, challenges of energy access, and successful solar energy implementations in rural healthcare systems, providing strategic recommendations to overcome adoption challenges. To fulfill the aims of this study, a focused literature review was conducted, covering publications from 2005 to 2025 in the Scopus, ScienceDirect, MDPI, and Google Scholar databases. With targeted investments, policy support, and community engagement, solar energy can significantly improve healthcare access in underserved regions and contribute to sustainable development. Full article

To collect the energy generated by rotational motion in the natural environment, a piezoelectric electromagnetic hybrid energy harvester (HEH) based on a planetary gear system is proposed. The harvester combines piezoelectric and electromagnetic effects and is mainly used for collecting low-frequency rotational energy. The HEH has a compact structure and contains four sets of piezoelectric energy harvesters (PEHs) and electromagnetic energy harvesters (EMHs) inside. The working principle of the energy harvester is analyzed, its theoretical model is established, and a simulation analysis is conducted. To verify the effectiveness of the design, an experimental device is constructed. The results indicate that the HEH can generate an average output power of 250 mW under eight magnets and an external excitation frequency of 7 Hz. In actual power supply testing, the HEH can light up 60 LEDs and provide stable power supply for the temperature–humidity meter. Full article

Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried out even before flowering, provides a valuable foundation for estimating potential yield far in advance of veraison. Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages. This study presents a deep learning-based approach for detecting grapevine inflorescences and bunches during early development, assessing how phenological stage and illumination conditions influence detection performance using the YOLOv11 architecture under commercial field conditions. A total of 436 RGB images were collected across two phenological stages (pre-bloom and fruit-set), two lighting conditions (daylight and artificial night-time illumination), and six grapevine cultivars. All images were manually annotated following a consistent protocol, and models were trained using data augmentation to improve generalization. Five models were developed: four specific to each condition and one combining all scenarios. The results show that the fruit-set stage under daylight provided the best performance (F1 = 0.77, R² = 0.97), while for inflorescences, night-time imaging yielded the most accurate results (F1 = 0.71, R² = 0.76), confirming the benefits of artificial lighting in early stages. These findings define optimal scenarios for early-stage organ detection and support the integration of automated detection models into vineyard management systems. Future work will address scalability and robustness under diverse conditions. Full article

Topping is an essential step in cotton cultivation in Xinjiang, China, which can effectively increase the number of bolls per plant and optimize the yield and quality. Paclobutrazol, as a common chemical topping agent for cotton, faces challenges such as unstable topping effect and limited leaf surface absorption during application. In this study, paclobutrazol was used as the ligand and a zinc complex was synthesized by the thermosolvent method to replace paclobutrazol and improve the topping effect on cotton. The structure of the complex was characterized using FTIR, UV-vis, TG, and XRD analyses. The results confirmed that each zinc ion coordinated with four nitrogen atoms from the triazole rings of paclobutrazol and two oxygen atoms from nitrate ions, forming an octahedral geometry. Surface tension measurement and analysis revealed that the complex had a surface tension reduction of 12.75 mN/m compared to paclobutrazol, thereby enhancing the surface activity of the complex in water systems and improving its absorption efficiency on plant leaves. Two-year field trials indicated that the foliar application of the complex at a dosage of 120 g·hm⁻² in inhibiting cotton plant height was more stable to that of paclobutrazol or mepiquat chloride. It also shortened the length of fruiting branches, making the shape of cotton plants compact, thereby indirectly improving the ventilation and light penetration of the cotton field and the convenience of mechanical harvesting. Yield data showed that, compared with artificial topping, the complex at a dosage of 120 g·hm⁻² treatment increased cotton yield by approximately 4.6%. Therefore, the paclobutrazol–zinc complex is a promising alternative to manual topping and have great application potential in future mechanized cotton production. Full article

The increasing demand for sustainable and renewable energy solutions has made radio frequency energy harvesting (RFEH) a promising technique for powering low-power electronic devices. RFEH captures ambient RF signals from wireless communication systems, such as mobile networks, Wi-Fi, and broadcasting stations, and converts them into usable electrical energy. This approach offers a viable alternative for battery-dependent and hard-to-recharge applications, including streetlights, outdoor night/security lighting, wireless sensor networks, and biomedical body sensor networks. This article provides a comprehensive review of the RFEH techniques, including state-of-the-art rectenna designs, energy conversion efficiency improvements, and multi-band harvesting systems. We present a detailed analysis of recent advancements in RFEH circuits, impedance matching techniques, and integration with emerging technologies such as the Internet of Things (IoT), 5G, and wireless power transfer (WPT). Additionally, this review identifies existing challenges, including low conversion efficiency, unpredictable energy availability, and design limitations for small-scale and embedded systems. A critical assessment of current research gaps is provided, highlighting areas where further development is required to enhance performance and scalability. Finally, constructive recommendations for future opportunities in RFEH are discussed, focusing on advanced materials, AI-driven adaptive harvesting systems, hybrid energy-harvesting techniques, and novel antenna–rectifier architectures. The insights from this study will serve as a valuable resource for researchers and engineers working towards the realization of self-sustaining, battery-free electronic systems. Full article

Fabricating efficient photocatalysts that can be used in solar-to-fuel conversion and to enhance the photochemical reaction rate is essential to the current energy crisis and climate changes due to the excessive usage of nonrenewable fossil fuels. To attain high photo-to-chemical conversion efficiency, it is important to fabricate cost-effective and durable catalysts with high activity. One-dimensional cadmium sulfides (1D CdS), with higher surface area, charge carrier separation along the linear direction, and visible light harvesting properties, are promising candidates for converting solar energy to H₂, reducing CO₂ to commodity chemicals, and remediating environmental pollutants. The main disadvantage of CdS is photocorrosion due to the leaching of S²⁻ ions during the photochemical reactions, and further charge recombination rate leads to low quantum efficiency. Therefore, the implementation of core–shell heterostructured morphology, i.e., the growth of the shell on the surface of the 1D CdS, which offers unique features such as protection of CdS from photocorrosion, a tunable interface between the core CdS and shell, and photogenerated charge carrier separation via heterojunctions, provides additional active sites and enhanced visible light harvesting. Therefore, the viability of the core–shell synthesis strategy and synergetic effects offer a new way of designing photocatalysts with enhanced stability and improved charge separation in solar energy conversion systems. This review highlights some critical aspects of synthesizing 1D CdS core–shell heterostructures, underlying reaction mechanisms, and their performance in photoredox reactions. Finally, some challenges and considerations in the fabrication of 1D CdS-based core–shell nanostructures that can overcome the current barriers in industrial applications are discussed. Full article

Current tomato harvesters rely primarily on external color as the sole indicator of ripeness. However, this approach often results in premature harvesting, leading to insufficient lycopene accumulation and a suboptimal nutritional content for human consumption. Such limitations are especially critical in controlled-environment agriculture (CEA) systems, where maximizing fruit quality and nutrient density is essential for both the yield and consumer health. To address that challenge, this study introduces a novel, multimodal harvest readiness framework tailored to nutrient film technology (NFT)-based smart farms. The proposed approach integrates plant-level stress diagnostics and fruit-level phenotyping using wearable biosensors, AI-assisted computer vision, and non-invasive physiological sensing. Key physiological markers—including the volatile organic compound (VOC) methanol, phytohormones salicylic acid (SA) and indole-3-acetic acid (IAA), and nutrients nitrate and ammonium concentrations—are combined with phenomic traits such as fruit color (a*), size, chlorophyll index (rGb), and water status. The innovation lies in a four-stage decision-making pipeline that filters physiologically stressed plants before selecting ripened fruits based on internal and external quality indicators. Experimental validation across four plant conditions (control, water-stressed, light-stressed, and wounded) demonstrated the efficacy of VOC and hormone sensors in identifying optimal harvest candidates. Additionally, the integration of low-cost electrochemical ion sensors provides scalable nutrient monitoring within NFT systems. This research delivers a robust, sensor-driven framework for autonomous, data-informed harvesting decisions in smart indoor agriculture. By fusing real-time physiological feedback with AI-enhanced phenotyping, the system advances precision harvest timing, improves fruit nutritional quality, and sets the foundation for resilient, feedback-controlled farming platforms suited to meeting global food security and sustainability demands. Full article

The accurate classification of banana ripeness is essential for optimising agricultural practices and enhancing food industry processes. This study investigates the classification of banana ripeness using Machine Learning (ML) and Deep Learning (DL) techniques. The dataset consisted of 1565 high-resolution images of bananas captured over a 20-day ripening period using a Canon EOS 90D camera under controlled lighting and background conditions. High-resolution images of bananas at different ripeness stages were classified into ‘unripe’, ‘ripe’, and ‘overripe’ categories. The training set consisted of 1398 images (89.33%), and the validation set consisted of 167 images (10.67%), allowing for robust model evaluation. Various ML models, including Decision Tree, Random Forest, KNN, SVM, CNN, and VGG models, were trained and evaluated for ripeness classification. Among these, DL models, particularly CNN and VGG, outperformed traditional ML algorithms, with the CNN and VGG achieving accuracy rates of 90.42% and 89.22%, respectively. These rates surpassed those of Decision Trees (71.86%), Random Forests (85.63%), KNNs (86.83%), and SVMs (89.22%). The study points out the importance of dataset quality, model selection, and preprocessing techniques in achieving accurate ripeness classification. Practical applications of these results include optimised harvesting practices, enhanced post-harvest handling, improved consumer experience, streamlined supply chain logistics, and automation in sorting systems. These results confirm the feasibility of using deep learning for the automated classification of ripening stages, with implications for reducing postharvest losses and improving supply chain logistics. These findings have significant implications for stakeholders in the banana industry, from farmers to consumers, and pave the way for the development of innovative solutions for banana ripeness classification. Full article