Search Results (8)

Hydro-cultural heritage is shaped by strong hydrological dependence and historical accessibility. To address insufficient identification of river-basin heritage linkages and their weak translation into graded protection, this study develops an analytical framework integrating heritage-site evaluation, cultural source identification, resistance-surface construction, potential corridor extraction, network grading, and protection guidance, and applies it to the Xiangjiang River Basin, China. Heritage sites were evaluated by protection level, historical continuity, spatial proximity, and hydro-cultural relevance. Cultural source areas were identified using weighted kernel density analysis, potential corridors were extracted using the minimum cumulative resistance model, and the graded corridor network was examined using network-structure indices. The results show river-oriented clustering, localized nucleation, and belt-like extension. Eight primary and fourteen supplementary cultural source areas were identified. Potential corridors are concentrated along the Xiangjiang main stem and major tributaries. In the resistance-surface construction, distance to the water system received the highest AHP-derived resistance weight, while GeoDetector showed that it had the highest, although modest, single-factor explanatory power among the tested variables for corridor spatial differentiation. The corridor network exhibits a primary–secondary–tertiary graded structure. This study reveals the spatial continuity and hierarchy of hydro-cultural heritage corridors and provides a methodological reference for river-basin conservation. Full article

When carbon quantum dots (CDs) are used to enhance photosynthesis, they inevitably enter the plant. However, the dose-dependent effects of CDs on plant growth are poorly understood. In this study, we investigated the dose-dependent effects of CDs on soybean growth. CDs were synthesized from citric acid and urea via a hydrothermal procedure. The analysis of the structure, chemical composition, and optical properties revealed that synthetic CDs have a sphere-like shape with rich hydrophilic groups on their surface. CDs exhibited superior upconverted photoluminescence properties and emitted strong fluorescence (exciting wavelength of 220 nm; emitting wavelength of 438 nm). Various concentrations of synthetic CDs (0–1000 mg L⁻¹), as a photosynthesis enhancer, were applied to soybean plants under hydroculture for 1–10 days. CDs positively affected soybean growth at concentrations less than 200 mg L⁻¹. However, at higher concentrations (500 or 1000 mg L⁻¹), they exhibited significant toxicity to plant growth, which was evidenced by the mass accumulation of CDs in damaged leaves. Metabolomic and transcriptomic analyses indicated that CDs at a low concentration (100 mg L⁻¹) could increase antioxidant and biomass accumulation in soybeans to promote plant growth. This study provided valuable insights into the impacts of CDs on plants in sustainable agricultural practices involving the use of nanomaterials. Full article

Toxic cyanobacterial blooms are prevalent in surface waters. Depending on several conditions, these blooms produce cyanotoxins. Human exposure to these toxins can occur through multiple routes, including contaminated crops through irrigation with contaminated water. Analytical methods have been developed for cyanotoxin quantification to investigate these exposures. Yet, proper comparisons between different labs via proficiency tests or interlaboratory comparison tests, as well as method quality assurance, are impossible. Developing reference materials for cyanotoxins in plants would help resolve these problems. Therefore, a novel liquid hydroculture setup was optimized to grow and contaminate strawberries. During fruit ripening, these plants were exposed to growth medium contaminated with pure microcystin-LR or freeze-dried cyanobacterial biomass containing different microcystin congeners. Afterwards, fruits, greens, and roots were harvested. Validated UHPLC-MS/MS methods were used to quantify the microcystin congeners in the growth medium and the plants. Furthermore, both contamination conditions resulted in the accumulation of toxin(s) in the roots and the greens. Yet in the contamination models, no toxin(s) accumulated in the fruits. Therefore, this contamination approach, combined with strawberries as a berry plant model, is only suitable for reference material production for limited matrices. Our cultivation model to produce reference material could be evaluated for other berry producers. Full article

This review analyzes the role of artificial intelligence (AI) and automation in optimizing vegetable production within hydroculture systems. Methods: Following the PRISMA methodology, this study examines research on IoT-based monitoring and AI techniques, particularly Deep Neural Networks (DNNs), K-Nearest Neighbors (KNNs), Fuzzy Logic (FL), Convolutional Neural Networks (CNNs), and Decision Trees (DTs). Additionally, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models were analyzed due to their effectiveness in processing temporal data and improving predictive capabilities in nutrient optimization. These models have demonstrated high precision in managing key parameters such as pH, temperature, electrical conductivity, and nutrient dosing to enhance crop growth. The selection criteria focused on peer-reviewed studies from 2020 to 2024, emphasizing automation, efficiency, sustainability, and real-time monitoring. After filtering out duplicates and non-relevant papers, 72 studies from the IEEE, SCOPUS, MDPI, and Google Scholar databases were analyzed, focusing on the applicability of AI in optimizing vegetable production. Results: Among the AI models evaluated, Deep Neural Networks (DNNs) achieved 97.5% accuracy in crop growth predictions, while Fuzzy Logic (FL) demonstrated a 3% error rate in nutrient solution adjustments, ensuring reliable real-time decision-making. CNNs were the most effective for disease and pest detection, reaching a precision rate of 99.02%, contributing to reduced pesticide use and improved plant health. Random Forest (RF) and Support Vector Machines (SVMs) demonstrated up to 97.5% accuracy in optimizing water consumption and irrigation efficiency, promoting sustainable resource management. Additionally, LSTM and RNN models improved long-term predictions for nutrient absorption, optimizing hydroponic system control. Hybrid AI models integrating machine learning and deep learning techniques showed promise for enhancing system automation. Conclusion: AI-driven optimization in hydroculture improves nutrient management, water efficiency, and plant health monitoring, leading to higher yields and sustainability. Despite its benefits, challenges such as data availability, model standardization, and implementation costs persist. Future research should focus on enhancing model accessibility, interoperability, and real-world validation to expand AI adoption in smart agriculture. Furthermore, the integration of LSTM and RNN should be further explored to enhance real-time adaptability and improve the resilience of predictive models in hydroponic environments. Full article

We train and compare the performance of two machine learning methods, a multi-variate regression network and a ResNet-50-based neural network, to learn and forecast plant biomass as well as the relative growth rate from a short sequence of temporal images from plants in aeroponic cultivation. The training dataset consists of images of 57 plants taken from two different angles every hour during a 5-day period. The results show that images taken from a top-down perspective produce better results for the multi-variate regression network, while images taken from the side are better for the ResNet-50 neural network. In addition, using images from both cameras improves the biomass estimates from the ResNet-50 network, but not those from the multi-variatemultivariate regression. However, all relative growth rate estimates were improved by using images from both cameras. We found that the best biomass estimates are produced from the multi-variate regression model trained on top camera images using a moving average filter resulting in a root mean square error of 0.0466 g. The best relative growth rate estimates were produced from the ResNet-50 network training on images from both cameras resulting in a root mean square error of 0.1767 g/(g·day). Full article

Over the last years, the use of artificial lakes and ponds to irrigate agricultural crops has been intensified and cultivation methods have been diversified. Hydroponics is a type of hydroculture which usually involves growing plants in an inert substrate, by using nutrient-enriched water to support plant growth. However, irrigating plants in hydroponic-based culture must be accompanied by monitoring the quality of irrigation water. The human health risks involved are mainly related to the proliferation of microcystin-producing cyanobacteria that contaminate water used for irrigation purposes. Strawberry (Fragaria vulgaris L.) is a widely cultivated plant of an increased economically importance worldwide. Its fruits provide essential elements for human nutrition; therefore, the study of its sensitivity to microcystins (MCs) is of paramount importance. The objective of this study was to evaluate the effects of MCs in irrigation water on the growth, physiology, and antioxidant defense system in F. vulgaris. In this study, strawberry seedlings at the three-leaf stage were grown in pots containing perlite under controlled conditions. Plants were exposed to a crude extract of Microcystis aeruginosa bloom at different concentrations of MCs (1, 5, 10, and 20 μg/L) for 60 days of exposure. The results showed that the highest concentrations of 10 and 20 μg/L induced a decrease in growth parameters. They resulted in root/shoot length decrease as well as number of leaves, roots/leaves dry and fresh weight. Furthermore, MCs reduced chlorophyll/carotenoid content, stomatal conductance, fluorescence, and total protein content of strawberry plants. At the same time, a significant increase in Malondialdehyde (MDA) (an indicator of lipid peroxidation), polyphenol, and sugar content were recorded in strawberry plants exposed to MCs at 5, 10, and 20 μg/L compared with the control. Additionally, superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), Polyphenoloxydase (PPO), and ascorbate peroxidase (APX) activities significantly increased in plants under MCs exposure. The oxidative stress was higher in plants exposed to 10 and 20 μg/L of MCs from the second harvest (after 60 days of exposure) compared to those from the first harvest (after 30 days). Overall, the results obtained in this study indicate an increasingly negative effect of MCs on strawberry plants grown in hydroponics even at concentrations (10 and 20 µg/L). This effect is more damaging on the roots after exposure (60 days). Full article

In response to decreases in the assimilation efficiency of CO₂, plants oxidize the reaction center chlorophyll (P700) of photosystem I (PSI) to suppress reactive oxygen species (ROS) production. In hydro-cultured sunflower leaves experiencing essential mineral deficiencies, we analyzed the following parameters that characterize PSI and PSII: (1) the reduction-oxidation states of P700 [Y(I), Y(NA), and Y(ND)]; (2) the relative electron flux in PSII [Y(II)]; (3) the reduction state of the primary electron acceptor in PSII, Q_A (1 − qL); and (4) the non-photochemical quenching of chlorophyll fluorescence (NPQ). Deficiency treatments for the minerals N, P, Mn, Mg, S, and Zn decreased Y(II) with an increase in the oxidized P700 [Y(ND)], while deficiencies for the minerals K, Fe, Ca, B, and Mo decreased Y(II) without an increase in Y(ND). During the induction of photosynthesis, the above parameters showed specific responses to each mineral. That is, we could diagnose the mineral deficiency and identify which mineral affected the photosynthesis parameters. Full article

This editorial introduces the special issue, ‘World Literature and the Blue Humanities’. The authors articulate the commonalities and tensions between world literature, world-ecology, blue humanities, and hydrocultural approaches. Taking megadams, water pollution, and the blue revolution as baselines, we offer short analyses of works by Namwali Serpell, Craig Santos Perez, Jean Arasanayagam, Paul Greengrass, Wyl Menmuir, and Emily St. John Mandel in order to articulate how culture can both contest and normalize water enclosure. The piece ends with a brief summary of the contributions to the special issue. Full article