Search Results (483)

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

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

Urban agriculture (UA) is emerging as a promising strategy for sustainable food production in response to growing environmental pressures. Indoor vertical farming (IVF), combining Controlled Environment Agriculture (CEA) with Building-Integrated Agriculture (BIA), enables efficient resource use and year-round crop cultivation in urban settings. This study assesses the environmental performance of a prospective IVF system located on a university campus in Portugal, focusing on the integration of photovoltaic (PV) energy as an alternative to the conventional electricity grid (GM). A Life Cycle Assessment (LCA) was conducted using the Environmental Footprint (EF) method and the LANCA model to account for land use and soil-related impacts. The PV-powered system demonstrated lower overall environmental impacts, with notable reductions across most impact categories, but important trade-offs with decreased soil quality. The LANCA results highlighted cultivation and packaging as key contributors to land occupation and transformation, while also revealing trade-offs associated with upstream material demands. By combining EF and LANCA, the study shows that IVF systems that are not soil-based can still impact soil quality indirectly. These findings contribute to a broader understanding of sustainability in urban farming and underscore the importance of multi-dimensional assessment approaches when evaluating emerging agricultural technologies. Full article

Maedi-visna virus (MVV), a small ruminant lentivirus causing chronic multisystemic disease in sheep, poses significant economic burdens due to reduced productivity and a lack of effective treatments. Despite its worldwide prevalence, epidemiological data from Algeria remain absent. This first national seroprevalence study aimed to elucidate MVV distribution, risk factors, and transmission dynamics in Algerian sheep herds. A cross-sectional survey of 1400 sheep across four regions (East, Center, West, South) was conducted, with sera analyzed via indirect ELISA (IDvet). Risk factors (geography, age, sex, breed, farming system) were evaluated using chi-square tests and Cramer’s V. Overall seroprevalence was 9.07% (95% CI: 7.57–10.57), with significant variation by sex (females: 20.44% vs. males: 3.68%; p < 0.05), age (1–5 years: 6.86% vs. <1 year: 0.29%; p = 0.01), and region (Central: 3.36% vs. Eastern: 0.86%; p < 0.05). Notably, no association was found with breed or farming system (p ≥ 0.08), contrasting prior studies and suggesting region-specific transmission dynamics. Females exhibited heightened seropositivity, implicating prolonged herd retention and vertical transmission risks. Geographic disparities highlighted industrialized farming in central Algeria as a potential transmission amplifier. Strikingly, seronegative animals in high-prevalence herds hinted at genetic resistance, warranting further investigation. This study provides foundational insights into MVV epidemiology in North Africa, underscoring the need for targeted surveillance, ewe-focused control measures, and genetic research to mitigate transmission. The absence of prior national data elevates its significance, offering actionable frameworks for resource-limited settings and enriching the global understanding of SRLV heterogeneity. Full article

To support the growing global population, sustainable farming methods like vertical farming must complement traditional agriculture. This study evaluated the effects of various nitrogen fertilizer application rates (N_low (1055.3 ppm), N_rec (1640.9 ppm), N_high (2811.3 ppm), and N_0 (469.9 ppm)) on organic kale (Brassica oleracea L. var. acephala ‘Lacinato’) and Swiss chard (Beta vulgaris subsp. Vulgaris ‘Ruby/Rhubarb Red’), grown in a vertical growing system installed in a high tunnel during the spring and fall season of 2023 at the organic farm of Tennessee State University. Growth parameters studied included fresh weight, Brix, chlorophyll, plant height, and leaf count. Most parameters did not exhibit statistically significant differences (alpha = 0.05). However, consistent numerical trends and deviations were observed. Although not statistically significant, kale achieved the highest mean fresh weight in N_rec (688.08 g), and Swiss chard in N_high by spring (649.62 g). Among the few parameters, significant differences were observed for Swiss chard plant height (48.07 cm) and leaf count (47.25), with N_high during fall. Findings suggest that while definitive conclusions were limited, recommended nitrogen rates (N_rec) may enhance crop performance and contribute sustainable yields in resource constrained vertical farming systems. Further controlled studies are warranted to validate trends and refine nutrient strategies in vertical growing system. Full article

Vertical farming systems offer an efficient solution for sustainable food production in urban areas. However, managing nitrate (NO₃⁻) levels remains a significant challenge for improving crop yield, quality, and safety. This study evaluated the effects of nitrate availability on growth performance, nutrient uptake, and water use efficiency in a vertical hydroponic system that intercropped lettuce (Lactuca sativa) with alfalfa (Medicago sativa). The experiment was conducted in a controlled vertical hydroponic system using Nutrient Film Technique (NFT) channels, with nitrogen levels set at 0, 33, 66, 100, and 133% of the standard concentration. The results indicated that the intercropping treatment with 66% nitrate (IC-N_66%) improved water use efficiency by 38% and slightly increased leaf area compared to the other intercropping treatments. However, the control group, which consisted of a monoculture with full nitrate supply, achieved the highest overall biomass. Ion concentrations, including nitrate, calcium, magnesium, and micronutrients, were moderately affected by the intercropping strategy and nitrate levels. These findings suggest that moderate nitrate input, combined with nitrogen-fixing legumes, can enhance resource efficiency in hydroponic systems without significantly compromising yield. These findings offer a promising framework for incorporating legumes into hydroponic systems, minimizing the need for synthetic inputs while maintaining yield. These results support the use of agroecological intensification strategies in highly efficient soilless systems. Full article

This perspective paper examines the research and development challenges faced by plant factories with artificial lighting (plant factories hereafter). The global and local challenges facing our planet can be divided into the following four categories: (1) food and agriculture; (2) environment and ecosystems; (3) depletion, uneven distribution, and the overuse of nonrenewable resources; and (4) society, economy, and quality of life. All of the aspects of this four-way deadlock problem must be resolved simultaneously, since solving only one of them could exacerbate one or more of the remaining three. In this paper, the role of plant factories in solving the four-way deadlock problem is discussed from the following perspectives: (1) civilization and culture, (2) participatory science, and (3) the integration of biotechnology and the latest nonbiological technology, such as artificial intelligence (AI). The relationship and interactions between the environment and plant ecosystems are easily observed in the plant factories’ cultivation room. Thus, it is easy to analyze their relationship and interactions. The findings from such observations can also be applied to increase the yield in plant factories, with minimum resource inputs. Moreover, if the electricity generated by renewable energy sources is used, it will become an energy-autonomous plant factory. This means that the plant factory can be operated with the minimum contribution of greenhouse gas emissions to global warming and land area use. Full article

Optimizing artificial lighting in controlled-environment agriculture is crucial for enhancing crop productivity and resource efficiency. This study presents a spectral–spatial co-optimization strategy for LED lighting tailored to the physiological needs of Italian lettuce (Lactuca sativa L. var. italica). A miniature plant factory system was developed with dimensions of 400 mm × 400 mm × 500 mm (L × W × H). Seven customized spectral treatments were created using 2835-packaged LEDs, incorporating various combinations of blue and violet LED chips with precisely controlled concentrations of red phosphor. The spectral configurations were aligned with the measured absorption peaks of Italian lettuce (450–470 nm and 640–670 nm), achieving a spectral mixing uniformity exceeding 99%, while the spatial light intensity uniformity surpassed 90%. To address spatial light heterogeneity, a particle swarm optimization (PSO) algorithm was employed to determine the optimal LED arrangement, which increased the photosynthetic photon flux density (PPFD) uniformity from 83% to 93%. The system operates with a fixture-level power consumption of only 75 W. Experimental evaluations across seven treatment groups demonstrated that the E-spectrum group—comprising two violet chips, one blue chip, and 0.21 g of red phosphor—achieved the highest agronomic performance. Compared to the A-spectrum group (three blue chips and 0.19 g of red phosphor), the E-spectrum group resulted in a 25% increase in fresh weight (90.0 g vs. 72.0 g), a 30% reduction in SPAD value (indicative of improved light-use efficiency), and compared with Group A, Group E exhibited significant improvements in plant morphological parameters, including a 7.05% increase in plant height (15.63 cm vs. 14.60 cm), a 25.64% increase in leaf width (6.37 cm vs. 5.07 cm), and a 6.35% increase in leaf length (10.22 cm vs. 9.61 cm). Furthermore, energy consumption was reduced from 9.2 kWh (Group A) to 7.3 kWh (Group E). These results demonstrate that integrating spectral customization with algorithmically optimized spatial distribution is an effective and scalable approach for enhancing both crop yield and energy efficiency in vertical farming systems. Full article

This study examined the transhumance of bee colonies from the perspective of Hungarian beekeepers. Hungarian scientific literature on this topic is minimal. Therefore, it is necessary to assess the sustainability of the Hungarian beekeeping sector using a research method based on a survey of beekeepers. This research was based on a questionnaire (N = 1067). Basic statistical tools were used to analyse the data. To evaluate the differences between the individual category variables, we used the chi-square test for cross-stability and non-parametric tests were used. In this study, we defined four farm (apiary) size categories based on the number of bee colonies. Our results revealed statistically significant correlations between apiary size and transhumance, payment for the use of bee pastures and transhumance, as well as between the type of hive used and transhumance. Transhumance is mainly characteristic of semi-professional and professional apiaries. Hungarian beekeepers (typically small-scale and professional apiary size) traditionally give honey as a “gift” to landowners for the use of bee pastures, which is related to transhumance. In terms of the type of hive used, a significant difference can be seen between transhumance (28 kg/colony) and stationary apiaries (21 kg/colony) in the case of vertical hives. Full article

Navigation in offshore wind farm (OWF) areas is essential for construction, maintenance, safety, and traditional activities like fishing. However, the presence of OWFs extends to sea routes, negatively impacting maritime transport economics. This paper examines navigational risk indicators in the vertical and horizontal planes of the ship domain for three representative vessels navigating under different hydrometeorological conditions within the location of a proposed offshore wind farm in the Polish sector of the Baltic Sea. The study compares three types of domain parameters defined by the PIANC guidelines, Coldwell’s two-dimensional model, and Rutkowski’s three-dimensional model. The analysis includes navigational hazards located ahead of the ship’s bow and astern from the aft, as well as keeping under-keel and over-head clearance. Besides the main numerical indicators of navigational risk estimated for obstacles on the port and starboard sides, the study emphasizes the importance of such additional factors. The primary objective of this paper is to identify the ship types that can navigate and fish safely in proximity to and within the OWF area. The analysis employs hydrometeorological data, mathematical models, and operational data derived from maritime navigation and maneuvering simulators. This comprehensive approach aims to enhance maritime safety in OWF areas. Full article

Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework that combines satellite-derived soil moisture estimates, ground-based observations, the HYDRUS-1D vadose zone model, and the ensemble Kalman filter (EnKF) data assimilation method to improve soil moisture simulations over saline-affected farmland in the Hetao irrigation district. Vegetation effects were first removed using the water cloud model; after correction, a cubic regression using the vertical transmit/vertical receive (VV) signal retrieved surface moisture with an R² value of 0.7964 and a root mean square error (RMSE) of 0.021 cm³·cm⁻³. HYDRUS-1D, calibrated against multi-depth field data (0–80 cm), reproduced soil moisture profiles at 17 sites with RMSEs of 0.017–0.056 cm³·cm⁻³. The EnKF assimilation of satellite and ground observations further reduced the errors to 0.008–0.017 cm³·cm⁻³, with the greatest improvement in the 0–20 cm layer; the accuracy declined slightly with depth but remained superior to either data source alone. Our study improves soil moisture simulation accuracy and closes the knowledge gaps in multi-source data integration. This framework supports sustainable land management and irrigation policy in vulnerable farming regions. Full article

China leads global carp aquaculture (farming of species within the family Cyprinidae), producing 20 million tons annually in a sector shaped by favorable policies, infrastructure, and innovation. Carp farming in China is rooted in millennia of traditional practices and transformative post-1978 economic reforms. This review synthesizes the historical trajectory, technological advancements, policy frameworks, and sustainability challenges shaping China’s carp aquaculture sector. Historically, carp polyculture systems, developed during the Tang Dynasty (618–907 CE), laid the foundation for resource-efficient practices. Modern intensification, driven by state-led policies, genetic innovations, and feed-based systems, enabled unprecedented growth. However, rapid expansion has exacerbated environmental trade-offs, including nutrient pollution, habitat loss, and antibiotic resistance, while socioeconomic disparities, aging labor forces, and market volatility threaten sectoral resilience. Policy shifts since the 2000s prioritize ecological sustainability, exemplified by effluent regulations, wetland restoration, and green technologies. Despite progress, challenges persist in reconciling economic viability with environmental safeguards. Key success factors include long-term policy support, smallholder capacity building, vertically integrated supply chains, product differentiation, and adaptive management. With balanced policies emphasizing economic, social, and environmental sustainability, carp aquaculture can enhance domestic food and nutrition security. China’s experience showcases the potential of aquaculture to bolster food security but highlights the urgent need to harmonize productivity with ecological and social equity to ensure long-term resilience. Lessons from China’s model offer actionable insights for global aquaculture systems navigating similar sustainability imperatives. Full article

Jasmonates have emerged as a prominent elicitor for enhancing trichome development and cannabinoid production in Cannabis sativa L. (cannabis). These glandular trichomes synthesize and store important cannabinoids, including tetrahydrocannabinol (THC) and cannabidiol (CBD), which determine the yield, potency, and quality of cannabis flowers. Methyl jasmonate (MeJA) acts through the COI1–JAZ–MYC signaling pathway to upregulate genes associated with trichome initiation and cannabinoid precursor formation. Evidence suggests that moderate MeJA concentrations (typically 50–100 µM) can effectively boost trichome density, elevate hexanoyl-CoA availability, and modestly enhance key biosynthetic enzyme activities, ultimately increasing THC and CBD content. However, higher methyl jasmonate doses can amplify these benefits, yet pose a risk of excessive vegetative stunting, highlighting the crucial trade-off between enhancing cannabinoid potency and maintaining overall biomass yield. Interaction with hormones like gibberellins, salicylic acid, and ethylene further shapes the plant’s stress responses and secondary metabolism. Application in controlled environments, such as greenhouses or vertical farms, shows promise for enhancing resin production while minimizing biomass loss. In outdoor conditions, the application may offer additional defense benefits against pests and pathogens. These responses can vary depending on the cultivar, underscoring the importance of cultivar-specific optimization. As demand for high-cannabinoid cannabis products continues to grow and agrochemical options remain limited, leveraging MeJA treatments offers a practical, non-genetically modified approach to optimize yield, quality, and resilience in cannabis cultivation. Full article

The production of duckweed (Lemnaceae) as a novel protein source could make a valuable contribution to human nutrition. The greatly reduced habitus of duckweed enables simple cultivation with extremely low space requirements, making this free-floating freshwater plant ideal for substrate-free and vertical cultivation in controlled environment agriculture. Of particular importance in the design of a plant-producing Indoor Vertical Farming process is the determination of light intensity, as artificial lighting is generally the most energy-intensive feature of daylight-independent cultivation systems. In order to make the production process both cost-effective and low emission in the future, it is, therefore, crucial to understand and mathematically describe the primary metabolism, in particular the light utilization efficiency. To achieve this, a growth model was developed that mathematically describes the combined effects of plant density and light intensity on the growth rate of Lemna minor L. and physiologically explains the intraspecific competition of plants for light through mutual shading. Furthermore, the growth model can be utilized to derive environmental and process parameters, including optimum harvest quantities and efficiency-optimized light intensities to improve the production process. Full article

Photosynthetically active radiation (PAR) is crucial for plant growth, influencing photosynthesis efficiency and crop yield. The increasing adoption of controlled-environment agriculture (CEA) necessitates precise PAR monitoring. The high cost of commercial PAR sensors, however, limits their accessibility and widespread use, creating a growing need for a low-cost alternative capable of reliable deployment in diverse agricultural environments. Building on recent advancements in PAR sensing using multi-channel spectral sensors such as the AS7341 and AS7265, this study develops the electronics for an AS7341-based, open-source, cost-effective (~USD 50) PAR sensor validated across a broad PPFD range and conditions, ensuring reliability and ease of replication. It uses a relatively simple multi-linear regression that offers real-time applications without energy intensive machine learning. The developed sensor is calibrated against the industry-standard Apogee SQ-500SS PAR sensor in four distinct farming environments: (i) horizontal grow lights, (ii) vertical agrotunnel lighting, (iii) agrivoltaics, and (iv) in greenhouses. A mean error ranging from 1 to 5% indicates its suitability for controlled environment farming and continuous data logging. The open-source hardware design and systematic installation guidelines enable users to replicate, calibrate, and integrate the sensor with minimal background in electronics and optics. Full article