Search Results (1,077)

Nitrous oxide (N₂O) is a potent greenhouse gas and a major ozone-depleting substance of the nitrogen cycle. While global N₂O budgets focus on microbial soil sources, evidence suggests that living plants can also contribute to N₂O emissions. However, the origin of plant-associated N₂O fluxes is contested: measured emissions may arise from endogenous plant metabolism or merely from transport of soil-derived N₂O. In this review, we clarify these pathways and synthesize current findings from laboratory and field studies. Experimental evidence from ¹⁵N-tracer and axenic-culture studies supports N₂O formation linked to nitrate (NO₃⁻) and nitrite (NO₂⁻) in photosynthetic organisms, although mechanistic resolution varies among taxa. In angiosperms, proposed plastid/chloroplast and hypoxia-associated mitochondrial routes are linked to NO₃⁻/NO₂⁻ metabolism and NO formation, but the terminal NO-to-N₂O step remains unresolved and differs from the flavodiiron-dependent mechanism demonstrated in algae. We discuss key environmental and physiological controls (substrate availability, light, O₂ status, reductant supply) on these processes and highlight methodological challenges in source attribution (chamber artefacts, destructive sampling, isotope analysis). Our analysis suggests that plant-associated N₂O fluxes are real, but highly variable and context-dependent. Rather than assuming generic “plant emission factors”, we emphasize source-specific approaches that distinguish endogenous formation from soil-derived transport and microbial production. This framework can improve N₂O budgets and guide mitigation by indicating whether management should target soil N availability and microbial processes or physiological conditions favouring plant-associated N₂O formation. Full article

Commercial greenhouses are becoming significant, controllable, and weather-dependent electricity loads in regions pursuing controlled environment agriculture (CEA). Their electrical demands—such as supplemental lighting, heating, ventilation/cooling, irrigation pumps, and CO₂ enrichment—exhibit substantial intra-day flexibility. This flexibility stems from the fact that plant productivity depends on time-integrated agronomic variables (e.g., Daily Light Integral, accumulated thermal time, and mean vapor pressure deficit) rather than instantaneous environmental setpoints. However, existing studies have predominantly focused on greenhouse thermal modeling and energy conservation, while decision-making models that integrate crop physiological characteristics into day-ahead (DA) market bidding remain limited. To bridge this gap, this paper develops a scenario-based stochastic DA bidding framework for a load aggregator, representing multiple smart greenhouses in a wholesale electricity market. In the DA stage, the aggregator submits hourly demand–price bidding curves based on price-conditional schedules of heating and lighting demand while satisfying coupled thermal–photon balance constraints. Fifty representative scenarios generated from 2023 to 2024 historical price and weather data through cGAN-based scenario generation and K-means scenario reduction are used for stochastic bid construction and feasibility analysis. A separate 30-day historical dataset from January 2025 is used for benchmark comparison. The aggregator portfolio consists of 100 greenhouses divided into five LAI-based crop groups, with 20 greenhouses in each group. Relative to the 15–25 °C trapezoidal temperature baseline, which is adopted as the primary practical benchmark, the proposed strategy reduces the mean daily electricity procurement cost by 15.80%. A reduction of 18.89% is also observed relative to the rigid 20 °C thermostat case, which is retained as a capacity-intensive reference. These results represent simulation-based operating-cost comparisons under a common equipment configuration and do not include equipment capital costs. Full article

Micro-sprinkler irrigation is commonly used to optimize plant growing environments and prevent growth inhibition and yield losses caused by high air temperatures (T_a) in greenhouses. Nevertheless, instantaneous photosynthetic rate measurements suffer from time lag, and the temporally dynamic microclimate alterations induced by micro-sprinkling make it difficult to reproduce the real ambient conditions for crop growth. Therefore, two treatments, namely micro-sprinkling combined with drip irrigation (MSDI) and conventional drip irrigation control (DI), were established in a Venlo-type greenhouse. Continuous chlorophyll fluorescence (ChlF) monitoring combined with rapid light curves under fixed photosynthetically active radiation was adopted to investigate the diurnal alleviation effects of micro-sprinkling on tomatoes under high-temperature stress. This study found that Φ_PSII was more sensitive than Fv/Fm in detecting changes in PSII photochemical performance under high-temperature stress. Micro-sprinkling showed greater mitigation effects under moderate heat stress, with the highest enhancement in Φ_PSII (approximately 0.12) observed when leaf temperature (T_l) was around 34.5 °C. However, the improvement effect decreased under extreme heat conditions, and Φ_PSII increased by only 0.017 when T_a exceeded 38 °C. The slope of the fitted line between Φ_PSII and PAR on sunny days increased with increasing heat stress, indicating the enhanced sensitivity of PSII photochemical regulation to thermal stress. Compared with DI, MSDI increased tomato yield by 31.2% and 47.6% in 2021 and 2022, respectively, while improving fruit quality by increasing single fruit weight, fruit shape index, and soluble sugar content. In addition, MSDI increased SPAD, Fv/Fm, Φ_PSII, and ETR by 9.6–15.6%, 8.8–14.8%, 10.3–13.3%, and 10.3–19.6%, respectively, indicating improved PSII photochemical performance under high-temperature conditions. In conclusion, micro-sprinkling mitigated part of the negative effects of high-temperature stress and significantly improved tomato yields and fruit quality, which could be used in agricultural production. Full article

This paper considers the problem of early detection of late blight (Phytophthora infestans) in tomatoes based on computer vision and machine learning methods. The main purpose of the study was to develop a representative dataset of images of tomato leaves and an approach to extracting informative features for classifying the stages of disease development. A new dataset was generated using tomato plants grown under greenhouse conditions, with leaf images subsequently captured under controlled laboratory conditions, including five stages of late blight progression with variability in imaging devices, lighting conditions, and temporal disease dynamics. To improve the quality of image analysis, a preprocessing stage was applied, including conversion to grayscale, median filtering, and binarization using the Otsu method. In addition to the traditional textural features, fractal analysis was used to quantify the structural complexity of the affected leaf areas. To verify the information content of the selected features, classification experiments were conducted using Random Forest, XGBoost, and Support Vector Machine models, and the quality was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the combination of textural and fractal features contributes to a more accurate distinction between the stages of disease. The developed dataset and the proposed approach can be used in further research on plant disease diagnosis, agricultural monitoring, and precision farming systems although it should be acknowledged that the dataset is limited to greenhouse settings, and field-scale generalizability requires further validation. Full article

It is well-known nowadays that the usage of conventional opaque photovoltaics in agricultural practices has negative effects on crops growth, mainly due to the shading effect they cause. On the other hand, most of the emerging semi-transparent solar cells do not demonstrate the appropriate optical characteristics and scalability to attain their viable integration in agriculture, undermining their commercialization. This study deals with the development of wavelength-selective semi-transparent dye-sensitized solar cells (DSSCs) using the scalable slot-die deposition method. These devices are designed to provide high transparency in the photosynthetically active radiation (PAR) region and effectively exploit the near-ultraviolet to blue-visible spectrum for power production, simultaneously protecting cultivations from harmful short-wavelength irradiation. To this aim, a new quinoline-based dye and a highly transparent iodine-free electrolyte were employed in DSSCs, giving an external quantum efficiency of 70% for wavelengths up to 500 nm and a PAR transmittance on the level of 50% (55% crop growth factor). Additionally, the light-to-electricity conversion efficiency of these devices is high for both front- and rear-side illumination under all-weather irradiation conditions (up to 94% bifaciality factor). Finally, two new figures-of-merit (greenhouse compatibility factor, agrivoltaic performance factor) are introduced to quantify the balance of photovoltaic performance and agronomic functionality. Full article

Greenhouse strawberry production has rapidly increased around the world, since greenhouse production allows for protection from adverse weather events and optimized climate control. During the winter months, supplemental electric lighting is essential to maintain production. However, the use of supplemental lighting can account for up to 30% of operating expenses, impacting the profitability of production. In addition, strawberries are prized for their taste, so maintaining fruit quality during production is essential. This study examined the impact four different supplemental overhead LED spectral lighting treatments (red–blue (95% red, red-rich), pink, warm white, and white) and two different irrigation electrical conductivity (EC) levels (low = 2 mS cm⁻¹ and high = 3 mS cm⁻¹) on morphology, physiology, fruit quality and electricity costs in two strawberry cultivars—cv. ‘Albion’ and cv. ‘Favori’—grown in rockwool during the 2023–2024 winter greenhouse season in Harrow, Ontario, Canada. The results showed that plants exposed to a board spectrum generally had higher photosynthetic rates and vegetative biomass. However, a threshold was observed when the green light fraction was above 24% (i.e., plants grown under the white-light treatment), resulting in reductions in photosynthesis and vegetative biomass. Although differences in photosynthetic capacity and biomass were observed, there was no impact of the light spectrum or irrigation EC on yield in either cultivar. Compared to the red–blue spectrum, plants grown under pink and warm white were observed to have lower titratable acidity (TA) and higher ratios of total soluble solids (TSSs) relative to TA. Importantly, it was determined that due to the higher efficacy of the red–blue supplemental lighting fixtures, the red-rich spectrum resulted in the lowest estimated lighting electricity cost per kilogram under the tested conditions. This study suggests that the use of supplemental high-efficacy, narrow wavelength, red-rich supplemental lighting can maintain yield and improve economic feasibility for greenhouse-grown strawberries. Full article

To address the difficulty of directly incorporating crop growth-stage information into industrial control processes and the limited adaptability of control parameters to different developmental stages in conventional greenhouse fertigation management, this study developed a vision-based growth-stage determination and PLC-based fertigation parameter invocation system for greenhouse blueberry cultivation. The system integrated greenhouse blueberry image acquisition, edge-based visual recognition, STM32-based encoding conversion, PLC control, human–machine interaction, and actuator linkage. Image samples were collected from greenhouse blueberry plants, whereas system-level linkage verification was conducted on a small greenhouse prototype platform. The edge vision module was used to output preliminary blueberry growth-stage labels, while environmental and substrate sensor data were used for sensor status verification and control safety validation. The final growth-stage label was converted by the STM32 unit into a discrete coded signal and then transmitted to the PLC. Based on a predefined stage-strategy table, the PLC invoked the corresponding target parameters and drove the irrigation, fertilizer delivery, supplemental lighting, ventilation, and shading devices for coordinated control. The image-level stage classification evaluation based on an independent test set showed that different lightweight YOLO classification models exhibited different performance levels in identifying the major growth stages of blueberry. YOLO11n-cls achieved the highest Accuracy and Macro F1-score, reaching 85.71% and 84.81%, respectively. YOLOv8n-cls achieved an Accuracy, Macro F1-score, and Macro AP of 80.95%, 81.10%, and 91.25%, respectively, showing a favorable balance between model size and recognition performance. The confusion matrix indicated that misclassifications mainly occurred between the fruit expansion stage and the ripening stage, reflecting the morphological continuity of blueberry fruit development during the transitional period. The system linkage test results showed that blueberry growth-stage labels could be output by the edge vision terminal, converted by the STM32 unit, read by the PLC, and used for stage-specific target parameter invocation. Sensor acquisition, HMI display, and actuator response were completed cooperatively. The single determination and output time of the edge terminal was 500–1000 ms, and the remote-control response delay was 0.3–1.0 s. No obvious communication interruption, command loss, or abnormal shutdown occurred during system operation. These results indicate that blueberry growth-stage recognition results can serve as input conditions for PLC parameter invocation and device-control testing on a small greenhouse prototype platform. This study did not conduct a complete closed-loop cultivation experiment under real production greenhouse conditions or establish long-term blueberry cultivation control treatments. Therefore, no quantitative conclusions are drawn regarding water and fertilizer use efficiency, fertilizer application reduction, plant physiological responses, yield, or fruit quality improvement. Full article

This study evaluated the effects of different LED spectral irradiance ratios on the growth, secondary metabolites, and essential oil characteristics of Ocimum × africanum Lour. cultivated for four weeks under controlled conditions. Four LED lighting treatments with different red, blue, green, ultraviolet-A, and far-red light ratios, and a treatment in greenhouse as low-light control were used. The constant 16 h photoperiod and light intensity of 220 µmol·m⁻²·s⁻¹ were maintained. The F2 treatment (UV-A:B:R:Fr = 6.60:45.15:29.23:19.02) promoted the greatest plant height (76.62 cm), and chlorophyll a (7.41 mg/100 g, FW), chlorophyll b (4.73 mg/100 g, FW), and total phenolic (25.78 mg/g, FW) concentrations. F1 treatment (B:G:R:Fr = 17.14:29.8:47.4:5.66) produced significantly higher (p < 0.01) biomass (8.3 ton/ha, FW), oil yield (10.89 L/ha), and carotenoid (3.74 mg/100 g, FW) than the others. Essential oils contained 12–15 compounds, dominated by neral (27.5–37.3%), geranial (41.1–49.9%), and (E)-β-caryophyllene (2.4–9.9%), while the highest contents of oil (0.83%, DW), anthocyanin (16.50 mg/100 g, FW), and total flavonoid (14.17 mg/g, FW) were obtained under F4 (B:G:R:Fr = 13.85:43.50:39.30:3.35). These findings demonstrate that optimized LED spectra can effectively improve both productivity and phytochemical quality in O. africanum through regulating both primary and secondary metabolism. Full article

Despite rapid global growth in renewable energy capacity, fossil fuels still dominate the energy mix. Renewable energy firms often face limited access to bank credit because their asset-light, technology-intensive business models provide little collateral, constraining investment in clean energy deployment. This study examines whether China’s Fintech Reform Pilot Zones, which introduce digital technology-based credit evaluation, can alleviate these financing constraints and accelerate corporate energy transition. Using a staggered difference-in-differences design on a panel of Chinese listed renewable energy firms, we find that pilot zone designation significantly improves firms’ access to external financing and increases Energy Transition Depth (ETD) by approximately 3.6 percentage points, equivalent to 24.7% of the sample mean, indicating economically meaningful improvements in corporate energy transition. The strongest effects are observed in solar photovoltaic deployment and battery storage penetration. Greater energy transition is also associated with lower firm-level greenhouse gas emission intensity, suggesting potential environmental benefits. Mediation analysis identifies two complementary pathways: an innovation-accumulation route which advances renewable energy technology, and a capital-deployment route which supports renewable energy capacity expansion by relaxing firms’ general financing constraints. Regions with more developed renewable energy industries also exhibit lower fossil energy consumption and carbon emissions, suggesting potential regional spillover effects. These findings demonstrate that Fintech-enabled financial reform can facilitate renewable energy deployment and support broader energy transition and decarbonization, with important implications for emerging economies. Full article

Drainage solution reuse in soilless strawberry production can reduce nutrient-rich discharge, but adoption requires microbial control, hydraulic reliability, and manageable energy demand. This field study evaluated a photovoltaic (PV) assisted ultraviolet-C (UV-C) treatment loop for substrate derived drainage solution in a 132 m² three-tier natural light greenhouse producing ‘Solhyang’ strawberry in Sokcho-si, Republic of Korea. The system used an 11.25 kWp vertical windbreak-type PV facility and a 650 W treatment loop comprising a 250 W low-pressure mercury UV-C reactor, a 350 W pump, and a 50 W controller. The loop operated for 2.5 h day⁻¹, processed 3.25 m³ day⁻¹ as cumulative reactor throughput, and consumed 1.625 kWh day⁻¹, equal to 4.22% of the measured daily PV alternating current (AC) output (38.5 kWh day⁻¹). The drainage solution had low ultraviolet transmittance at 254 nm (UVT₂₅₄; 25–50%) and moderate turbidity (5–30 NTU), conditions that can attenuate UV radiation and shield microorganisms. Across six post fruit set sampling events, the mean log₁₀ reductions were 1.15 ± 0.09 for culturable molds/fungal propagules and 1.64 ± 0.09 for culturable aerobic bacteria; paired tests on log₁₀ transformed counts were significant (p < 0.001). Total coliform bacteria were not detected after treatment, corresponding to a detection limit-based lower-bound reduction of ≥2.69 ± 0.17 log₁₀. Apparent fluence values were treated as engineering estimates rather than validated delivered dose. The results support UV-C sanitation as a preliminary enabling step for drainage solution reuse, while biodosimetry, untreated circulation controls, multi-stage seasonal sampling, full-season recirculation, and crop response validation remain necessary. Full article

Smart greenhouse management requires decisions that balance yields, costs, resource use, emissions, and revenue. Existing threshold-based controls and many learning models use sensor data mainly for environmental feedback or yield prediction, leaving limited support for operational decisions tied to farm profitability and resource efficiency. This study proposes AER-SFRDNet, a multisource sensor fusion and deep reinforcement learning framework for revenue-aware greenhouse regulation. Crop images, environmental sensor sequences, management records, resource consumption data, and economic variables from facility tomato production are integrated into a unified agro-economic state representation. A yield–cost–revenue prediction module estimates the yield, resource consumption, production cost, sales revenue, and net revenue. These outputs guide a reinforcement learning module that optimizes continuous actions including irrigation, ventilation, supplemental lighting, heating, and CO₂ application. A comparison with traditional machine learning, unimodal deep learning, multimodal learning, and reinforcement learning baselines shows lower prediction errors and improved regulation outcomes. AER-SFRDNet achieves a yield MAE of $0.486$, yield RMSE of $0.674$, yield $R^2$ of $0.893$, cost RMSE of $0.613$, and net revenue MAPE of $10.82$. In regulation experiments, the cumulative net revenue and input–output ratio reach $1.276$ and $1.263$, while the normalized energy use, water use, and carbon emissions per unit yield decrease to $0.752$, $0.781$, and $0.741$, respectively. The results suggest that agro-economic state modeling can support greenhouse decisions that consider both revenue and sustainability-related resource efficiency. Full article

This paper describes the design and implementation of an automated irrigation control system that uses data collected by a wireless sensor network. Each sensor node, built on a custom-designed printed circuit board, includes sensors for light intensity, temperature, and a custom soil moisture sensor. Data is transmitted to a central control node via ESP-NOW, where it is processed and compared with configurable thresholds retrieved from Google Sheets over Wi-Fi. Irrigation is triggered automatically when conditions meet the remotely defined thresholds. A key contribution is the development and testing of a custom soil moisture sensor, with results compared to commercial models. The system supports low-power operation through deep sleep modes, enabling long-term field deployment. The novelty lies in the complete integration of hardware, software, and cloud-based control, providing a flexible and low-cost solution for precision agriculture. The system can be deployed in greenhouses or open fields and serves as a platform for future research in smart irrigation. The fundamental aspect is a very user-friendly solution for any farmer attributable to easy accommodation to the Google Sheets interface, no maintenance cost over the cloud account, and up to 45 days of battery life or a built-in alternative for solar power. Full article

Highly perishable strawberries demand strict post-harvest time management, making accurate short-term yield prediction central to optimizing modern greenhouse production and supply chain scheduling. However, existing models that rely excessively on isolated environmental factors exhibit delayed responsiveness to actual crop physiological dynamics and struggle with integrating multimodal data. To overcome these limitations, we propose a short-term method for predicting greenhouse strawberry yield that integrates visual phenology with meteorological sequences. The proposed method was validated using a multimodal dataset acquired from 150 tracked greenhouse strawberry plants over a 72-day monitoring period (11 December 2025, to 20 February 2026), incorporating continuous microclimate records and an image repository of 784 original images annotated into five distinct phenological classes (flower, green, white, pink, and red). First, using our improved YOLO11-SC model, we effectively resolve challenges of complex illumination and dense foliage occlusion, achieving high-precision automated extraction of five consecutive strawberry phenological stages. Second, by fusing these visual markers with meteorological time series (e.g., temperature, humidity, and light intensity), we construct a multimodal spatiotemporal feature matrix. To accommodate diverse smart agriculture application scenarios, we designed two distinct prediction architectures: on servers with ample computing power, a Bidirectional Temporal Convolutional Network with self-attention (BiTCN-SA) to achieve highly accurate predictions; and for resource-constrained IoT edge nodes, a lightweight machine learning ensemble (Stack-LGR). Experimental results demonstrate that, in predicting the cumulative mature fruit yield within the next harvesting cycle, BiTCN-SA achieves strong performance with a coefficient of determination ($R^2$) of 0.958 and a root mean square error (RMSE) of 3.154. Simultaneously, the edge-deployed Stack-LGR ensemble maintains stable prediction accuracy ($R^2$ = 0.892) while ensuring acceptable inference latency. This study mitigates the latency limitations of single-environment-driven models. It provides a solution for precise crop yield prediction and tiered computational deployment, with good predictive performance, deployment adaptability, and methodological reference value. Full article

A process-based developmental model was developed to predict leaf appearance and expansion in romaine lettuce (Lactuca sativa L. var. longifolia) using hourly air temperature and daily light integral as environmental inputs. The model consisted of two linked modules representing leaf appearance and individual leaf expansion as distinct biological processes. The leaf appearance module calculated the hourly leaf tip appearance rate as the product of a beta-type nonlinear temperature response function and a developmental stage weighting function based on growing degree days accumulated above a base temperature of 4 °C. The leaf expansion module estimated individual leaf expansion using photothermal age, a Gompertz growth function, and a leaf length–area allometric relationship, with potential leaf length determined by the mean growing temperature and leaf rank. The optimum temperatures for leaf appearance rate and potential leaf length differed by approximately 6 °C (26.7 °C vs. 20.4 °C), indicating distinct temperature response patterns between the two developmental processes. Model calibration was performed using datasets (n = 437) collected from a temperature-gradient greenhouse with a nutrient film technique hydroponic system across the spring, summer, and autumn growing seasons, yielding an overall model efficiency (EF) of 0.92 and a root mean square error (RMSE) of 4.26 leaves for leaf appearance, and an EF of 0.80 and an RMSE of 448.1 cm² for leaf expansion. Independent model evaluation was also performed using datasets (n = 132) obtained from a commercial greenhouse with either a deep flow technique hydroponic system or a perlite-based substrate system across the same three growing seasons, yielding an EF of 0.96 and an RMSE of 2.24 leaves for leaf appearance, and an EF of 0.92 and an RMSE of 216.8 cm² for leaf expansion. These results demonstrate that the model effectively described leaf appearance and expansion in romaine lettuce across the tested soilless culture systems under greenhouse conditions, highlighting its potential as a leaf development module for integration into canopy photosynthesis and biomass production models. Full article

Supplemental light spectrum strongly influences crop physiology, quality, and marketability in controlled-environment horticulture. This study evaluated greenhouse-grown basil (Ocimum basilicum L.) under natural daylight supplemented with 250 µmol m⁻² s⁻¹ of multi-band WHITE or narrow-band RED, GREEN, and BLUE LEDs, compared with non-supplemented controls (CNT). Diurnal gas exchange, cumulative photosynthetic activity, quality, sensory traits, and productive responses were assessed to identify spectrum-specific effects. WHITE supplementation produced the highest net photosynthesis (P_n) throughout the photoperiod, increasing carbon assimilation by up to 90% during active supplementation compared with CNT, while improving stomatal–photosynthetic coordination and titratable acidity. BLUE light promoted the highest stomatal conductance and transpiration, whereas RED induced faster stomatal closure without affecting the plant’s final biomass. GREEN supplementation showed intermediate physiological responses while enhancing fresh biomass, leaf brightness, and greenness. Spectral treatments significantly affected chlorophyll content, colorimetric parameters, sensory descriptors, aroma typicity, and texture, although antioxidant activity and overall consumer acceptability remained largely stable. WHITE and CNT samples achieved the highest sensory appreciation, while BLUE supplementation increased bitterness and reduced aromatic typicality. Overall, no single spectrum optimized all production objectives. WHITE provided the most balanced strategy for maximizing photosynthetic efficiency and premium-quality traits, whereas RED, GREEN, and BLUE offered more specialized benefits related to biomass increase, visual quality, or pigmentation. These findings highlight precision spectral management as an effective tool for tailoring basil production to specific agronomic and commercial targets in protected cultivation systems. Full article