Search Results (1,762)

Spent mushroom substrate (SMS) is a major agricultural byproduct whose complex lignocellulosic matrix hinders direct reuse and poses environmental risks when stockpiled. This study evaluated whether pretreatment with plant growth-promoting rhizobacteria (PGPR) could enhance SMS as a soil amendment for blueberry cultivation. Two PGPR-treated SMS formulations, along with raw SMS and a blank control, were applied to blueberry seedlings in a 10-month greenhouse experiment. Plant height, rhizosphere soil nutrients, enzyme activities, lignocellulose fractions, and the microbial communities were monitored over three growth phases and four sampling points. PGPR-treated SMS significantly increased blueberry height gain during the fast-growing phase (June–September) and sustained elevated levels of organic carbon, nitrogen, phosphorus, and potassium throughout the experiment. Activities of cellulase, xylanase, laccase, peroxidase, protease, and lipase were markedly enhanced, accompanied by reduced lignin and cellulose contents and persistently high glucose availability. The amendments reshaped bacterial and fungal communities, enriching Bacillota, Acidobacteriota, Acidibacter, and Hyphomicrobium, and increasing alpha diversity, with clear structural separation from controls in principal coordinate analysis. Correlation and principal component analyses linked improved plant growth to nutrient availability, enzyme stimulation, and specific microbial taxa. These findings indicate that PGPR-treated SMS acts as a multifunctional amendment that promotes lignocellulose degradation, sustains soil fertility, and restructures the rhizosphere microbiome, offering a sustainable recycling strategy for horticultural production. Full article

Greenhouses in subtropical and tropical regions must be designed as agricultural engineering systems adapted to local climates, rather than simply replicating the “insulation” models of temperate areas. Under extreme climatic conditions such as persistent high temperatures, intense solar radiation, high humidity, heavy rainfall, and frequent extreme winds, greenhouses transform from enclosed insulation layers into selective climate filters, mitigating crop stress while maintaining close contact with the outdoor environment. This paper summarizes how these climate drivers are reshaping the use, structure, and control concepts of greenhouses, emphasizing that the performance of warm-zone greenhouses depends primarily on heat dissipation, humidity management, and biohazard control, rather than heating and insulation. In this review, we analyze the climatic boundary conditions that define warm-climate conservation cultivation, including long-term overheating risk, high UV radiation, vapor pressure deficit, and suppressed condensation tendency, as well as storm-induced uplift and dynamic loads. These constraints necessitate unique structural forms: tall, lightweight, well-ventilated building types with large roof and side openings, roof geometries that facilitate rainwater runoff, sophisticated drainage systems, and corrosion-resistant materials suitable for humid and coastal environments. Because insect netting significantly reduces ventilation, pest control and temperature regulation become co-design issues, requiring oversized vents, optimized airflow paths, and hybrid roof–mesh structures. Ventilation is considered the primary climate-control mechanism, supplemented by passive cooling measures such as shading and radiation/optical management (e.g., diffuse films and near-infrared-selective films). Active evaporative cooling is considered a conditional measure due to humidity limitations and disease risks. This paper also integrates the impacts on specific crops (fruits and vegetables, leafy greens, and orchids). It highlights emerging trends: typhoon-resistant and adaptive geometries, computational fluid dynamics (CFD)-based design, and sensor-rich IoT/digital twin control frameworks. These principles collectively establish a coherent design framework for achieving resilient, resource-efficient greenhouse production in warm climates. Full article

Crop straw is an abundant lignocellulosic resource, but its efficient bioconversion is hindered by the recalcitrant structure of plant cell walls. This study integrated degradation phenotyping, enzyme activity assays, whole-genome analysis, and comparative transcriptomics to link the wheat-straw degradation performance of strain XH2 with its enzymatic and molecular responses. Strain XH2 was isolated from fully decomposed compost collected in Anhui Province, China, selected based on the formation of a distinct hydrolysis halo on CMC-Congo red agar, and deposited in the China Center for Type Culture Collection (CCTCC) under accession number CCTCC M 2025519. Morphological, cultural, phylogenetic, and genomic analyses identified strain XH2 as Streptomyces thermocarboxydus. Its degradation capacity was evaluated during 28 days of cultivation by measuring straw degradation, lignocellulosic components, scanning electron microscopy (SEM), and extracellular enzyme activities. S. thermocarboxydus XH2 caused marked disruption of the wheat-straw surface and achieved a degradation rate of 31.45%. Cellulose and hemicellulose contents decreased from 41.10% to 28.87% and from 30.72% to 16.85%, respectively, whereas lignin decreased from 8.28% to 6.30%. Endoglucanase activity, filter paper activity (FPase, an indicator of total cellulase activity), and xylanase activity peaked on day 7, reaching 35.99, 17.68, and 37.01 U/mL, respectively. Genome analysis revealed multiple genes encoding cellulases and hemicellulases. Comparative transcriptomic analysis after 72 h of cultivation in wheat-straw medium identified 1614 differentially expressed genes relative to Gause No. 1 medium, with major enrichment in ABC transporters and fructose and mannose metabolism. Most genes associated with polysaccharide degradation were upregulated. These findings link the degradation phenotype of S. thermocarboxydus XH2 to its enzymatic and molecular responses and support its further evaluation as a candidate for wheat-straw bioconversion under greenhouse and field conditions. Full article

Environmental sustainability of natural and synthetic fibers used in textiles and composites depends on their impacts throughout production, use, and end-of-life (EoL) stages. Natural fibers are renewable and biodegradable but may require substantial water and agricultural inputs, whereas synthetic fibers contribute to fossil resource depletion, microplastic pollution, and persistent waste generation. Natural fibers are often regarded as more sustainable alternatives to synthetic fiber; however, evidence from a life cycle assessment (LCA) reveals a more nuanced reality. As demand for fiber-based materials increases across textile and composite applications, a deeper understanding of the environmental implications of both natural and synthetic options becomes essential. This review compares these fiber categories from a life cycle perspective, examining carbon footprint, energy demands, resource consumption, and EoL pathways. Natural fibers such as cotton, flax, jute, hemp, sisal, banana, coir, and emerging plant-based alternatives offer advantages including biodegradability and carbon sequestration during cultivation. Nevertheless, agricultural practices and subsequent industrial processing require substantial land, water, and chemical inputs. Synthetic fibers, predominantly derived from fossil resources, provide a long service life and consistent performance but are associated with high greenhouse gas (GHG) emissions, dependence on non-renewable feedstocks, microplastic pollution, and broader environmental impacts. By presenting a comprehensive life cycle-based comparison, this review identifies the conditions under which each fiber type may offer environmental benefits, supporting informed material selection for sustainable development. Full article

The scarcity of effective selective herbicides has made weed management a limiting factor for alfalfa yield and large-scale cultivation. This study aimed to preliminarily screen selective herbicides with acceptable safety to alfalfa and establish an effective weed management program based on sequential pre- and post-emergence herbicide applications through greenhouse screening and multi-year field trials. The greenhouse herbicide safety evaluation showed that, among the 22 herbicides tested, the post-application of 2 pre-emergence herbicides (s-metolachlor and prodiamine) and 5 post-emergence herbicides (benazolin, bentazon, fluazifop-p, imazethapyr, and nicosulfuron) showed a relatively higher survival rate (~4× the standard dose) on two alfalfa cultivars through assessing visual efficacy, plant height, and dry biomass per plant. However, even herbicides that result in relatively high plant survival can produce induce significant growth inhibition and biomass reduction when applied at high doses. Those herbicides were further tested under field conditions (2020–2023) for evaluation of weed control efficiency and alfalfa plant biomass yield potential and nutritive values. Among the herbicide application regimes, the pre-application of prodiamine followed by post-application of bentazon or fluazifop-p showed the most effective weed control efficacy (70–85% reduction of weed biomass in the alfalfa plot), which was greater than the reductions achieved with all single-herbicide applications (24–48%) and sequential application of s-metolachlor followed by the five post-emergence herbicides (25–46%). Additionally, the optimized combinations had no significant effect on the 1st and subsequent alfalfa plant height compared to that of alfalfa in weed-free control. No significant reductions in alfalfa yield or nutritive value were observed in the herbicide-treated plots treated with prodiamine followed by bentazon or fluazifop-p compared with the weed-free control. Collectively, sequential application combining pre- (prodiamine) and post-emergence (bentazon or fluazifop-p) herbicides shows effective weed control in alfalfa under the tested conditions, which provides a useful weed management program to enhance alfalfa forage production and nutritive values. Full article

Accurate detection of blooming roses and flower buds is essential for greenhouse phenotyping, cultivation scheduling, harvest planning, and yield management. However, UAV-derived greenhouse imagery presents major challenges because rose targets are often small, densely distributed, partially occluded, and visually similar to complex backgrounds. This study proposes a lightweight rose detection framework that combines multi-source dataset construction with an improved YOLOv12n-based detector, termed BloomRoseNet. A GreenHouse Rose dataset was constructed by integrating self-collected UAV overhead images, screened RoseTracker images, and supplementary multi-view rose images to increase diversity in scale, growth stage, viewpoint, and background complexity. BloomRoseNet introduces task-oriented improvements for fine-grained feature extraction, adaptive feature fusion, and attention-enhanced detection. The supplementary multi-view data improved precision, recall, and mAP@50 from 85.2%, 82.8%, and 89.2% to 86.1%, 85.3%, and 90.5%, respectively. Compared with the baseline YOLOv12n, BloomRoseNet increased precision, recall, mAP@50, and mAP@50:95 by 3.2, 3.3, 3.6, and 1.6 percentage points, respectively, while reducing parameters from 2.55 M to 2.08 M and model size from 5.5 MB to 4.5 MB. The model also maintained real-time inference capability and stronger robustness under blur, occlusion, and illumination disturbances. The proposed framework provides an effective and practical solution for UAV-based greenhouse rose monitoring and supports precision cultivation management. Full article

Global warming-induced temperature increases adversely affect plant growth, exacerbating agricultural risks and threatening global food security. These challenges are particularly acute for important crops like non-heading Chinese cabbage (NHCC; Brassica campestris L. syn. B. rapa), a nutritionally and economically significant leafy vegetable cultivated worldwide. While breeding for thermotolerance has been explored to stabilize summer supplies, these efforts remain insufficient under complex field conditions, necessitating cost-effective and practical alternatives. Here, we demonstrate that biochar derived from enoki mushroom (Flammulina velutipes) spent mushroom substrate (SMS-BC) significantly alleviates heat-induced growth suppression in greenhouse-cultivated NHCC. SMS-BC alters the soil nutrient profile and enhances available phosphorus and potassium through its unique feedstock properties, including its filamentous architecture and surface functional groups. Furthermore, SMS-BC may reshape microbial community composition of NHCC under heat stress. Our findings establish SMS-derived biochar as an effective soil amendment for alleviating heat stress-induced growth suppression in NHCC. Full article

Improper disposal of coffee-processing by-products can cause environmental pollution, greenhouse gas emissions, and resource loss, whereas their reuse in coffee plantations may support sustainable production. This review systematically examines the material properties, stabilization methods, field application pathways, agronomic effects, quality responses, and environmental risks of coffee-pulp-type by-products in cultivation. Relevant studies published up to June 2026 were retrieved from Web of Science, Scopus, ScienceDirect, SpringerLink, Google Scholar, and CNKI and qualitatively synthesized along the soil–plant–quality continuum. Current evidence suggests that properly stabilized materials, applied at appropriate rates, can improve soil organic matter, structure, water and nutrient retention, microbial activity, plant growth, photosynthesis, and crop yield in plantations. They may also indirectly influence green bean quality by regulating sugars, amino acids, chlorogenic acids, and caffeine. However, these effects depend strongly on material properties, maturity, application rate, coffee genotype, soil and climatic conditions, and management practices. Excessive or insufficiently decomposed materials may cause soil acidification, phytotoxicity, oxygen depletion, nutrient imbalance, and yield–quality trade-offs. Overall, recycling within plantations can turn processing waste into farm inputs, reinforce on-farm carbon and nutrient cycles, ease disposal burdens, and advance BCG and wider circular-economy principles in practice. 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

Investigating the coordination between greenhouse-oriented cultivated-land transformation and low-carbon agricultural emission performance is important for optimizing land use and advancing agricultural decarbonization. Using data for 31 mainland Chinese provinces in 2010, 2016, and 2022, this study constructs corresponding indices and examines their relationship using a coupling coordination model, spatial autocorrelation analysis, fixed-effects estimation, and a spatial Durbin model. The results show that greenhouse-oriented transformation increased but remained regionally uneven, with higher levels concentrated in eastern and northern coastal regions. The interprovincial mean of standardized agricultural carbon-emission intensity increased from 0.3225 in 2010 to 0.3557 in 2016, representing a 10.29% rise, before declining to 0.3279 in 2022—7.83% below the 2016 level but 1.66% above the 2010 level. Coupling coordination improved initially and subsequently entered a period of adjustment and convergence, while remaining predominantly low to moderate. It also exhibited significant positive spatial clustering. Rural economic development was negatively associated with local coordination, whereas population density showed a positive but model-sensitive association, and the urban–rural income gap produced limited spillover effects. The spatial autoregressive coefficient was insignificant. Facility-agriculture policies should therefore prioritize quality improvement, clean energy, input reduction, and resource efficiency rather than continued scale expansion. Full article

Driven by global climate change and Taiwan’s Net-Zero initiatives, high-input livestock farming faces urgent pressures to transform. Pennisetum purpureum, a primary forage and bioenergy crop, traditionally relies on heavy chemical nitrogen inputs, thereby exacerbating its carbon footprint. This study evaluated a sustainable cultivation model integrating legume intercropping with reduced fertilization. A field trial in central Taiwan utilizing a Completely Randomized Design (CRD) compared four treatments: conventional full nitrogen (800 kg N/ha), sunn hemp (Crotalaria juncea) intercropping with half nitrogen, sunn hemp intercropping only, and organic compost. We monitored biomass, forage chemistry, and soil organic carbon (SOC) dynamics. The sunn hemp plus half-nitrogen treatment achieved dry matter yields (30–35 Mg/ha) and crude protein levels (7.1–7.4%) statistically equivalent to the full-nitrogen control, significantly surpassing other groups. While short-term SOC stocks (72–81 Mg C/ha) showed no significant differences, trends indicated carbon accumulation in deep soil (30–50 cm). Consequently, substituting 50% of chemical nitrogen through biological fixation proves a viable strategy to maintain yield while reducing inputs. This model sustains productivity and mitigates greenhouse gas emissions, offering an economically feasible, potential carbon-negative solution for tropical forage systems. Full article

High temperature is a primary meteorological hazard restricting crop yield and quality, particularly in semi-automatic greenhouses. However, the accuracy of crop models under controlled high-temperature conditions in greenhouses has rarely been examined. To address this, our study evaluated the AquaCrop (7.1) and DSSAT (4.8.2) models for greenhouse-grown tomato under high-temperature conditions. Field experiments were conducted over two consecutive years (2024 and 2025) with two temperature treatments (TH: daily maximum temperature > 35 °C; TD: daily maximum temperature < 35 °C) and two irrigation levels (WH: 100%E_pan; WD: 60%E_pan, where E_pan is cumulative pan evaporation). Key indicators were continuously measured, including canopy cover, soil water content, above-ground biomass, yield, and water consumption. Additionally, the XGBoost algorithm was introduced to estimate reference crop evapotranspiration across different treatment combinations, facilitating a multi-scenario analysis. The results indicated that under both TD and TH conditions, the AquaCrop model accurately simulated canopy cover (RMSE ≤ 6.84%, NRMSE ≤ 12.27%, EF ≥ 0.92), yield (PE ≤ 12.20%), above-ground biomass (PE ≤ 2.34%), and water consumption (PE ≤ 10.33%) across different irrigation levels. However, its performance in simulating soil water content declined markedly under the TH treatment, with EF becoming negative in some cases (−1.66 ≤ EF ≤ 0.43), indicating that the model was largely ineffective for SWC under these conditions. The DSSAT model exhibited lower simulation accuracy under the TH treatment compared to the TD treatment, and its overall performance was slightly inferior to that of the AquaCrop model (RMSE ≤ 15.79 mm, NRMSE ≤ 14.54%, −1.68 ≤ EF ≤ 0.46). Scenario analysis of 20 temperature and irrigation combinations, integrating XGBoost-derived ET₀ into the AquaCrop model, revealed that maximizing greenhouse tomato yield requires a combination strategy of 1.1E_pan and 34 °C, whereas optimizing water use efficiency is best achieved under 0.9E_pan and 32 °C. These findings provide a theoretical basis and technical support for the application of crop models and the development of environmental regulation strategies in greenhouse cultivation under high-temperature conditions. Full article

Converting conventional rice monoculture to co-culture with fish is a common strategy to increase farmers’ incomes. The Chinese soft-shell turtle is a typical high-value species for paddy co-culture, but the effects of the co-culture system (RT) on greenhouse gas emissions (GHG) are poorly understood. This study evaluated the impacts of RT on economic benefits and greenhouse gas emissions. Our results showed that RT co-culture did not significantly increase rice yield, but it provided additional income from turtle sales, increasing net profits. GHG emission responses to RT differed between the two farms. In farm 1, RT increased CH₄ emissions from the rice-cultivated area but decreased them from the turtle culture area, resulting in no net effect on total CH₄ emissions. The reduction of CH₄ emissions in the turtle culture area was associated with lower DOC content and reduced methanogenic gene mcrA abundance. In farm 2, RT reduced CH₄ emissions from both areas, lowering total CH₄ emissions by 36.5%, which was linked to less water flooding and higher methanotrophic gene (pmoA) abundance. Total N₂O emissions were not significantly affected by RT at either farm. RT increased indirect GHG emissions, but these accounted for only 7.2–16.9% of total emissions. Overall, RT reduced total GHG emissions by 27.7% at farm 2, with no significant change at farm 1. These findings suggest that RT can enhance economic benefit while lessening total GHG emissions. Full article

Camera-based visual sensing provides a non-destructive and scalable approach for monitoring strawberry diseases and pests in greenhouse environments. However, greenhouse images acquired under practical cultivation conditions often contain early-stage tiny lesions, complex leaf backgrounds, uneven target scales, illumination variations, and partial occlusions, making accurate and efficient visual detection challenging. To address these issues, this study proposes YOLOv8n-DSLW (YOLOv8n enhanced by Dense reuse, Shuffle attention, LSKA–LAMP lightweight modeling, and Wise-IoU optimization), an AI-enabled vision-sensing detection model based on YOLOv8n for tiny strawberry disease and pest detection. Specifically, Shrink Residual Dense Block (ShrinkRDB) dense connection blocks and the C2f with Shuffle Attention (C2fSA) module are introduced to preserve weak lesion textures and suppress background interference in greenhouse visual data. A high-resolution P2 detection layer combined with Wise-IoU (WioU) dynamic regression loss is further incorporated to enhance tiny-target perception and localization. In addition, the Spatial Pyramid Pooling-Fast with Large Separable Kernel Attention (SPPF-LSKA) module strengthens contextual modeling under occlusion and clutter, while Layer-Adaptive Magnitude-based Pruning (LAMP) is adopted to mitigate model redundancy and improve the accuracy–efficiency balance. Experiments on a self-collected greenhouse strawberry disease and pest dataset show that YOLOv8n-DSLW achieves a mean Average Precision at 0.5 IoU threshold (mAP@0.5) of 94.3% and a mAP@0.5:0.95 of 77.5%, outperforming the YOLOv8n baseline. The final model has a parameter count of 4.386 M and a computational cost of 27.6 GFLOPs, achieving a frame rate of 45 FPS on the test workstation. It shows application potential for real-time visual monitoring in greenhouses under controlled data acquisition conditions. The results demonstrate that the proposed method improves tiny lesion detection under dense targets, complex backgrounds, and leaf occlusions, providing an AI-enabled vision-sensing framework for automated strawberry health monitoring in greenhouses. Nevertheless, due to limitations associated with imaging equipment, dataset representativeness, and the inherent constraints of the algorithm, further optimization and validation are required to support large-scale field deployment. 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