Search Results (167)

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

Healthy peatlands provide valuable ecosystem services. Peat extraction damages peatlands, leading to carbon emissions. One of the main reasons for peat extraction is for use in horticulture. Replacing peat with recycled organic materials in horticulture is critical to preserve the valuable ecosystems provided by peatlands and to establish more circular supply chains that are reliant on recycling rather than extraction. Despite the strong policy commitment and budget allocation to restore peatlands, the demand for peat-based growing media remains high and drives most of the peat demand. In our research, we mapped the growing media supply chain, held semi-structured interviews with key stakeholders representing different interests, and surveyed amateur gardeners in the UK to understand the bottlenecks experienced by each profile in ending peat use and how to overcome them. We employed semi-structured key expert surveys to understand the supply chain dynamics and consumer demand, informed by these early interviews and the previous literature, we prepared and distributed an online consumer survey and interviewed supply-side stakeholders to understand their perspectives. The findings indicate that the barriers of availability, cost, and performance are shared between the supply-and-demand-side stakeholders. A portfolio of financial, educational and logistic interventions is required to simultaneously support the supply side to accelerate the transformation of production and supply patterns and to aid the demand side to adapt to growing with compost of recycled organic materials. The policies promoting recycled organic material use in horticulture must be coordinated within the UK and with other parts of Europe focusing on the elimination of the peat content in products rather than peat extraction to avoid extraction and the associated destruction of peat stocks elsewhere. Full article

The evolution of global agriculture encourages the extensive use of pesticides although significant concerns regarding their impact on human health and the environment must be considered. The present paper highlights the presence and concentrations of various pesticide residues in fruits and vegetables available on Romanian markets. A total of 74 pesticide compounds authorized for agricultural use were identified and quantified in 620 randomly selected samples spanning a wide range of horticultural products by employing the QuEChERS extraction method and liquid chromatography–mass spectrometry (LC-MS/MS). The most often detected pesticides comprised boscalid and azoxystrobin, present in 42% and 37% of apple and strawberry samples, respectively, with mean concentrations of 0.12 mg/kg and 0.09 mg/kg. In cucumbers and tomatoes, difenoconazole and acetamiprid were predominant, detected in 35% and 40% of samples, with average residue amounts of 0.08 mg/kg and 0.07 mg/kg, respectively. Statistical analysis, achieved with Python 3.13.2, the pandas library (alongside descriptive statistics), and ANOVA, revealed significant variations in residue levels based on the product type and geographic origin. Boscalid and azoxystrobin were commonly encountered in apples and strawberries while difenoconazole and acetamiprid predominated in cucumbers and tomatoes. Even though the majority of pesticide residues conformed to EU maximum residue limits (MRLs), about 6% of samples, generally from imported products, displayed some residue concentrations approaching critical thresholds, with the highest exceedance observed for chlorpyrifos and lambda-cyhalothrin at concentrations of up to 0.25 mg/kg. This research provides a comprehensive overview of pesticide residues prevalence in Romania’s fresh product supply while, at the same time, supporting consumer awareness initiatives and evidencing the critical demand for continuous monitoring and strengthened regulatory frameworks for food safety. Full article

Postharvest spoilage of horticultural produce is a significant challenge, contributing to substantial food waste and economic losses. Traditional preservation methods, such as chemical preservatives and fungicides, are increasingly being replaced by sustainable, chemical-free alternatives. Microbial interventions using beneficial bacteria, fungi, and yeasts have emerged as effective solutions to enhance the postharvest quality and extend shelf life. Advancements in omics technologies, such as metabolomics, transcriptomics, and microbiomics, have provided deeper insights into plant–microbe interactions, facilitating more targeted and effective microbial treatments. The integration of artificial intelligence (AI) and machine learning further supports the selection of optimal microbial strains tailored to specific crops and storage conditions, further enhancing the treatment efficacy. Additionally, the integration of smart cold storage systems and real-time microbial monitoring through sensor technologies offers innovative approaches to optimize microbial interventions during storage and transport. This review examines the mechanisms through which microbes enhance the postharvest quality, the role of omics technologies in improving microbial treatments, and the challenges associated with variability and regulatory approval. Amid growing consumer demand for organic and sustainable solutions, microbial-based postharvest preservation offers a promising, eco-friendly alternative to conventional chemical treatments, ensuring safer, longer-lasting produce while reducing food waste and environmental impact. Full article

Lettuce is the most widely consumed leafy vegetable in the world. Its quality and yield depend highly on the growing conditions, including the growing substrate. Peat is commonly used as a growing substrate, but there is an increasing interest in finding alternatives to reduce peat usage. One potential alternative is vermicompost, and this study aims to investigate the impact of vermicompost as an additive to a peat substrate on the quality of lettuce seedlings and yield. This research was carried out in a greenhouse covered with a polymer film at the Institute of Horticulture of the Lithuanian Agricultural and Forestry Research Center. Lettuce seedlings were grown in peat with varying amounts of vermicompost (0%, 10%, 20%, 30%, 40%, or 50% vermicompost). Various parameters such as lettuce growth, biometric data, the content of pigments in the leaves, and the accumulation of elements (N, P, K, Ca, Mg) were evaluated. The addition of vermicompost, regardless of its amount, significantly increased plant height (from 7.5 cm in control up to 10.9–11.3 cm with vermicompost), the number of leaves (up to 4.2–4.6), the leaf area (up to 107–131 cm²), and the percentage of dry matter accumulation (up to 6.4–7.5%). Vermicompost also had a positive effect on photosynthesis, resulting in higher yields and a better quality of lettuce. The summarized research results demonstrate the potential of using vermicompost in the production of high-quality lettuce. Full article

Nitrogen is a crucial environmental factor influencing lettuce growth, development, and quality formation. This study aimed to determine the relationship between plant growth, nutritional quality formation, and different nitrogen levels of lettuce. A machine learning approach was also applied to data collected from RGB and hyperspectral imaging systems. Traditional methods for nitrogen diagnosis in lettuce, such as laboratory-based analysis of plant samples, are labor-intensive, time-consuming, and lack real-time monitoring capabilities. In contrast, the deep learning models used in this research can make full use of the original data from imaging systems. Nondestructive techniques have the ability to handle complex relationships in the data, enabling more accurate and efficient nitrogen diagnosis. Collected spectral features were combined with chemometrics, and a lettuce nitrogen regression diagnostic model was trained. Furthermore, lettuce crop growth was assessed using a model development of environmental and plant physiological parameters. Additionally, nitrogen fertilization was precisely assessed using developed models. Lettuce cultivation experiments under different nitrogen levels showed the best physiological and biochemical indicators performance when the nitrogen concentration reached 18.75 mmol·L⁻¹. Using machine learning with hyperspectral reflectance in nitrogen diagnostics, random forest showed excellent performance with the highest R², MSE, and MAE of 0.7012, 8.940, and 2.1859, respectively. ShuffleNet-v2-1.0 obtained a high R² of 0.9592, MSE of 132.9974, and MAE of 8.1430 regarding transfer learning and hyperspectral images. Applying the transfer learning technique in RGB images exhibited EfficientNet-v2-s, the best model for precise determination of nitrogen diagnostics, with R² of 0.9859, MSE of 24.0755, and MAE of 2.3433. Current research comprehensively provides both a theoretical basis and practical solutions for precision nitrogen fertilization in lettuce cultivation. Its implications hold significance for the intelligent management of horticultural crop production. Full article

Accurately determining the hydraulic properties of soilless growing media is essential for optimizing water management in container-based horticulture and agriculture. The very rapid estimation of hydraulic properties using a Mini Disk Infiltrometer has great potential for practical use compared to the very time-consuming standard methods. The objectives of this study were (1) to calibrate simulated cumulative stepwise infiltration under different suctions with the measured data from Mini Disk Infiltrometer, (2) to evaluate the efficiency of the Hydrus-2D inverse model to predict water dynamics through substrates, (3) to compare the substrate hydraulic parameters obtained through the numerical inversion model to those obtained via laboratory methods, and (4) to provide recommendations on how to effectively use the MDI-based method for practical applications. This study employs numerical inversion of Mini Disk Infiltrometer (MDI) data to estimate the hydraulic parameters of three different growing media, namely white peat, thermally treated wood fibre (WF4), and Seedling substrate. Infiltration experiments were conducted under suction-controlled conditions using varying initial moisture contents, followed by numerical simulations using the Hydrus-2D model and the Van Genuchten equation to describe the hydraulic parameters. The results demonstrated strong agreement between observed and simulated infiltration data, particularly under moistened conditions, with high R² > 0.9 values indicating the model’s effectiveness. However, discrepancies were observed for substrates in their initial dry state, suggesting limitations in capturing early-stage infiltration dynamics. The findings highlighted the potential of numerical inversion methods for estimating substrate hydraulic properties but also revealed the need for methodological refinements. Modifying the Van Genuchten model or exploring alternative approaches such as the Brooks and Corey model may enhance accuracy. Extending the suction range of measurement techniques is also recommended to improve parameter estimation. This study provides important evidence that the inverse method based on MDI is an effective tool for rapidly determining the hydraulic functions of substrates, which are important in promoting sustainable horticultural practices. Future research should focus on refining parameter estimation methods and addressing model limitations to enhance the reliability of hydraulic property assessments in soilless growing media. Full article

Coffee is a beverage that contains a high concentration of bioactive compounds, particularly polyphenols. These compounds significantly contribute to the polyphenol intake in the diet and have been shown to have beneficial effects on consumer health. The objective of this research was to conduct a comparative analysis of the polyphenolic composition of coffee beans and infusions obtained from coffee beans sourced from both organic and conventional farming practices while taking into consideration variations in roast intensity and geographical origin. The lyophilized coffee grounds and infusions derived from these grounds were also subjected to analysis. The antioxidant activity was measured by using the radical ABTS, and the quantitative and qualitative analysis of polyphenolic compounds was conducted by HPLC. The conventional coffee samples were richer in chlorogenic acid, catechin, and caffeic acid. However, the coffee beans from organic farming contained more gallic acid, epigallocatechin gallate, and quercetin than those grown conventionally. We did not observe significant differences among the coffee plant production sites in Ethiopia, Sumatra, and Peru, but Peru had the poorest amount of polyphenols when compared to Ethiopia and Sumatra. Coffee infusions prepared from organic coffee beans were characterized by a significantly high sum of identified polyphenols. A higher content of caffeine was observed in the organic coffee bean samples than in the conventional coffee bean samples. Conventional coffee beans were characterized by stronger antioxidant activity than organic beans. Coffees from different parts of the world were characterized by different profiles of polyphenol compounds. Moreover, the coffee beans from Ethiopia were characterized by the highest caffeine content. However, among the different geographical areas of coffee beans, the highest antioxidant activity was detected in the coffee beans from Sumatra. Coffee grounds also have the potential to be used as compounds for the cultivation of horticultural plants, and they can be used as a source of numerous health-promoting compounds in the food and cosmetics industries. Full article

To select a good quality watermelon, one needs the ability and experience to recognize specific patterns in its visual characteristics. As buyers usually cannot taste the watermelon beforehand, the outer patterns of a good quality watermelon may vary depending on the perspective of the purchaser. As a result, there is a gradual adoption of new generative artificial intelligence (AI) tools in the field of horticulture. These tools are expected to minimize bias in human perception when determining the quality of a watermelon based on its outer characteristics. This study aimed to compare the quality of watermelons selected by generative AI with a panel sensory evaluation test. The results of the two case studies indicate a significant difference in the quality of the generative AI-selected watermelons. As an average, watermelon evaluators favored the watermelons selected by ChatGPT as the best based on the Wilcoxon rank sum test and paired t-test (p < 0.05). In conclusion, watermelons can be selected by ChatGPT with minimal effort, promptly meeting consumer expectations. Full article

Cherries, as high-value horticultural products, have long faced preservation challenges due to their perishable nature and limited postharvest longevity. During storage and transportation, these stone fruits are particularly susceptible to quality deterioration and pathological decay, significantly impacting commercial viability and consumer acceptance. Modified atmosphere preservation (MAP) technology has emerged as the predominant preservation method for cherry storage, recognized for its operational safety, environmental controllability, and technical reliability. This review systematically examines the physiological degradation mechanisms of cherries during storage, identifies critical environmental factors influencing decay patterns, and synthesizes recent advancements in MAP applications. The analysis encompasses technological principles and efficacy evaluations of atmospheric modification, with particular emphasis on how regulated temperature, humidity, and gas composition parameters affect the bioactive compound retention, organoleptic properties, and overall eating quality—crucial factors for enhancing consumer satisfaction and market value. Furthermore, this paper critically addresses current technological limitations, including implementation costs, operational complexity, and environmental sustainability concerns. Finally, it proposes innovative optimization strategies and outlines future development trajectories to advance technological refinement and promote sustainable innovation in terms of cherry preservation methodologies. Full article

Red sweet peppers are economically important since they are widely farmed and consumed worldwide. As a high-value crop, it has a significant impact on the horticulture economy. This study aimed to improve the quality of sweet pepper fruits (total polyphenols; chlorophyll A and B; lycopene, β-carotene, tannins, ABTS, DPPH, protein and 15N) using three hybrids—Kornelya F1, Kaptur F1 and Napoca F1—four fertilization methods (chemical, organic, biologic and unfertilized), and two irrigation regimes (5200 and 7800 m³·ha⁻¹, respectively). The results revealed substantial similarities between organic and conventional management practices. From a genetic point of view, ‘Kornelya’ cultivar reacted well with most compounds with antioxidant effects. This study revealed that peppers react positively when subjected to hydric and nutrient stress, with the fruits having the highest values for total polyphenols, chlorophyll A and B, lycopene, ABTS, and DPPH. Following the interactions between factors, ‘Kornelya’ reacted positively to organic and unfertilized methods with an irrigation regime of 5200 m³·ha⁻¹. Full article

More than ten species of the Actinidia Lindl. genus bear edible fruits rich in biologically active compounds, which are essential and beneficial for human health. The most popular cultivars today are the large-fruited Actinidia species, A. deliciosa and A. chinensis, commonly known as kiwi. However, small-fruited kiwi cultivars are gaining prominence due to their high nutritional value, superior cold resistance, and suitability for temperate climates. In Russia, these are represented by Far Eastern species: A. arguta, A. kolomikta, and A. polygama. Despite increasing consumer interest, Russian Actinidia cultivars remain little studied, with fragmented genetic data available for breeding purposes. Our objective was to analyze the Actinidia collection at the Federal Horticultural Center for Breeding, Agrotechnology, and Nursery and the N.V. Tsitsin Main Botanical Garden (MBG RAS, Moscow), which includes samples from four species, A. kolomikta, A. arguta, A. polygama, A. purpurea, interspecific hybrids, and derived varieties, using RAD sequencing. We assessed the genetic variability of all species, identified population groups within A. kolomikta and A. arguta based on origin, determined ploidy levels across the collection, and identified a set of SNP markers associated with valuable agronomic traits. Full article

The COVID-19 pandemic has reshaped various aspects of life, influencing consumer behaviors and economic activities worldwide. This paper delves into the shifts in consumer preferences and shopping patterns for gardening products and services during and after the pandemic era. Through nationwide online surveys conducted from 2020 to 2022, we analyze changes in time spent and expenses on gardening and identify key demographic and pandemic-related factors influencing these behaviors during the pandemic era. We employed both generalized and standard ordered logistic regression models to assess the impact of various variables on the ordinal dependent variable. The findings reveal a significant surge in both time spending on home gardening activities and money invested in gardening products and services, especially among individuals who considered gardening beneficial for well-being and as a family activity. Notably, the preference for online shopping and mail delivery for gardening supplies emerged as a prominent trend during the pandemic, accompanied by a significant increase in the purchase of fruit plants, vegetable plants, and seeds, indicating that primary motivation for gardening was self-food consumption. These insights provide valuable guidance for businesses in the green industry, informing production, marketing practices, and economic recovery efforts post-COVID-19. Full article

The Eurasian grapevine (Vitis vinifera L.) is one of the most extensively cultivated horticultural crop worldwide, with significant economic relevance, particularly in wine production. Accurate grapevine variety identification is essential for ensuring product authenticity, quality control, and regulatory compliance. Traditional identification methods have inherent limitations limitations; ampelography is subjective and dependent on skilled experts, while molecular analysis is costly and time-consuming. To address these challenges, recent research has focused on applying deep learning (DL) and machine learning (ML) techniques for grapevine variety identification. This study systematically analyses 37 recent studies that employed DL and ML models for this purpose. The objective is to provide a detailed analysis of classification pipelines, highlighting the strengths and limitations of each approach. Most studies use DL models trained on leaf images captured in controlled environments at distances of up to 1.2 m. However, these studies often fail to address practical challenges, such as the inclusion of a broader range of grapevine varieties, using data directly acquired in the vineyards, and the evaluation of models under adverse conditions. This review also suggests potential directions for advancing research in this field. Full article

Strawberry (Fragaria ananassa) is a widely grown horticultural crop, which exists in red, yellow, and white varieties. In recent years, the white-fleshed strawberry variety is gaining more attention from consumers for its unique taste and appearance, but a comprehensive understanding of the molecular processes governing the ripening of white-fleshed strawberry remains undisclosed. In this study, based on the joint analysis of physiology, metabolome, and transcriptome, we screened and identified the key metabolites that were highly correlated to the maturation of white-fleshed strawberry (cv. ‘snow white’, SW for short) fruits. In contrast to red-fleshed strawberries, SW fruits exhibited three main ripening stages during the maturation, accompanied by the increases in total soluble solid and total sugar and the declines in total anthocyanin and total acid. Metabolomic analysis identified 832 differential accumulated metabolites (DAMs) at the secondary level of LC-MS/MS, and further investigations suggested that the increase in sucrose, citric acid, and epicatechin levels potentially play a role in the ripening process of SW fruits. Furthermore, abscisic acid and methyl jasmonate were recognized as the primary phytohormones involved in the production of these metabolites. The enrichment analysis of RNA-Seq data revealed that the differential expressed genes (DEGs) were primarily attributed to the pathways of ‘Starch and sucrose metabolism’ and ‘Plant hormone signal transduction’ but were undetected in ‘Flavonoid biosynthesis’ at the late ripening stage. Moreover, the de novo biosynthesis pathway, WGCNA, and Pearson correlation analysis indicated a direct relationship between FaSPS1, FaSPP1, and FaSPP2 with sucrose, FaPEPC1, FaV-PPase2, and FaV-PPase3 with citric acid, and Fa4CL2, Fa4CL3, and FaANR1 with anthocyanin. Further analysis revealed a co-expression of MYBs, bHLHs, NACs, and WRKYs with the structural genes mentioned. Overall, our findings uncovered a molecular mechanism regulating the maturation of white-fleshed strawberry, providing valuable insights for enhancing the flavor of white-fleshed strawberry through the gene-editing technique. Full article