Search Results (2,531)

Aronia melanocarpa (black chokeberry) is a medicinally valuable small fruit species, yet its commercial propagation remains limited by low rooting and genotype-specific responses. This study developed an efficient, callus-free micropropagation and rooting protocol using a Shrub Plant Medium (SPM) supplemented with 5 mg/L BAP in large 660 mL jars, which yielded up to 27 shoots per explant. Optimal rooting (100%) was achieved with 0.5 mg/L NAA + 0.25 mg/L IBA in half-strength SPM. In the second phase, supervised machine learning models, including Random Forest (RF), XGBoost, Gaussian Process (GP), and Multilayer Perceptron (MLP), were employed to predict morphogenic traits based on culture conditions. XGBoost and RF outperformed other models, achieving R² values exceeding 0.95 for key variables such as shoot number and root length. These results demonstrate that data-driven modeling can enhance protocol precision and reduce experimental workload in plant tissue culture. The study also highlights the potential for combining physiological understanding with artificial intelligence to streamline future in vitro applications in woody species. Full article

The Solar Agro Savior (SAS) is an innovative solution that is assisted by drones for the sustainable utilization of water and plant disease observation in the agriculture sector. This system integrates an alerting mechanism for humidity, moisture, and temperature variations, which affect the plants’ health and optimization in water utilization, which enhances plant yield productivity. A significant feature of the system is the efficient monitoring system in a larger region through drones’ high-resolution cameras, which enables real-time, efficient response and alerting for environmental fluctuations to the authorities. The machine learning algorithm, particularly recurrent neural networks, which is a pioneer with agriculture and pest control, is incorporated for intelligent monitoring systems. The proposed system incorporates a specialized form of a recurrent neural network, Long Short-Term Memory (LSTM), which effectively addresses the vanishing gradient problem. It also utilizes an attention-based mechanism that enables the model to assign meaningful weights to the most important parts of the data sequence. This algorithm not only enhances water utilization efficiency but also boosts plant yield and strengthens pest control mechanisms. This system also provides sustainability through the re-utilization of water and the elimination of electric energy through solar panel systems for powering the inbuilt irrigation system. A comparative analysis of variant algorithms in the agriculture sector with a machine learning approach was also illustrated, and the proposed system yielded 99% yield accuracy, a 97.8% precision value, 98.4% recall, and a 98.4% F1 score value. By encompassing solar irrigation and artificial intelligence-driven analysis, the proposed algorithm, Solar Argo Savior, established a sustainable framework in the latest agricultural sectors and promoted sustainability to protect our environment and community. Full article

The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R² > 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN–GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations. 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

The translocation of cadmium (Cd) in the soil-rice system is complicated; therefore, most of the soil-plant models of Cd have not been extensively studied. Hence, we studied the back-propagation artificial neural network model (BP-ANN) and multiple regression model (MLR) to predict the cadmium (Cd) content in rice grain and soil through testing soil parameters. In this study, 486 pairs of rice grains and corresponding soil samples of 456 vectors were used for training + validation, and 30 vectors were collected from the southwestern karst area of Guangxi Province as a test data set. In this study, the Cd content in rice was successfully predicted by using the factors soil available cadmium (A_Cd), total soil cadmium (T_Cd), soil organic matter (SOM), and pH, which have a more significant impact on rice, as the main prediction variables. Root mean square error (RMSE), Relative Percent Difference (RPD), and correlation coefficient (R²) were used to assess the models. The R², RPD, and RMSE values for R_Cd medium obtained by the MLR model with pH, T_Cd, and A_Cd as entered variables were 0.551, 2.398, and 0.049, respectively. The R² and RMSE values for R_Cd medium obtained by the BP-ANN model with pH, T_Cd, and A_Cd as entered variables were 0.6846, 2.778, and 0.104, respectively. Therefore, it was concluded that BP-ANN was useful in predicting R_Cd and had better performance than MLR. Full article

This study analyzed the heavy metal tolerance and chromium reduction and the potential of plant growth to promote Rhizobium sp. OS-1. By genetic makeup, the Rhizobium strain is nitrogen-fixing and phosphate-solubilizing in metal-contaminated agricultural soil. Among the Rhizobium group, bacterial strain OS-1 showed a significant tolerance to heavy metals, particularly chromium (900 µg/mL), zinc (700 µg/mL), and copper. In the initial investigation, the bacteria strains were morphologically short-rod, Gram-negative, appeared as light pink colonies on media plates, and were biochemically positive for catalase reaction and the ability to ferment glucose, sucrose, and mannitol. Further, bacterial genomic DNA was isolated and amplified with the 16SrRNA gene and sequencing; the obtained 16S rRNA sequence achieved accession no. HE663761.1 from the NCBI GenBank, and it was confirmed that the strain belongs to the Rhizobium genus by phylogenetic analysis. The strain’s performance was best for high hexavalent chromium [Cr(VI)] reduction at 7–8 pH and a temperature of 30 °C, resulting in a total decrease in 96 h. Additionally, the adsorption isotherm Freundlich and Langmuir models fit best for this study, revealing a large biosorption capacity, with Cr(VI) having the highest affinity. Further bacterial chromium reduction was confirmed by an enzymatic test of nitro reductase and chromate reductase activity in bacterial extract. Further, from the metal biosorption study, an Artificial Neural Network (ANN) model was built to assess the metal reduction capability, considering the variables of pH, temperature, incubation duration, and initial metal concentration. The model attained an excellent expected accuracy (R² > 0.90). With these features, this bacterial strain is excellent for bioremediation and use for industrial purposes and agricultural sustainability in metal-contaminated agricultural fields. Full article

Background: Anoectochilus roxburghii (Wall.) Lindl. (A. roxburghii) was widely used in traditional Chinese medicine and also as a health food in China. Gibberellins (GAs) are plant hormones that regulate various aspects of growth and development in A. roxburghii. Ent-kaurene synthase (KS) plays a crucial role in the biosynthesis of GAs in plants. However, there is limited functional analysis of KS in GA biosynthesis and its effect on salt tolerance, especially in A. roxburghii. Methods: The ArKS genes were cloned from A. roxburghii, and its salt tolerance characteristics were verified by prokaryotic expression. Under salt stress, analyze the regulation of KS gene on GA and active ingredient content by qRT-PCR and HPLC-MS/MS, and explore the mechanism of exogenous GAs promoting active ingredient enrichment by regulating the expression level of the KS under salt stress. Results: The ArKS protein was highly homologous to KSs with other plant species; subcellular localization of KS protein was lacking kytic vacuole. The transformants displayed a significant increase in salt tolerance under the stress conditions of 300 mM NaCl. And the expression of ArKS genes and the GAs accumulation was downregulated under the salt stress; among them, the contents of GA3, GA7, GA8, GA24, and GA34 showed a significant decrease. It was further found that there was an increase (1.36 times) in MDA content and a decrease (0.84 times) in relative chlorophyll content under the salt conditions from A. roxburghii. However, the content of active constituents was elevated from A. roxburghii under the NaCl stress, including polysaccharides, total flavonoids, and free amino acids, which increased by 1.14, 1.23, and 1.44 times, respectively. Interestingly, the ArKS gene expression and the chlorophyll content was increased, MDA content showed a decrease from 2.02 μmoL·g⁻¹ to 1.74 μmoL·g⁻¹ after exogenous addition of GAs, and the elevation of active constituents of polysaccharides, total flavonoids, and free amino acids were increased by 1.02, 1.09, and 1.05 times, implying that GAs depletion mitigated the damage caused by adversity to A. roxburghii. Conclusions: The ArKS gene cloned from A. roxburghii improved the salt tolerance of plants under salt stress by regulating GA content. Also, GAs not only alleviate salt tolerance but also play a key role in the synthesis of active components in A. roxburghii. The functions of KS genes and GAs were identified to provide ideas for improving the salt tolerance and quality of ingredients in artificial cultivation from A. roxburghii. Full article

This study aimed to train and validate two artificial neural network (ANN) models, one with four hidden layers and the other with five hidden layers, to predict the daily photovoltaic power output of a 20 Kw photovoltaic power plant with bifacial photovoltaic modules with tilt angle variation from 0° to 90° in two Moroccan cities, Ouarzazate and Oujda. To validate the two proposed models, photovoltaic power data calculated using the System Advisor Model (SAM) software version 2023.12.17 were employed to predict the average daily power of the photovoltaic plant for December, utilizing MATLAB software Version R2020a 9.8, and for the tilt angles corresponding to the latitudes of the two cities studied. The results differ from one model to another according to their mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R²) values. The artificial neural network model with five hidden layers obtained better results with a R² value of 0.99354 for Ouarzazate and 0.99836 for Oujda. These two proposed models are trained using the Levenberg Marquardt (LM) optimizer, which is proven to be the best training procedure. Full article

Objectives: Light intensity is a critical environmental factor regulating plant growth, development, and stress adaptation. However, the physiological and molecular mechanisms underlying light responses in Aconitum kusnezoffii, a valuable alpine medicinal plant, remain poorly understood. This study aimed to elucidate the adaptive strategies of A. kusnezoffii under different light intensities through integrated physiological and transcriptomic analyses. Methods: Two-year-old A. kusnezoffii plants were exposed to three controlled light regimes (790, 620, and 450 lx). Leaf anatomical traits were assessed via histological sectioning and microscopic imaging. Antioxidant enzyme activities (CAT, POD, and SOD), membrane lipid peroxidation (MDA content), osmoregulatory substances, and carbon metabolites were quantified using standard biochemical assays. Transcriptomic profiling was conducted using Illumina RNA-seq, with differentially expressed genes (DEGs) identified through DESeq2 and functionally annotated via GO and KEGG enrichment analyses. Results: Moderate light (620 lx) promoted optimal leaf structure by enhancing palisade tissue development and epidermal thickening, while reducing membrane lipid peroxidation. Antioxidant defense capacity was elevated through higher CAT, POD, and SOD activities, alongside increased accumulation of soluble proteins, sugars, and starch. Transcriptomic analysis revealed DEGs enriched in photosynthesis, monoterpenoid biosynthesis, hormone signaling, and glutathione metabolism pathways. Key positive regulators (PHY and HY5) were upregulated, whereas negative regulators (COP1 and PIFs) were suppressed, collectively facilitating chloroplast development and photomorphogenesis. Trend analysis indicated a “down–up” gene expression pattern, with early suppression of stress-responsive genes followed by activation of photosynthetic and metabolic processes. Conclusions: A. kusnezoffii employs a coordinated, multi-level adaptation strategy under moderate light (620 lx), integrating leaf structural optimization, enhanced antioxidant defense, and dynamic transcriptomic reprogramming to maintain energy balance, redox homeostasis, and photomorphogenic flexibility. These findings provide a theoretical foundation for optimizing artificial cultivation and light management of alpine medicinal plants. Full article

This study aims to analyze the differences in soil bacterial community structure under different vegetation restoration types, and to explore the role of microorganisms in the process of vegetation restoration on the soil ecosystem of the Grain for Green area in the Loess Plateau. High-throughput sequencing technology was used to analyze the alpha diversity of soil bacteria, community structure characteristics, and the correlation between soil environmental factors and bacterial communities in different artificial Hippophae rhamnoides forests. Soil microbial C and N show a decreasing trend with an increase in the 0–100 cm soil layers. The results indicated that the bacterial communities comprised 24 phyla, 55 classes, 110 orders, 206 families, 348 genera, 680 species, and 1989 OTUs. Additionally, the richness indices and diversity indices of the bacterial community in arbor shrub mixed forest are higher than those in shrub pure forest, and the indices of shrub forest on sunny slope are higher than those on shady slope. Across all samples, the dominant groups were Actinobacteria (37.27% on average), followed by Proteobacteria (23.91%), Acidobacteria (12.75%), and Chloroflexi (12.27%). Soil nutrient supply, such as TOC, TN, AN, AP, and AK, had crucial roles in shaping the composition and diversity of the bacterial communities. The findings reveal that vegetation restoration significantly affected soil bacterial community richness and diversity. Furthermore, based on the results, our data provide a starting point for establishing soil bacterial databases in the Loess Plateau, as well as for the plants associated with the vegetation restoration. Full article

The treatment processes from a wastewater treatment plant (WWTP) are known for their complexity and highly nonlinear behavior, which makes them challenging to analyze, model, and especially, to control. This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. This research aims to study the ML and DL systematic applications in optimizing the wastewater treatment processes from an industrial plant, such as the modeling of complex physical–chemical processes, real-time monitoring and prediction of critical wastewater quality indicators, chemical reactants consumption reduction, minimization of plant energy consumption, plant effluent quality prediction, development of data-driven type models as support in the decision-making process, etc. To perform a detailed analysis, 87 articles were included from an initial set of 324, using criteria such as wastewater combined with ML, DL, and artificial intelligence (AI), for articles from 2010 or newer. From the initial set of 324 scientific articles, 300 were identified using Litmaps, obtained from five important scientific databases, all focusing on addressing the specific problem proposed for investigation. Thus, this paper identifies gaps in the current research, discusses ML and DL algorithms in the context of optimizing wastewater treatment processes, and identifies future directions for optimizing these processes through data-driven methods. As opposed to traditional models, IA models (ML, DL, hybrid and ensemble models, digital twin, IoT, etc.) demonstrated significant advantages in wastewater quality indicator prediction and forecasting, in energy consumption forecasting, in temporal pattern recognition, and in optimal interpretability for normative compliance. Integrating advanced ML and DL technologies into the various processes involved in wastewater treatment improves the plant systems’ predictive capabilities and ensures a higher level of compliance with environmental standards. Full article

Light-emitting diodes (LEDs) in agricultural systems mainly contribute their capacity to create a precise and constant light spectral environment. However, the potential of LED in crop production was underestimated. LEDs serve not only as efficient artificial light sources for plant growth, but are also a good tool for enhancing biomass production with limited energy consumption. This article reviewed innovative applications of LED in facility agriculture, e.g., plant factory, and greenhouse. Compared to conventional application of LED, innovative lighting strategies such as intermittent lighting, night break, continuous lighting, alternate lighting, dynamic lighting, and end-of-day (EOD) far-red provided by LED light can elevate the production efficiency effectively. However, the scientific explanation of the above lighting strategies remains to be clearly revealed, providing theoretical support for the further optimization of conducting parameters. This review summarizes the physiological effects of different lighting strategies on crop cultivation and illustrates their future application in facility agriculture, aiming to provide novel methods for elevating the energy utilization efficiency and lowering the cost in facility agriculture using artificial light. Full article

This study aims to select the most suitable submerged plants for the remediation and ecological rehabilitation of nutrient-enriched aquatic environments. The experiment selected Vallisneria natans, Myriophyllum verticillatum, and Elodea nuttallii as research objects. An artificial outdoor pot experiment was conducted with six different levels of ammonia nitrogen: 2, 4, 6, 8, 12, and 16 mg/L. The present study measured the physiological and growth parameters of submerged macrophytes under varying ammonia nitrogen concentrations. The response characteristics of plants to ammonia nitrogen stress were analyzed, and the tolerance thresholds of different submerged macrophyte species to ammonia nitrogen were determined. This enabled us to screen for ammonia nitrogen-tolerant pioneer species suitable for water ecological restoration in eutrophic water bodies. The experiment spanned 28 days. The results showed that the maximum suitable concentration and maximum tolerance concentration of ammonia nitrogen for Vallisneria natans, Myriophyllum verticillatum, and Elodea nuttallii were 2, 4, and 4 mg/L and 4, 12, and 8 mg/L. Submerged plants can grow normally within their maximum ammonia nitrogen tolerance concentration. When the concentration exceeds the maximum tolerance level, the growth of submerged plants is severely stressed by ammonia nitrogen. Low ammonia nitrogen concentrations promote the growth of submerged macrophyte biomass and chlorophyll content as well as the accumulation of dry matter in plants, while high ammonia nitrogen concentrations damage the antioxidant enzyme system and inhibit the growth of submerged plants. The tolerance of the three submerged macrophytes to ammonia nitrogen is as follows: Myriophyllum verticillatum > Elodea nuttallii > Vallisneria natans. Therefore, Myriophyllum verticillatum should be chosen as the ammonia nitrogen-tolerant pioneer species in the ecological restoration of eutrophic water bodies. The research results can provide a theoretical basis for the application of aquatic macrophytes in the treatment of eutrophic water bodies and ecological restoration. Full article

Plant growth monitoring is a complex and challenging task, which depends on a variety of environmental variables, such as temperature, humidity, nutrient availability, and solar radiation. Advances in optical sensors have significantly enhanced data collection on plant growth. These developments enable the optimization of agricultural practices and crop management through the integration of artificial vision techniques. Despite advances in the application of these technologies, limitations and challenges persist. This review aims to analyze the current state-of-the-art methodologies for using artificial vision and optical sensors in plant growth assessment. The systematic review was conducted following the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Relevant studies were analyzed from the Scopus and Web of Science databases. The main findings indicate that data collection in agricultural environments is challenging. This is due to the variability of climatic conditions, the heterogeneity of crops, and the difficulty in obtaining accurately and homogeneously labeled datasets. Additionally, the integration of artificial vision models and advanced sensors would enable the assessment of plant responses to these environmental factors. The advantages and limitations were examined, as well as proposed research areas to further contribute to the improvement and expansion of these emerging technologies for plant growth assessment. Finally, a relevant research line focuses on evaluating AI-based models on low-power embedded platforms to develop accessible and efficient decision-making solutions in both agricultural and urban environments. This systematic review was registered in the Open Science Framework (OSF). Full article

Accurate crop row detection is crucial for determining weed pressure (weeds item per square meter). However, this task is complicated by the similarity between crops and weeds, the presence of missing plants within rows, and the varying growth stages of both. Our hypothesis was that in drone imagery captured at altitudes of 20–30 m—where individual plant details are not discernible—weed presence among crops can be statistically detected, allowing for the generation of a weed distribution map. This study proposes a computer vision detection method using images captured by unmanned aerial vehicles (UAVs) consisting of six main phases. The method was tested on 208 images. The algorithm performs well under normal conditions; however, when the weed density is too high, it fails to detect the row direction properly and begins processing misleading data. To investigate these cases, 120 artificial datasets were created with varying parameters, and the scenarios were analyzed. It was found that a rate variable—in-row concentration ratio (IRCR)—can be used to determine whether the result is valid (usable) or invalid (to be discarded). The F1 score is a metric combining precision and recall using a harmonic mean, where “1” indicates that precision and recall are equally weighted, i.e., β = 1 in the general Fβ formula. In the case of moderate weed infestation, where 678 crop plants and 600 weeds were present, the algorithm achieved an F1 score of 86.32% in plant classification, even with a 4% row disturbance level. Furthermore, IRCR also indicates the level of weed pressure in the area. The correlation between the ground truth weed-to-crop ratio and the weed/crop classification rate produced by the algorithm is 98–99%. As a result, the algorithm is capable of filtering out heavily infested areas that require full weed control and capable of generating weed density maps on other cases to support precision weed management. Full article