Search Results (1,342)

A factory energy management system, based on information and communication technology, facilitates efficient energy management using the real-time monitoring, analyzing, and controlling of the energy consumption of a factory. However, traditional food processing plants use basic control systems that cannot analyze energy consumption for each phase of processing. This makes it difficult to identify usage patterns for individual operations. This study identifies steam energy consumption patterns across four stages of food processing. Additionally, it proposes a customized predictive model employing four machine learning algorithms—linear regression, decision tree, random forest, and k-nearest neighbor—as well as two deep learning algorithms: long short-term memory and multi-layer perceptron. The enhanced multi-layer perceptron model achieved a high performance, with a coefficient of determination (R²) of 0.9418, a coefficient of variation of root mean square error (CVRMSE) of 9.49%, and a relative accuracy of 93.28%. The results of this study demonstrate that straightforward data and models can accurately predict steam energy consumption for individual processes. These findings suggest that a customized predictive model, tailored to the energy consumption characteristics of each process, can offer precise energy operation guidance for food manufacturers, thereby improving energy efficiency and reducing consumption. Full article

Food quality detection by machine learning (ML) is more practical and sustainable as it does not require sample preparation and reagents. However, the prediction of pineapple quality by hyperspectral data applied to ML is not known. The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. Three inputs were used for each model: leaf reflectance, peel reflectance, and fruit reflectance. The machine learning model SVM, stood out for its best results, demonstrating good generalization capacity and effectiveness in predicting these attributes, reaching accuracy values above 0.7 for Brix and ratio, using fruit reflectance. In terms of the overall efficiency of the input variables, peel and fruit were the most informative, with peel standing out for the estimation of secondary metabolism compounds, while the fruit showed excellent performance in predicting flavor-related attributes, such as acidity, °Brix and ratio, as mentioned previously, above 0.7. These results highlight the potential of using spectral data and machine learning in the non-destructive assessment of pineapple quality, enabling advances in monitoring and selecting fruits with better sensors. Full article

Forest ecosystems represent a natural repository of biodiversity, bioenergy, food, timber, water, medicine, wildlife shelter, and pollution control. In many countries, forests offer great potential to provide biogenic resources that could be utilized for large-scale biotechnological synthesis and products. The evolving nanotechnology could be an excellent platform for the transformation of forest products into value-added nanoparticles (NPs). It also serves as a tool for commercial production, placing the forest at the heart of conservation and sustainable management strategies. NPs are groups of atoms with a size ranging from 1 to 100 nm. This review analyzes the scholarly articles published over the last 25 years on the forest and woody plant-based green synthesis of NPs, highlighting the plant parts and applications discussed. The biosynthesis of nanomaterials from plant extracts provides inexpensiveness, biocompatibility, biodegradability, and environmental nontoxicity to the resultant NPs. The leaf is the most critical organ in woody plants, and it is widely used in NP biosynthesis, perhaps due to its central functions of bioactive metabolite production and storage. Most biosynthesized NPs from tree species have been used and tested for medical applications. For sustainable advancements in forest-based nanotechnology, broader species coverage, expanded applications, and interdisciplinary collaboration are essential. Full article

Background: Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with growing evidence linking risk to lifestyle and dietary factors. However, nutrition-related exposures have rarely been integrated into existing CRC risk prediction models. This study aimed to develop and validate a lifestyle-based 10-year CRC risk prediction model using longitudinal data from a large-scale population-based screening cohort to facilitate early risk stratification and personalized screening strategies. Methods: Data were obtained from 21,358 individuals participating in a CRC screening program in Shanghai, China, with over 10 years of active follow-up until 30 June 2021. Of these participants, 16,782 aged ≥40 years were used for model development, and 4576 for external validation. Predictors were selected using random survival forest (RSF) and elastic net methods, and the final model was developed using Cox regression. Machine learning approaches (RSF and XGBoost) were additionally applied for performance comparison. Model performance was evaluated through discrimination, calibration, and decision curve analysis (DCA). Results: The final model incorporated twelve predictors: age, gender, family history of CRC, diabetes, fecal immunochemical test (FIT) results, and seven lifestyle-related factors (smoking, alcohol use, body shape, red meat intake, fried food intake, pickled food intake, and fruit and vegetable intake). Compared to the baseline demographic-only model (C-index = 0.622; 95% CI: 0.589–0.657), the addition of FIT improved discrimination, and further inclusion of dietary and lifestyle variables significantly enhanced the model’s predictive accuracy (C-index = 0.718; 95% CI: 0.682–0.762; ΔC-index = 0.096, p = 0.003). Conclusions: Incorporating dietary and lifestyle variables improved CRC risk stratification. These findings highlight the value of dietary factors in informing personalized screening decisions and providing an evidence-based foundation for targeted preventive interventions. Full article

The interconnection between biodiversity loss, food system and climate change is a complex issue with profound implications for global sustainability. Small ruminant farming is a crucial part of addressing these challenges as it contributes to environmental, social, and economic resilience. In Italy, sheep and goat farming is most common in marginal areas with a prevalence of pastoral systems and low mechanization levels. In the Apulia region of Southern Italy, autochthonous small ruminant breeds are at high risk of extinction, due to changing agricultural practices and market pressures. Autochthonous breeds represent valuable genetic resources, adapted to the local environment and capable of producing high-quality products. Apulia boasts an ancient dairy tradition, producing a variety of cheeses from small ruminants, such as Canestrato Pugliese, a Protected Designation of Origin, and four cheeses (Cacioricotta, Pecorino Foggiano, Scamorza di Pecora, and Caprino) recognized as Traditional Agri-Food Products by the Italian Ministry of Agriculture, Food Sovereignty and Forests. These products represent an essential element for biodiversity conservation, encompassing ecosystems, autochthonous breeds, microbial diversity, traditional farming practices, and production systems. This review surveys the main small ruminant native breeds of Apulia region, highlighting their historical significance, distinctive traits, and traditional productions, to help shape strategies for animal biodiversity conservation. Full article

Soybean (Glycine max) seeds are rich in amino acids, offering key nutritional and physiological benefits. In this study, 290 soybean accessions from the USDA Germplasm Collection based in Urbana, IL Information Network (GRIN) were analyzed. Four Genome-Wide Association Study (GWAS) models—Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), Mixed Linear Model (MLM), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Multi-Locus Mixed Model (MLMM)—identified two significant Single Nucleotide Polymorphisms (SNPs) associated with arginine content: Gm06_19014194_ss715593808 (LOD = 9.91, 3.91% variation) at 19,014,194 bp on chromosome 6 and Gm11_2054710_ss715609614 (LOD = 9.05, 19% variation) at 2,054,710 bp on chromosome 11. Two candidate genes, Glyma.06g203200 and Glyma.11G028600, were found in the two SNP marker regions, respectively. Genomic Prediction (GP) was performed for arginine content using several models: Bayes A (BA), Bayes B (BB), Bayesian LASSO (BL), Bayesian Ridge Regression (BRR), Ridge Regression Best Linear Unbiased Prediction (rrBLUP), Random Forest (RF), and Support Vector Machine (SVM). A high GP accuracy was observed in both across- and cross-populations, supporting Genomic Selection (GS) for breeding high-arginine soybean cultivars. This study holds significant commercial potential by providing valuable genetic resources and molecular tools for improving the nutritional quality and market value of soybean cultivars. Through the identification of SNP markers associated with high arginine content and the demonstration of high prediction accuracy using genomic selection, this research supports the development of soybean accessions with enhanced protein profiles. These advancements can better meet the demands of health-conscious consumers and serve high-value food and feed markets. Full article

Dipeptidyl peptidase-4 (DPP-4) inhibitors play a critical role in the management of type 2 diabetes; however, some synthetic drugs may cause adverse effects. Natural peptides derived from rice offer a promising alternative due to their favorable biocompatibility and development potential. In this study, an AI-assisted virtual screening pipeline integrating machine learning, molecular docking, and molecular dynamics (MD) simulations was established to identify and evaluate rice-derived DPP-4 inhibitory peptides. A random forest classification model achieved 85.37% accuracy in predicting inhibitory activity. Peptides generated by simulated enzymatic hydrolysis were screened based on machine learning and docking scores, and four proline-rich peptides (PPPPPPPPA, PPPSPPPV, PPPPPY, and CPPPPAAY) were selected for MD analysis. The simulation results showed that PPPSPPPV formed a stable complex with the DPP-4 catalytic triad (Ser592–Asp670–His702) through electrostatic and hydrophobic interactions, with low structural fluctuation (RMSF < 1.75 Å). In vitro assays revealed that PPPPPY exhibited the strongest DPP-4 inhibitory activity (IC₅₀ = 153.2 ± 5.7 μM), followed by PPPPPPPPA (177.0 ± 6.0 μM) and PPPSPPPV (216.3 ± 4.5 μM). This study presents an efficient approach combining virtual screening and experimental validation, offering a structural and mechanistic foundation for the development of natural DPP-4 inhibitory peptides as candidates for functional foods or adjunct diabetes therapies. Full article

Rice is an important crop that significantly contributes to food security. Lodging is considered an important factor limiting rice yield and quality. The objective of this study was to investigate the effects of carbon and nitrogen on lodging in fragrant rice. A 2-year field experiment (2021 to 2022) was conducted with the fragrant rice cultivars Meixiangzhan 2 and Xiangyaxiangzhan grown under nine carbon and nitrogen co-application treatments (CK: 0 mg/L glucose + 0 mg/L urea; T1: 0 mg/L glucose + 50 mg/L urea; T2: 0 mg/L glucose + 100 mg/L urea; T3: 150 mg/L glucose + 0 mg/L urea; T4: 150 mg/L glucose + 50 mg/L urea; T5: 150 mg/L glucose + 100 mg/L urea; T6: 300 mg/L glucose + 0 mg/L urea; T7: 300 mg/L glucose + 50 mg/L urea; and T8: 300 mg/L glucose + 100 mg/L urea). The lodging index and stem characteristics of fragrant rice were investigated. Compared with the CK treatment, the T5 and T7 treatments significantly increased the pushing resistance force by 22.22–127.78% and 50.00–77.50%, respectively. Compared with the other fertilization treatments, the T5 treatment kept the lodging index at a low level and reduced the plant height. The stem characteristics were regulated under the carbon and nitrogen co-application treatments, and the internode length and dry weight significantly influenced the plant height and the pushing resistance force and then regulated the lodging index. Structural equation modeling and random forest modeling analyses suggest that carbon and nitrogen co-application treatments may further improve the resistance of rice to lodging by increasing the dry weight of the third and fourth internodes. Overall, optimized carbon and nitrogen co-application could regulate stem internode morphology and improved lodging resistance. Furthermore, the T5 treatment (150 mg/L glucose + 100 mg/L urea) improved lodging resistance. This study provides guidelines for enhancing lodging resistance by regulating internode characteristics via the co-application of carbon and nitrogen at the booting stage in fragrant rice. Full article

This study evaluates and estimates the agronomic effectiveness of food waste-derived fertilizers by analyzing plant yield and the internal efficiency of nitrogen utilization (IENU) via statistical and machine learning models. A dataset of 448 cases from various food waste treatments gathered from our experiments and the existing literature was analyzed. Plant yield and IENU exhibited substantial variability, averaging 2268 ± 3099 kg/ha and 32.3 ± 92.5 kg N/ha, respectively. Ryegrass dominated (73.77%), followed by unspecified grass (10.76%), oats (4.87%), and lettuce (2.02%). Correlation analysis revealed that decomposition duration positively influenced plant yield and IENU (r = 0.42 and 0.44), while temperature and volatile solids had negative correlations. Machine learning models outperformed linear regression in predicting plant yield and IENU, especially after preprocessing to remove missing values and outliers. Random Forest and Cubist models showed strong generalization with high R² (0.79–0.83) for plant yield, while Cubist predicted IENU well in testing, with RMSE = 3.83 and R² = 0.78. These findings highlight machine learning’s ability to analyze complex datasets, improve agricultural decision-making, and optimize food waste utilization. Full article

Nucleopolyhedroviruses of lepidopteran larvae (Alphabaculovirus, Baculoviridae) form the basis for effective and highly selective biological insecticides for the control of caterpillar pests of greenhouse and field crops and forests. Horizontal transmission is usually achieved following the release of large quantities of viral occlusion bodies (OBs) from virus-killed insects. In the present review, I examine the evidence for productive midgut infection in different host species and the resulting transmission through the release of OBs in the feces (frass) of the host. This has been a neglected aspect of virus transmission since it was initially studied over six decades ago. The different host–virus pathosystems vary markedly in the quantity of OBs released in feces and in their ability to contaminate the host’s food plant. The release of fecal OBs tends to increase over time as the infection progresses. Although based on a small number of studies, the prevalence of transmission of fecal inoculum is comparable with that of recognized alternative routes for transmission and dissemination, such as cannibalism and interactions with predators and parasitoids. Finally, I outline a series of predictions that would affect the importance of OBs in feces as a source of inoculum in the environment and which could form the basis for future lines of research. Full article

Reducing the use of chemical inputs (fertilizers, pesticides) in agriculture while maintaining crop productivity is the main challenge facing sub-Saharan African family farming systems. The use of effective microorganisms (EM) is among the various innovative approaches for minimizing chemical inputs and the environmental impact of agricultural production and protecting soil health while enhancing crop yields and improving food security. This study sought to characterize the microbial biodiversity of local beneficial microorganisms (BMs) products from locally fermented forest litter and investigate their ability to enhance tomato plant growth and development. Beneficial microorganisms (BMs) were obtained by anaerobic fermentation of forest litter collected in four agroecological regions of Senegal mixed with sugarcane molasses and various types of carbon sources (groundnut shells, millet stovers, and rice bran in different proportions). The microbial community composition was analyzed using next-generation rDNA sequencing, and their effects on tomato growth traits were tested in greenhouse experiments. Results show that regardless of the litter geographical collection site, the dominant bacterial taxa in the BMs belonged to the phyla Firmicutes (27.75–97.06%) and Proteobacteria (2.93–72.24%). Within these groups, the most prevalent classes were Bacilli (14.41–89.82%), α-proteobacteria (2.83–72.09%), and Clostridia (0.024–13.34%). Key genera included Lactobacillus (13–65.83%), Acetobacter (8.91–72.09%), Sporolactobacillus (1.40–43.35%), and Clostridium (0.08–13.34%). Fungal taxa were dominated by the classes Leotiomycetes and Sordariomycetes, with a prevalence of the acidophilic genus Acidea. Although microbial diversity is relatively uniform across samples, the relative abundance of microbial taxa is influenced by the litter’s origin. This is illustrated by the PCoA analysis, which clusters microbial communities based on their litter source. Greenhouse experiments revealed that five BMs (DK-M, DK-G, DK-GM, NB-R, and NB-M) significantly (p < 0.05) enhanced tomato growth traits, including plant height (+10.75% for DK-G and +9.44% for NB-R), root length (+56.84–62.20%), root volume (+84.32–97.35%), root surface area (+53.16–56.72%), and both fresh and dry shoot biomass when compared to untreated controls. This study revealed that forest-fermented litter products (BMs), produced using litter collected from various regions in Senegal, contain beneficial microorganisms known as plant growth-promoting microorganisms (PGPMs), which enhanced tomato growth. These findings highlight the potential of locally produced BMs as an agroecological alternative to inorganic inputs, particularly within Senegal’s family farming systems. Full article

Soil pollution is a global threat that seriously affects biodiversity in (agro)ecosystems and may compromise water and food quality. Therefore, the ability of tree species (Populus tremula, Salix caprea, and Betula pendula) to accumulate and phytoextract specific toxic heavy metals from soil was investigated. The study was conducted in and near relict mining areas of Freiberg (Germany) and sampling sites selected according to their spatial location relative to potential sources of metal(loid)s. The concentrations of geogenic (P, Fe, Mn, Ca) and pollutant (Pb, Cd, Zn, As) elements in soil and the present trees were measured using ICP-MS. The highest total soil concentrations of As (8978 µg g⁻¹) were found within the Davidschaft mining area, and for soil Pb, both in the Davidschaft vicinity (328 µg g⁻¹) and mining area (302 µg g⁻¹). Unexpectedly, the highest soil Zn (0.64 mg g⁻¹) and Cd (3.5 mg g⁻¹) concentrations were found in Freiberg city Forest. The lowest soil concentrations of pollutants (As, Cd, Pb, and Zn) were recorded for Seifersdorf. Total soil P was highest in Colmnitz, but Ca, Mn, and Fe concentrations were very similar across all sites. The available concentration of all measured toxic elements in the soil generally decreased in the order Davidschaft > Davidschaft vicinity, Colmnitz > Seifersdorf = Freiberg city forest. All studied tree species had higher concentrations of the essential elements in leaves than in branches. Generally, higher values of bioaccumulation coefficients (especially for Cd) were found for Salix caprea compared with Populus tremula and Betula pendula. Full article

The integration of artificial intelligence (AI) technologies is reshaping diverse domains of human activity, including natural resource management, urban and rural planning, agri-food systems, industry, energy, education, and healthcare. However, the impact of AI on the sustainability of local ecosystems remains insufficiently systematized. This highlights the need for a comprehensive review that considers spatial, sectoral, and socio-economic characteristics of regions, as well as interdisciplinary approaches to sustainable development. This study presents a scoping review of 198 peer-reviewed publications published between 2010 and March 2025, focusing on applied cases of AI deployment in local contexts. Special attention is given to the role of AI in monitoring water, forest, and agricultural ecosystems, facilitating the digital transformation of businesses and territories, assessing ecosystem services, managing energy systems, and supporting educational and social sustainability. The review includes case studies from Africa, Asia, Europe, and Latin America, covering a wide range of technologies—from machine learning and digital twins to IoT and large language models. Findings indicate that AI holds significant potential for enhancing the efficiency and adaptability of local systems. Nevertheless, its implementation is accompanied by notable risks, including socio-economic disparities, technological inequality, and institutional limitations. The review concludes by outlining research priorities for the sustainable integration of AI into local ecosystems, emphasizing the importance of cross-sectoral collaboration and scientific support for regional digital transformations. Full article

Background/Objectives: In South China, Lithocarpus species dominate mixed evergreen broadleaf forests, forming symbiotic relationships with ectomycorrhizal fungi and serving as food resources for diverse fauna, including frugivorous birds and mammals. The limited understanding of chloroplast genomes in this genus restricts our insights into its species diversity. This study investigates the chloroplast genome (cp genome) sequences from seven Lithocarpus species, aims to elucidate their structural variation, evolutionary relationships, and functional gene content to provide effective support for future genetic conservation and breeding efforts. Methods: We isolated total DNA from fresh leaves and sequenced the complete cp genomes of these samples. To develop a genomic resource and clarify the evolutionary relationships within Lithocarpus species, comparative chloroplast genome studies and phylogenetic investigations were performed. Results: All studied species exhibited a conserved quadripartite chloroplast genome structure, with sizes ranging from 161,495 to 163,880 bp. Genome annotation revealed 130 functional genes and a GC content of 36.72–37.76%. Codon usage analysis showed a predominance of leucine-encoding codons. Our analysis identified 322 simple sequence repeats (SSRs), which were predominantly palindromic in structure (82.3%). All eight species exhibited the same 19 SSR categories in similar proportions. Eight highly variable regions (ndhF, ycf1, trnS-trnG-exon1, trnk(exon1)-rps16(exon2), rps16(exon2), rbcL-accD, and ccsA-ndh) have been identified, which could be valuable as molecular markers in future studies on the population genetics and phylogeography of this genus. The phylogeny tree provided critical insights into the evolutionary trajectory of Fagaceae, suggesting that Lithocarpus was strongly supported as monophyletic, while Quercus was inferred to be polyphyletic, showing a significant cytonuclear discrepancy. Conclusions: We characterized and compared the chloroplast genome features across eight Lithocarpus species, followed by comprehensive phylogenetic analyses. These findings provide critical insights for resolving taxonomic uncertainties and advancing systematic research in this genus. Full article

Pest control in industrial buildings, such as silos and storage facilities, is critical for maintaining food safety and economic stability. Traditional methods like fumigation face challenges, including insect resistance and environmental concerns, prompting the need for alternative approaches. Heat treatments have emerged as an effective and eco-friendly solution, but optimizing their duration and efficiency remains a challenge. This study leverages machine learning (ML) to predict the duration of heat treatments required for effective pest control in various industrial buildings. Using a dataset of 1423 heat treatment time series collected from IoT devices, we applied exploratory data analysis (EDA) and ML models, including random forest, XGBoost, ridge regression, and support vector regression (SVR), to predict the time needed to reach 50 °C, a critical threshold for pest mortality. Results revealed significant variations in treatment effectiveness based on building type, geographical location, and ambient temperature. XGBoost and random forest models outperformed others, achieving high predictive accuracy. The findings highlight the importance of tailored heat treatment protocols and the potential of data-driven approaches to optimize pest control strategies, reduce energy consumption, and improve operational efficiency in industrial settings. This study underscores the value of integrating IoT and ML for real-time monitoring and adaptive control in pest management. Full article