Search Results (210)

This study explores the use of endophytic fungi for the biocontrol of harmful aflatoxins (AFTs) in maize (Zea mays L.). The main objective of this study was to evaluate the effects of fungal pathogens and biocontrol agents on the corn seed germination and growth of seedlings under controlled conditions. Experiments were conducted under laboratory conditions in a growth chamber and in a greenhouse to assess the influence of environmental factors on seed performance and treatment efficacy. The growth chamber provided uniform conditions for physiological assessment while the greenhouses represented more realistic field conditions. Corn kernels were sown in sterile pots inside the growth chamber at standard conditions or in the greenhouse at controlled conditions and four treatment groups were established: untreated control seeds, seeds treated with non-AFT-producing (non-aflatoxigenic) strains (Trichoderma harzianum, T. asperellum and Aspergillus niger), seeds inoculated with AFT-producing (aflatoxigenic) strains (A. flavus and A. parasiticus), and seeds co-inoculated with both aflatoxigenic and non-aflatoxigenic strains (A. flavus and A. parasiticus with T. harzianum, T. asperellum or A. niger). High-performance liquid chromatography was utilized to detect and analyze the presence of AFTs. Co-culturing of A. flavus with T. harzianum resulted in a significant decrease in AFT levels, achieving a relative reduction of 99.3% compared to aflatoxigenic treatments alone. Among the isolates tested, T. harzianum and T. asperellum were the most effective at lowering AFT production of the aflatoxigenic strains, reducing the 5120 ± 560 µg/kg AFT level produced by A. flavus alone to 50.1 ± 1.10 and 63.1 ± 3.1 µg/kg, respectively. A. flavus negatively affected germination and early growth, whereas T. harzianum significantly enhanced both parameters. This study demonstrates that non-aflatoxigenic Trichoderma isolates can effectively mitigate AFT contamination and improve seedling growth, highlighting their potential as effective. sustainable, and locally adopted biocontrol agents for Pakistan’s chronic AFT problem under diverse environmental conditions—an area with minimal prior research and high national relevance. Full article

In the development of intelligent processing for fresh corn cobs, automated inspection of ear appearance quality to promptly sort out cobs with surface defects and ensure overall product compliance is currently a hot topic in agricultural product processing research. However, fresh corn cob surfaces are covered with numerous independent, densely packed kernels, and defects affecting one or more kernels create surface anomalies of highly variable sizes. This leads to defect targets with multi-scale features and scattered distributions, making it challenging for existing deep learning-based visual inspection methods to simultaneously optimize small-target modeling capacity and computational efficiency. Consequently, these methods cannot effectively balance the accuracy of small-scale defect detection with computational efficiency, making it difficult to meet practical requirements. To address these issues, this paper proposes the SSAD-YOLOv8s-Prune defect detection method for small-scale defect detection in white fresh corn cobs. First, the backbone layer of the original model is replaced with a custom-designed SSA structure, which not only expands the feature dimensions for small-scale defects and enriches feature representation but also reduces the number of computational parameters to achieve model compression. Second, the original neck layer is replaced with a custom-designed RepDyFPN structure to enable feature fusion and interaction across different scales and depths. Finally, the LAMP algorithm is employed to prune and compress the newly improved model, further achieving model compression performance. Compared with the baseline YOLOv8s, our method reduces model parameters by 9.33 M, floating-point operations (FLOPs) by 12.5 G, and model size by 17.6 MB, while simultaneously improving mAP₅₀ by 1.2 percentage points to 96.1% and mAP_50–95 by 4.1 percentage points to 62.8%. Furthermore, our method maintains advantages over other mainstream detection models. Therefore, the proposed SSAD-YOLOv8-Prune detection model successfully balances detection accuracy with model compression, providing a feasible detection method for small-scale defect detection in fresh corn cobs. Full article

Popping expansion is the main quality trait determining the commercial value of popcorn maize, yet its evaluation requires destructive grain sampling. We investigated whether multitemporal UAV multispectral and textural features could predict grain yield and popping expansion in a native population of Peruvian popcorn and its five purple-kernel corn hybrids grown in 16 drainage lysimeters (80 subplots) under controlled irrigation in Lima, Peru. Eight UAV flights were conducted between 50 and 117 days after sowing, and 8 vegetation indices plus 5 GLCM texture metrics were extracted from canopy-masked imagery. Six regression algorithms were trained using Sequential Forward Selection (SFS; applied to five of six algorithms) and validated by Leave-One-Lysimeter-Out cross-validation (LOGO). Early grain, grain filling, and maturity were the most informative stages for yield prediction. The best model, obtained at maturity, was SVR-rbf using SCCCI and Homogeneity, reaching R² = 0.66 and RMSE = 1.23 t ha⁻¹. SCCCI was the most consistently selected predictor across models. By contrast, popping expansion was poorly predicted (R² = 0.17), indicating that this post-harvest quality trait is only weakly linked to canopy-level spectral information. Multitemporal UAV phenotyping therefore shows promise for non-destructive yield screening, but not for replacing direct popping expansion measurements. Full article

Simultaneously improving the yield and, in particular, the nutritional quality of sweet corn (Zea mays L. saccharata), one of the most important cereal fresh foods worldwide, remains a major challenge. Here, we demonstrated that compared to control groups, hydrogen-enriched water (HEW) irrigation significantly improved agronomic performance, increasing kernel number (~10.55%) and ear length (~5.73%) while notably reducing barren tip length by about 60.73%. Regarding nutritional quality, HEW-treated kernels exhibited remarkable increases in soluble protein (~61.53%), total soluble sugars (~31.10%), vitamin C (~28.31%), total phenolics (~21.06%), and flavonoids (~40.56%). Micronutrients were also enhanced, such as zinc (~96.82%), iron (~51.70%), and manganese levels (~40.37%). HEW effectively modulated the expression of sugar metabolism-related genes. Specifically, the coordinated upregulation of key genes, such as ZmSUS1 (~3.8 fold), ZmINCW2 (~1.9 fold), and ZmHXK1 (~1.6 fold), might contribute to the enhanced accumulation of sucrose (~11.79%), glucose (~6.21%), and fructose (~26.50%). Starch biosynthesis was also promoted. The improved sugar–acid ratio indicated enhanced taste quality. Importantly, representative key antioxidant genes (ZmSOD2/4, ZmPOD1/2, and ZmCAT1/3) as well as corresponding enzymatic activities in kernels were stimulated, which was negatively associated with lipid peroxidation. Overall, these results indicate that HEW irrigation is a promising, eco-friendly strategy that can be efficiently used to improve sweet corn yield and nutritional value. Full article

Indonesia’s E10 blending mandate presents a strategic opportunity for decarbonization and inclusive rural development, contingent on a robust supply chain integrating smallholder farmers. This study developed a novel supply chain framework for corn products in Sumenep to facilitate sustainable ethanol production. Methods involved comprehensive data collection, mathematical modeling using the p-median method, and farmer clustering techniques. Findings reveal that Sumenep Regency’s substantial corn harvest of 8,475,914.5 tons, yielding 1,271,387.175 tons of kernels, can produce 381,416.1525 L of bioethanol. By applying a clustering supply chain model, the farmers’ group profit is IDR 205,693,725,826, while it is IDR 177,394,823,353 for the non-clustering model, meaning that the clustering supply chain model increases profit by 16% compared to the model without clustering. This localized production, enabled by a simplified, decentralized supply chain architecture, significantly enhances national energy security, reduces greenhouse gas emissions, and improves the economic stability of smallholder farmers through equitable value capture and minimized logistical costs. The framework offers a practical, implementable strategy for Indonesia’s energy transition, fostering environmental sustainability and inclusive socio-economic development. Full article

The number of kernels per ear is a key yield parameter that reflects the effects of breeding and agronomic management practices on crop productivity. However, conventional manual counting is labor-intensive, time-consuming, and prone to human error. This study evaluated the performance of six YOLO models, trained from scratch and fine-tuned, alongside a Faster R-CNN model, for automated kernel detection and counting from manually harvested field corn ear images. Model performance was assessed for predicting the yield and harvest index (HI) of field corn under varying nitrogen and irrigation rates. Results show that models trained with fine-tuning consistently outperform those trained from scratch in both accuracy and computational speed. Among all tested YOLO models, YOLOv11x achieved the highest performance, with a precision of 0.978, a recall of 0.968, a latency of 4.8 ms, and a prediction coefficient of determination (R²_pred) of 0.858 for the test set and 0.890 for cross-year datasets. The YOLOv8x model ranked second, whereas YOLOv10x was the worst-performing model. Compared to YOLO, Faster R-CNN performed poorly. Yield and HI predictions using YOLOv11x achieved R² values of 0.881 and 0.758, respectively, and captured treatment effects. Overall, the findings demonstrate that YOLO-based architecture is highly effective for detecting kernels and predicting yield in precision agriculture applications. Full article

A simple, rapid, and cost-effective method for the determination of benzo[a]pyrene (BaP) in edible oil was developed and validated. Nickel oxide-deposited silica (SiO₂@NiO) was employed as a solid-phase extraction (SPE) adsorbent for the extraction of BaP from edible oil, followed by high-performance liquid chromatography–diode array detector (HPLC-DAD) analysis of BaP. The edible oil was diluted with n-hexane and directly loaded to SiO₂@NiO for SPE. The n-hexane was also used to clean the fat-soluble interference substance in the edible oil, and BaP was selectively captured using SiO₂@NiO through the electron donor–acceptor interaction. The SPE conditions, including the amount of adsorbent, volume of washing solvent, and type and volume of desorption solvent, were optimized. This SiO₂@NiO-based SPE coupled with the HPLC-DAD method demonstrated good linearity within a BaP concentration range of 6–1875 ng/g in edible oils, with a limit of detection of 1.3 ng/g, spiked recovery of 97.4–105.1%, and relative standard deviation (RSD) of <3.0%. The method was applied to the analysis of BaP in 11 real oil samples (soybean oil, olive oil, corn germ oil, flaxseed oil, walnut oil, sunflower kernel oil, peanut oil, unrefined oil, and high-temperature frying oil), and the results show that the unrefined oil and high-temperature frying oil were at risk of BaP exceeding acceptable level. Full article

This study aimed to determine the ileal digestibility of amino acids (AA) in various protein sources for 21-day-old broilers. A total of 448 Ross 308 male broilers were allocated to eight dietary treatments with eight replicates in a randomized complete block design. Experimental diets included one nitrogen-free diet and seven test diets, each containing one of the following feed ingredients—dehulled soybean meal (SBM), fermented SBM (FSBM), rapeseed meal (RM), copra meal (CM), palm kernel meal (PKM), corn distillers dried grains with solubles (DDGS), and fish meal (FM), as the sole source of AA. On day 21, all birds were euthanized and subsequently ileal digesta was collected from the distal two-thirds of the ileum, extending from Meckel’s diverticulum to 1 cm proximal to the ileocecal junction. The ileal digestibility of AA in the FM was the greatest, followed by the SBM. The ileal digestibility for AA in the SBM was greater than that in the RM. The ileal AA digestibility in the RM was greater than or not different from that in the FSBM, except for Val and Pro, and superior to the CM and the PKM. The ileal digestibility of AA in the FSBM was greater than or not different from those in corn DDGS, except for Met and Cys. Corn DDGS exhibited greater or not different ileal digestibility of AA compared to that of the CM and the PKM, except for Val and Asp, and the PKM was the lowest. In conclusion, the ileal digestibility of AA was the greatest in the FM, followed by the SBM, FSBM, the RM, corn DDGS, the CM, and the PKM. Furthermore, the results underscore the necessity for continuous evaluation of ileal AA digestibility in various protein sources. Full article

Background/Objectives: Sweetness is a key determinant of the eating quality of sweet corn, primarily governed by the soluble sugar content in kernels. The soluble sugar content decreases rapidly during the postharvest shelf life, which directly affects the flavor and quality. Relatively few studies have been conducted on the shelf life of sweet corn. Methods: An F₆ recombinant inbred line (RIL) population was constructed from two super sweet inbred lines with contrasting soluble sugar degradation rates: D174 (low degradation rate) and D179 (high degradation rate). Extreme phenotype pools were established using soluble sugar content as the target trait. Based on bulked segregant analysis sequencing, we identified chromosomal segments associated with postharvest soluble sugar reduction in sweet corn, annotated the gene information within these segments, and analyzed the functions of the annotated genes using the Gene Ontology and Genomes databases. Results: Results revealed three associated regions located at 44,205,775–45,290,843 bp on chromosome 4, 6,250,656–6,744,665 bp on chromosome 2, and 135,428,709–136,732,132 bp on chromosome 10. This interval contained 195 genes. Integrated analysis of gene expression, gene annotations, and quantitative real-time PCR indicated that Zm00001eb069070, which is highly expressed in kernels with a prolonged shelf life, might be a key candidate gene regulating soluble sugar degradation in sweet corn. Conclusions: This study provides valuable genetic resources for the improvement of favorable agronomic traits and the advancement of molecular breeding strategies for sweet corn. Full article

The complex polysaccharide pullulan is characterized as a glucose-containing biopolymer that is both water-soluble and neutral in polarity. A variety of commercial applications exist for pullulan, including its utilization as a flocculant, a blood plasma substitute, a food additive, a dielectric material, an adhesive, or a packaging film. The fungus Aureobasidium pullulans has used several hydrolysates derived from plant biomass or starch-containing processing coproducts to support polysaccharide production. These include various plant biomass or processing coproduct streams such as lignocellulosic-containing peat, prairie grass, stalks, hulls, straw, shells, and pods or starch-containing coproducts from the processing of corn, rice, jackfruit seeds, palm kernels, cassava, and potatoes. The pullulan concentration produced by A. pullulans and the pullulan content of the polysaccharide depend on the plant hydrolysate carbon content and the strain used. If a lower-cost culture medium for fungal pullulan production were to be developed, a more economical approach to synthesizing commercial pullulan would be the utilization of plant-derived hydrolysates. This review examines the ability of selected hydrolysates of lignocellulosic plant biomass or plant-derived starch-containing processing coproducts to support A. pullulans polysaccharide synthesis in order to identify those substrates with the greatest potential for reducing the cost of commercial pullulan. Full article

Global chestnut production is rising. However, the Portuguese chestnut industry still experiences annual post-harvest losses, largely due to microbial spoilage. Recovering high-value starch from spoiled chestnuts offers a promising strategy to reduce waste and increase economic returns. Yet, starch extracted from spoiled kernels is typically dark brown, limiting its industrial applications. This study aimed to enhance the sustainability of the chestnut sector by converting industrial byproducts into useful ingredients. We evaluated whether hypochlorite-mediated oxidative extraction at pHs around 8 and 12 could produce starch with functional properties suitable for industrial applications. Both native and bleached starches showed similar lightness (L* 84–91), though a slight yellow hue remained (ΔE* 12–19). The degree of crystallinity was higher in bleached starches (13–16%) while preserving the characteristic C_B-type crystalline pattern of native chestnut starch. The degree of oxidation was 0.88% and 0.43% for bleached starches isolated at pHs 8 and 12, respectively. Starch bleached at pH 8 exhibited moderate viscosity (breakdown 0.103) and greater swelling capacity at 50 °C than corn starch. In contrast, extraction under alkaline conditions markedly reduced gelatinization and retrogradation performance. Therefore, oxidative extraction at middle pH proved to be the most effective method for recovering functional starch from spoiled chestnuts. Full article

This study aimed to biosynthesize silver nanoparticles (AgNPs) using aqueous leaf extract of Galega officinalis and to evaluate their insecticidal activity against key stored-product pests. AgNP formation was confirmed through UV–vis spectroscopy, which showed a surface plasmon resonance peak at 380 nm. FTIR analysis indicated the presence of plant-derived functional groups likely involved in the reduction and stabilization of Ag⁺ ions. Dynamic light scattering revealed an average hydrodynamic diameter of 25.07 nm, a PDI of 0.39, and a zeta potential of −22 mV, while TEM images showed predominantly spherical and polydisperse particles ranging from 4.3 to 42.4 nm. Insecticidal bioassays performed on Sitophilus granarius, Tribolium confusum, Plodia interpunctella, and Ephestia kuehniella revealed concentration-dependent mortality. The highest mortality rates were recorded at 1000 ppm, reaching 100% in T. confusum, 83.33% in P. interpunctella, and 76.67% in both S. granarius and E. kuehniella. These findings demonstrate the potent insecticidal activity of G. officinalis-mediated AgNPs and support their potential as environmentally friendly alternatives for stored-product pest management, warranting further studies on safety, large-scale synthesis, and integration into pest-control programs. Full article

Accurate segmentation of adhered (sticky) corn kernels and reliable damage detection are critical for quality control in corn processing and kernel selection. Traditional watershed algorithms suffer from over-segmentation, whereas deep learning methods require large annotated datasets that are impractical in most industrial settings. This study proposes W&C-SVM, a hybrid computer vision method that integrates an improved watershed algorithm (Sobel gradient and Euclidean distance transform), convex hull defect detection and an SVM classifier trained on only 50 images. On an independent test set, W&C-SVM achieved the highest damage detection accuracy of 94.3%, significantly outperforming traditional watershed SVM (TW + SVM) (74.6%), GrabCut (84.5%) and U-Net trained on the same 50 images (85.7%). The method effectively separates severely adhered kernels and identifies mechanical damage, supporting the selection of intact kernels for quality control. W&C-SVM offers a low-cost, small-sample solution ideally suited for small-to-medium food enterprises and breeding laboratories. Full article

Corn fermentation in biorefineries produces residual biomass and by-products, particularly corn kernel fiber and outgassed carbon dioxide (CO₂), that have value-added potential for improving sugar and bioethanol conversions. Recovered corn kernel fiber contains lignocellulosic components which can be made accessible by pretreating the biomass with an alkaline sodium carbonate solution made with captured CO₂ and then used as supplemental biomass in corn ethanol production. In this work, different ratios of whole and degermed corn kernel fibers are pretreated and mixed with corn to be evaluated as beneficial ingredients in bioethanol co-fermentation. Sugar yields from enzymatic hydrolysis demonstrate the pretreatment promotes saccharification reaching over 70% total sugar conversion for the whole corn fibers. During co-fermentation, 10 and 20% corn solid loadings significantly increased ethanol yields while additional corn fiber loadings increased sugar yields. Conversion rates and yields were similar between the whole and degermed corn fibers supporting how a single recovery design can benefit multiple corn streams. Full article

Pod corn (Zea mays var. tunicata) bears leafy glumes that enclose kernels, resembling a partial reversion to wild-forms, yet remains poorly characterized in situ in Mexico. We evaluated Mexican accessions at two contrasting locations to quantify morphological/phenological diversity and to assess functional traits via proximate kernel composition. Standard descriptors captured variation in plant architecture, tassel/ear traits (including glume length), and reproductive timing. Accessions showed strong plasticity and significant accession × environment effects on ear morphology and maturation. Grain yield ranged from 6.32 to 10.78 t ha⁻¹, with peak values comparable to commercial hybrids and above-typical yields reported for native Mexican races (2.7–6.6 t ha⁻¹). Proximate analysis showed that milling with the tunic increased moisture/ash (up to 3.07% vs. 1.80% in dehulled grain), tended to lower fat and protein, and yielded lower crude fiber than dehulled samples (0.78–0.96% vs. 1.59–1.77%); protein varied widely (1.05–6.64%). Thus, the tunic modulates elemental composition, informing processing choices (with vs. without tunic). Our results document a spectrum of morphotypes and highlight developmental diversity and field adaptability. The observed accession × environment responses provide a practical baseline for comparisons with native and improved varieties, and help guide product development strategies. Collectively, these data underscore the high productive potential of pod corn (up to 10.78 t ha⁻¹ under optimal management) and show that including the tunic substantially alters proximate composition, establishing a quantitative foundation for genetic improvement and food applications. Overall, pod corn’s distinctive ear morphology and context-dependent composition reinforce its value for conservation, developmental genetics, and low-input systems. Full article