Search Results (4,733)

Colored potato cultivars are rich in phenolic compounds that confer high antioxidant capacity; however, these beneficial metabolites could be susceptible to oxidation by polyphenol oxidases (PPOs), leading to enzymatic browning and the loss of antioxidant potential. Despite the agronomic relevance of this trade-off, the dynamics of the PPO gene family (StPPOs) gene expression in pigmented potatoes remains poorly characterized. Here, we present an integrated biochemical and molecular analysis of two purple-fleshed Peruvian landraces (Siriñacha and Angashungo), a partially pigmented landrace (Sapa), and non-pigmented cultivars, including the commercial cultivar Desirée. We quantified the total phenolic content, antioxidant capacity, and enzymatic browning index (EBI) using colorimetric and spectrophotometric methods. We also generated gene expression profiles of ten StPPO genes using semi-quantitative and digital PCR. Purple-fleshed cultivars exhibited significantly higher phenolic content and antioxidant capacity but also displayed accelerated browning kinetics compared to non- or partially pigmented genotypes. Expression analysis revealed cultivar-specific StPPO patterns, with StPPO2 and StPPO8 being markedly upregulated in pigmented materials, particularly StPPO8. These findings provide the first integrated biochemical and transcriptional evidence linking specific StPPO isoforms to enzymatic browning in colored potatoes, and highlight their potential for biotechnological applications. Full article

Vegetable sweet potato shoot tips are harvested repeatedly for fresh markets, but harvest timing and cut length are still determined largely by experience, limiting their translation into mechanized design parameters and control thresholds. We conducted a two-factor shear-mechanics experiment using three cultivars (‘Fu 23’, ‘Fu 18’, and ‘HD-V4’) and five shoot-tip length levels (10–30 cm), while also measuring stem diameter and moisture content. Because shear tests were performed on short stem segments sampled from a fixed internodal position relative to the apex, the length factor is interpreted mainly as a field-operable harvest criterion and only secondarily as a variable partly associated with tissue position. Moisture content was uniformly high and did not differ among cultivars (p > 0.05). In a pooled two-way ANOVA, length significantly affected maximum shear force (p < 0.01), cultivar was also significant (p < 0.05), and the interaction was not significant (p > 0.05). After including stem diameter as a covariate, both diameter and length remained significant, whereas cultivar became non-significant, indicating that stem diameter explains much of the apparent cultivar difference in absolute load. The reported stress is nominal shear stress. Laboratory-based 95th percentile design loads with γ = 1.3 provide conservative engineering thresholds for preliminary design and harvest-window back-calculation. Full article

Steroidal glycoalkaloids (GAs) are key plant defense compounds, yet their effects on insect gut physiology are not fully understood. We investigated how purified α-chaconine and Solanum tuberosum leaf extract influence the gut function and growth of the mealworm Tenebrio molitor. Larvae were exposed to sublethal doses of GAs, and gut contractility, midgut digestive enzyme activity and body weight were analysed over time. Both α-chaconine and potato extract caused a rapid decrease in digestive enzyme activity 2 h after exposure, followed by a clear increase above control levels after 24 h, indicating a time-dependent compensatory response of the digestive system. Gut contractility was significantly enhanced in treated larvae, and larvae exposed to both treatments exhibited a body weight loss over 72 h. These results show that potato glycoalkaloids strongly modulate the gut physiology of T. molitor while allowing continued growth, highlighting both the plasticity of insect digestive responses and the need to consider sublethal, gut-centered effects when evaluating glycoalkaloids as candidates for bioinsecticidal agents. Full article

Potato is one of the most important food crops worldwide, playing a critical role in global food security and agricultural production. The broken and impurity rates are important indicators for evaluating the harvesting quality of potato combine harvesting operations. To address the difficulty of achieving continuous and online detection using traditional methods, this study investigates an online monitoring approach for potato combine harvesting based on machine vision. Considering the characteristics of large material volume, severe overlap, and similar appearance features under field operating conditions, an online monitoring device suitable for potato combine harvesters was designed, along with a corresponding image acquisition and processing workflow. For the online monitoring device, an improved You Only Look Once version 11 (YOLOv11) detection model, was proposed to meet the requirements of multi-object detection in complex operating scenarios. The model incorporates Multi-Scale Depthwise Convolution (MSDConv), C2PSA_DCA (with Directional Context Attention, DCA), and Directional Selective Attention (DSA) modules, and introduces the Efficient Intersection over Union (EIoU) loss function to enhance recognition capability for broken potatoes and multiple types of impurity targets. While maintaining lightweight characteristics, the improved model demonstrates favorable detection accuracy. Field experiment results show that when the combine harvester operates at a forward speed of 3 km/h, the relative errors for broken and impurity rates are measured as 3.78% and 3.67%, respectively. Under extreme operating conditions with a speed of 4 km/h, the corresponding average relative errors rise to 8.30% and 8.72%, respectively. Overall, the online detection results exhibit satisfactory consistency with manual measurements, providing effective technical support for real-time monitoring of harvesting quality in potato combine harvesting operations. Future research will focus on expanding multi-scenario datasets under diverse soil and illumination conditions, as well as integrating detection results with adaptive control strategies to further enhance intelligent harvesting performance. Full article

The potato tuber is a metamorphic organ formed by the expansion of the underground stolon tip. It is an economically important organ and an excellent material for studying the occurrence and development of modified plant organs. However, genetic studies have lagged due to the potato’s complex genetic background. In this study, we used stolons and tubers of the potato ‘Qingshu 9’ at different stages of the tuberization process as samples for transcriptome sequencing and systematically analyzed the transcriptome characteristics of tuberization. Through RT-qPCR analysis, 16 candidate genes related to tuberization were identified. Overexpression verification was performed on one candidate gene, StLSH10, and the results indicated that it might be involved in regulating tuberization. This research provides a theoretical basis for elucidating the molecular mechanism of tuberization and offers a new target to improve potato yield and quality through molecular breeding strategies. Full article

Weed identification and quantification are processes that are usually manual, subjective, and error-prone. Weeds compete with crops for nutrients, minerals, physical space, sunlight, and water. Thus, weed identification is a crucial component of precision agriculture for autonomous removal and site-specific treatments, efficient weed control, and sustainability. Convolutional Neural Networks (CNNs) are very common in weed identification. This work implemented CNN models for semantic segmentation based on the U-Net architecture for automatically segmenting and quantifying weeds in potato crops using RGB images acquired by a drone at 9–10 m height, flying at 1 m/s. Remote sensing images are affected by factors that degrade image quality and the model’s accuracy. Five U-Net variants were evaluated: the original U-Net, Residual U-Net, Double U-Net, Modified U-Net, and AU-Net. The models were trained using the TensorFlow/Keras frameworks on Google Colab Pro+, following the Knowledge Discovery in Databases (KDD) methodology for image analysis. Each model was trained using a diverse custom dataset in uncontrolled environments, considering six classes: background, Broadleaf dock (Rumex obtusifolius), Dandelion (Taraxacum officinale), Kikuyu grass (Cenchrus clandestinum), other weed species, and the crop potato (Solanum tuberosum L.). The models’ segmentation was widely assessed using Mean Dice Coefficient, Mean IoU, and Dice Loss metrics. The results showed that the Residual U-Net model performed the best in multi-class segmentation, achieving a Mean IoU of 0.8021, a performance comparable to or superior to that reported by other authors. Additionally, a Student’s t-test was applied to complement the data analysis, suggesting that the model is reliable for weed quantification. Full article

The threat of plant diseases in economically significant crops of the Solanaceae family, especially tomatoes and potatoes, is a significant challenge to global food security, highlighting the necessity of fast and convenient diagnostic methods. This paper introduces an enhanced MobileNetV2 model to perform automated disease classification through the use of a domain-specific self-supervised learning (SSL) pretraining approach. The model was first trained on 54,303 unlabeled plant images to learn basic botanical representations, followed by fine-tuning under six experimental conditions to optimize disease classification performance. Findings show that SSL pretrained weights consistently outperform traditional ImageNet-based transfer learning, achieving 0.9158 overall accuracy and a weighted F1-score of 0.9143 in joint tomato and potato classification. The model demonstrates strong cross-crop generalization, correctly identifying Early Blight and Late Blight with accuracies of 0.9600 and 0.9359, respectively, and effectively separating disease-specific visual symptoms from host morphology. Confusion matrix analysis further indicates a reduction in misclassification of visually similar necrotic lesions, a common challenge in supervised models. Overall, the proposed SSL architecture enhances the performance of lightweight convolutional neural networks (CNNs) to a large extent, providing a strong, computationally efficient solution for field-deployable diagnostics in precision agriculture, particularly for tomato and potato crops. Full article

Vernonia amygdalina Delile (Asteraceae), commonly known as bitter leaf, is a tropical shrub that may potentially serve as a biopesticide against the Colorado potato beetle Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae), a key pest of potatoes. The beetle’s behavioral response to the methanolic leaf extract of V. amygdalina was evaluated in this study. Using no-choice, dual-choice, and wind-tunnel assays under laboratory conditions, we evaluated responses of larvae and adults to potato leaf discs treated with V. amygdalina extract in a randomized complete block design, measuring feeding behavior, leaf damage levels, and remaining leaf area. The results showed that V. amygdalina had no biocidal effects against the beetle, as no mortality was incurred. However, dose-linked antifeedant effects were evident in both no-choice and dual-choice arenas. Vernonia amygdalina minimized leaf-area loss most effectively at the highest concentration, especially against the larvae. The extract showed no olfactory repellency but acted as an antifeedant, possibly through contact or taste (gustatory) receptors. The consistent behavioral avoidance at higher concentrations suggests that V. amygdalina acts as a form of deterrent against the Colorado potato beetle. Full article

Calmodulin-like proteins (CMLs) serve as core components in plant calcium signal transduction pathways, and they extensively modulate plant growth, development, and adaptive responses to various abiotic stresses. In this study, we cloned the StCML19 gene from potato and generated stable transgenic Arabidopsis thaliana lines constitutively expressing this gene to investigate its functional role under drought stress. Transcriptome analysis revealed that StCML19 was up-regulated under drought conditions. Phenotypic assays showed that overexpressing StCML19 notably increased the seed germination rate and root length of transgenic Arabidopsis under mannitol-induced osmotic stress, and greatly improved the plant survival rate under severe soil drought stress. Physiological analysis showed that when put under drought stress, transgenic plants had higher proline content, better SOD, CAT, and POD activities, and significantly less malondialdehyde (MDA) accumulation than wild-type plants. In addition, overexpression of StCML19 led to greater plant sensitivity to exogenous ABA, with inhibited root growth and delayed seed germination as indicators. Conclusively, this study is the first to make sense of the biological function of potato StCML19 in the drought stress response and views StCML19 as a promising candidate gene for the genetic improvement of drought-tolerant potato varieties. Full article

Dillapiol is a naturally occurring methylenedioxyphenyl compound with insecticide-synergizing activity comparable to piperonyl butoxide (PBO). This study identified structurally related molecules with practical potential for managing insecticide-resistant insects. Six new dillapiol analogs, containing ester- or ether-linked side chains, were synthesized and evaluated as pyrethrum synergists against the Colorado potato beetle (CPB) Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae). Their activity was assessed through bioassays and by quantifying inhibition of Phase I and II detoxification enzymes in vitro and in vivo. All six compounds displayed higher synergistic activity by ingestion than by topical exposure, and each structural class included at least one compound with a synergism ratio greater than 20. In the resistant CPB strain (RS-CPB), two ester compounds inhibited P450 monooxygenase activity in vitro as effectively as PBO, while dillapiol and one ether analog reduced P450 activity in vivo. Notably, all six analogs reduced glutathione S-transferase (GST) activity; the most active was an ether analog with an in vitro IC₅₀ of 0.23 (±0.04) mM. Dillapiol also significantly reduced GST activity in vivo. These analogs demonstrated PBO-equivalent P450 inhibition combined with unique GST inhibition and show promise as alternative synergists for managing insecticide-resistant insects. Full article

Canna (Canna edulis Ker.) starch is an important non-conventional starch in global applications. In this study, the structural and physicochemical properties of canna starches extracted from four different geographical regions in China were investigated. The four starches (CES-DH, CES-MS, CES-YB, and CES-YX) exhibited relatively high total starch contents (82.51–93.22%). Apparent and true amylose contents varied markedly among samples, ranging from 31.44% to 43.62% and from 15.21% to 35.90%, respectively. Morphologically, the granules were oval and disc-shaped, with D₅₀ values of 20.19–48.35 μm. CES-YX showed a distinct C-type pattern, while other starches exhibited B-type crystallinity, and relatively crystallinity values among samples were between 20.53% and 25.36%. IR absorbance ratios R_1047/1022 and R_995/1022 varied from 0.56 to 0.63 and from 1.15 to 1.26, respectively. Gelatinization temperatures and enthalpy revealed distinct thermal behaviors among the starches, corresponding to substantial differences in pasting properties with wide ranges in peak, breakdown, and setback viscosities. All starch pastes exhibited shear-thinning behaviors and weak gel characteristics. Notably, CES-YB demonstrated high potential as an effective food thickener and stabilizer, as distinguished by the high final viscosity and consistency coefficient (K), whereas the high amylose and resistant starch content in CES-YX made it a promising ingredient for low-glycemic-index food formulations. These findings provided a theoretical basis and practical guidance for the targeted utilization of canna starch in the food industry. Full article

Potato (Solanum tuberosum L.), the fourth most important food crop worldwide, serves as a multi-purpose resource for food, feed and industrial raw materials, and plays a pivotal role in safeguarding food security, diversifying dietary structure and boosting the development of agricultural economy. With increasing consumer demand for nutritional quality, elucidating the regulatory mechanisms of potato quality traits has become a research priority. In this study, three potato cultivars with distinct coloration were employed as materials. Metabolomic profiling identified a total of 1128 metabolites, and revealed that pigmented potato cultivars accumulated higher levels of flavonoids and linoleic acid derivatives compared with the white-fleshed cultivar. Transcriptomic analysis uncovered numerous differentially expressed genes (DEGs) among the three cultivars; notably, DEGs in pigmented cultivars were significantly enriched in pathways related to terpenoid backbone biosynthesis, flavonoid biosynthesis, linoleic acid metabolism, and starch and sucrose metabolism. Integrated multi-omics analysis revealed that the high expression of structural genes in the flavonoid biosynthesis pathway is strongly associated with flavonoid accumulation in pigmented potatoes, suggesting that transcriptional upregulation of these genes may be a key driver of flavonoid biosynthesis. Furthermore, several MYB and WD40 family transcription factors were identified as potential regulators of flavonoid and anthocyanin biosynthesis in potato. Collectively, our study provides insight into the regulatory mechanisms underlying the biosynthesis of secondary metabolites in potato by combining transcriptomic and metabolomic approaches, and the findings provide a valuable theoretical basis for the genetic improvement of potato nutritional quality in future breeding programs. Full article

To address the problems of high digging resistance, elevated energy consumption, and severe soil adhesion encountered during mechanized potato harvesting, a bionic potato digging shovel inspired by the corrugated dorsal structure of the white grub was developed. Based on reverse-engineered geometric curves, two longitudinally corrugated shovel models (L-S-1 and L-S-2) were constructed, and a coupled soil–potato–shovel model was established using the Discrete Element Method (DEM) to evaluate soil disturbance characteristics and digging resistance at a forward speed of 0.5 m/s and an entry angle of 35°. The simulation results indicated that the longitudinally corrugated shovel L-S-2 exhibited the best overall performance, reducing digging resistance by 13.87% and increasing the soil fragmentation rate by 20.67% compared with a conventional flat shovel (P-S). Using L-S-2 as the baseline design, additional DEM simulations were conducted at forward speeds ranging from 0.4 to 0.6 m/s to systematically investigate the influence of operating speed on digging performance. To further enhance anti-adhesion performance, a composite bionic shovel (H-L-S-2) was developed by embedding polytetrafluoroethylene (PTFE) hydrophobic material into the surface of L-S-2 and reinforcing the shovel tip using laser cladding. Soil-bin experiments were then performed under controlled conditions with forward speeds of 0.4–0.6 m/s and soil moisture contents of 15–20% at an entry angle of 35°, and the results showed an average resistance reduction rate of 17.46%, with a maximum reduction of 18.02%. Both DEM simulations and soil-bin tests confirmed the effectiveness of the composite bionic shovel in reducing soil adhesion, with the number of adhered soil particles decreasing by 41.2% in simulations and the mass of adhered soil reduced by 37.5% in physical tests. These results demonstrate that coupling a bionic corrugated structure with surface material modification can effectively reduce digging resistance, enhance soil fragmentation, and mitigate soil adhesion, providing a practical approach for optimizing the design of potato digging shovels. Full article

Plant by-products such as apple pomace, potato pulp, and sugar beet pulp can be an excellent source of polyphenols, other phytochemicals and fiber, which is why they can be an excellent addition to snacks. Snacks, on the other hand, contain a lot of sugar and starch, which increases the risk of metabolic diseases and is unfavorable for diabetics, but after adding the above-mentioned by-products, their nutritional and health-promoting value increases. The aim of the study was to examine the effect of different addition levels of a mixture of by-products on the nutritional composition, phytochemical content, antioxidant activity, and quality of corn snacks. It was found that mixtures of by-products are an excellent addition to corn snacks in order to increase the health benefits of the product, as this additive increases the content of polyphenols, phenolic acids, anthocyanins, dietary fiber, tocopherols, especially α- and γ-tocopherol, as well as phytosterols, including β-sitosterol, stigmasterol, and campesterol. Furthermore, it increases the antioxidant potential of the snacks and the nutritional value of the final product, especially protein and ash content, reducing the starch content. Snacks containing a 20% by-product mixture may be recommended due to their improved nutritional and health-promoting properties and acceptable physical characteristics. Full article