Search Results (284)

Produce supply chains play a critical role in ensuring fruits and vegetables reach consumers efficiently, affordably, and at optimal freshness. In recent decades, hub-and-spoke network models have emerged as valuable tools for optimizing sustainable cold food supply chains. Traditional optimization efforts typically focus on removing inefficiencies, minimizing lead times, refining inventory management, strengthening supplier relationships, and leveraging technological advancements for better visibility and control. However, the majority of models rely on deterministic approaches that overlook the inherent uncertainties of crop yields, which are further intensified by climate variability. Rising atmospheric CO₂ concentrations, along with shifting temperature patterns and extreme weather events, have a substantial effect on crop productivity and availability. Such uncertainties can prompt distributors to seek alternative sources, increasing costs due to supply chain reconfiguration. This research introduces a stochastic hub-and-spoke network optimization model specifically designed to minimize transportation expenses by determining optimal distribution routes that explicitly account for climate variability effects on crop yields. A use case involving a cold food supply chain (CFSC) was carried out using several weather scenarios based on climate models and real soil data for California. Strawberries were selected as a representative crop, given California’s leading role in strawberry production. Simulation results show that scenarios characterized by increased rainfall during growing seasons result in increased yields, allowing distributors to reduce transportation costs by sourcing from nearby farms. Conversely, scenarios with reduced rainfall and lower yields require sourcing from more distant locations, thereby increasing transportation costs. Nonetheless, supply chain configurations may vary depending on the choice of climate models or weather prediction sources, highlighting the importance of regularly updating scenario inputs to ensure robust planning. This tool aids decision-making by planning climate-resilient supply chains, enhancing preparedness and responsiveness to future climate-related disruptions. Full article

Genetic transformation is an essential tool for investigating gene function and editing genomes. Kiwifruit, recognized as a significant global fresh fruit crop, holds considerable economic and nutritional importance. However, current genetic transformation techniques for kiwifruit are impeded by low efficiency, lengthy culture durations (a minimum of six months), and substantial labor requirements. In this research, we established an efficient system for shoot regeneration and the stable genetic transformation of the ‘Hayward’ cultivar, utilizing leaf explants in conjunction with two strains of Agrobacterium that harbor the expression vector pBI121-35S::GFP, which contains the green fluorescent protein (GFP) gene as a visible marker within the T-DNA region. Our results show that 93.3% of leaf explants responded positively to the regeneration medium, producing multiple independent adventitious shoots around the explants within a six-week period. Furthermore, over 71% of kanamycin-resistant plantlets exhibited robust GFP expression, and the entire transformation process was completed within four months of culture. Southern blot analysis confirmed the stable integration of GFP into the genome, while RT-PCR and fluorescence microscopy validated the sustained expression of GFP in mature plants. This efficient protocol for regeneration and transformation provides a solid foundation for micropropagation and the enhancement of desirable traits in kiwifruit through overexpression and gene silencing techniques. Full article

Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Full article

In order to develop modern polymer films intended for food packaging, materials based on cellulose acetate propionate (CAP) with the addition of Tween 80 as a plasticizer and cinnamic acid (CA), known for its antibacterial properties, were prepared. It should be emphasized that materials based on CAP combined with Tween 80 have not been previously reported in the literature. Therefore, not only is the incorporation of cinnamic acid into these systems an innovative approach, but also the use of the CAP-Tween80 matrix itself represents a novel strategy in the context of the proposed applications. The conducted studies made it possible to assess the properties of the obtained materials with and without the addition of cinnamic acid. The obtained results showed that the addition of cinnamic acid significantly influenced the crucial properties relevant to food storage. The introduction of CA into the polymer matrix notably enhanced the UV barrier properties achieving complete (100%) blockage of UVB radiation and approximately a 20% reduction of UVA transmittance. Furthermore, the modified films exhibited pronounced antibacterial activity, with over 99% reduction in Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa populations observed for samples containing 2 and 3% CA. This antibacterial effect contributed to the extended freshness of stored blueberries. Moreover, the addition of cinnamic acid did not significantly affect the transparency of the films, which remained high (97–99%), thereby allowing the fruit to remain visible. Full article

Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m⁻¹ spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article

Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. A self-developed impact simulation device was designed to induce progressive damage under controlled energy levels, simulating realistic postharvest handling conditions. Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. The proposed CWT-UVE-SVM model achieved an overall classification accuracy of 93.22%, successfully distinguishing intact, mildly bruised, and cumulatively damaged samples. Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. This research not only advances nondestructive detection methods for prune grading but also provides a scalable modeling strategy for cumulative mechanical damage assessment in soft horticultural products. Full article

Tomatoes are among the most widely consumed and economically significant fruits in the world. However, the extensive use of pesticides in their cultivation has led to the contamination of the peels, posing potential health risks to consumers. As one of the top global producers, consumers, and exporters of tomatoes, Mexico requires rapid, non-destructive, and real-time methods for pesticide monitoring. In this study, a detailed characterization of six pesticides using Raman and Fourier Transform Infrared (FT-IR) spectroscopies was carried out to identify their characteristic vibrational modes. The pesticides examined included different chemical classes commonly used in tomato cultivation: organophosphorus (dichlorvos and methamidophos), pyrethroids (lambda-cyhalothrin and cypermethrin), and carbamates (methomyl and benomyl). Tomato peel samples were examined both before and after pesticide application. Prior to treatment, the peel exhibited a well-organized polygonal structure and showed the presence of carotenoid compounds. After pesticide application, no visible structural damage was observed; however, distinct vibrational bands enabled the detection of each pesticide. Organophosphorus pesticides could be identified through vibrational bands associated with P-O and C-S bonds. Pyrethroid detection was facilitated by benzene ring breathing modes and C=C stretching vibrations, while carbamates were identified through C-N stretching contributions. Phytotoxicity testing in the presence of pesticides indicates no significant damage during the germination of tomatoes. Full article

In this review, we aim to provide a comprehensive overview of the roles of fungi in horticultural crops. Their beneficial roles and pathogenic effects are investigated. In addition, the recent advancements in fungal detection and management strategies (especially the use of spectral analysis) are summarized. Beneficial fungi, including plant growth-promoting fungi (PGPF), ectomycorrhizal fungi (ECM), and arbuscular mycorrhizal fungi (AMF), enhance nutrient uptake, promote root and shoot development, improve photosynthetic efficiency, and support plant resilience against biotic and abiotic stresses. Additionally, beneficial fungi contribute to flowering, seed germination, and disease management through biofertilizers, microbial pesticides, and mycoinsecticides. Conversely, pathogenic fungi cause significant diseases affecting roots, stems, leaves, flowers, and fruits, leading to crop yield losses. Advanced spectral analysis techniques, such as Fourier Transform Infrared Spectroscopy (FTIR), Near-Infrared Spectroscopy (NIR), Raman, and Visible and Near-Infrared Spectroscopy (Vis-NIR), alongside traditional methods like Polymerase Chain Reaction (PCR) and Enzyme-Linked Immunosorbent Assay (ELISA), have shown promise in detecting and managing fungal pathogens. Emerging applications of fungi in sustainable agriculture, including biofertilizers and eco-friendly pest management, are discussed, underscoring their potential to enhance crop productivity and mitigate environmental impacts. This review provides a comprehensive understanding of the complex roles of fungi in horticulture and explores innovative detection and management strategies. Full article

Dragon fruit or pitaya (Hylocereus sp.) is an exotic tropical plant gaining popularity in the United States as it is a nutrient-rich fruit with mildly sweet flavor and a good source of fiber. Although high tunnels are being used to produce specialized crops, little is known about how pitaya growth, physiology and nutrient uptake change throughout the production period. This study aims to evaluate the impact of high tunnels and varying rates of vermicompost on three varieties of pitaya, White Pitaya (WP), Yellow Pitaya (YP), and Red Pitaya (RP), to assess the soil and plant nutrient dynamics, spectral reflectance changes and plant growth. Plants were assessed at 120 and 365 DAP (Days After Plantation). YP thrived in a high tunnel compared to an open environment in terms of survival before 120 DAP, with no diseased incidence and higher nutrient retention. The nutrient accumulation in the RP, WP, and YP shoot samples 120 DAP were ranked in the following order, K > N > Ca > Mg > P > Fe > Zn > B > Mn, while 365 DAP, they were ranked as K > Ca > N > Mg > P > S > Fe > Zn > B > Mn. The nutrient accumulation in the RP, WP, and YP, soil samples 120 and 365 DAP were ranked in the following order: N > Ca > Mg > P > K > Na > Zn. Soil nutrients showed a higher concentration of Na and K grown inside the high tunnels in all three pitaya species due to the increased concentration of soluble salts. Spectral reflectance analysis showed that RP and WP had higher reflectance in the visible and NIR region compared to YP due to their higher plant biomass and canopy cover. This study emphasizes the importance of environmental conditions, nutrition strategies, and plant physiology in the different pitaya plant species. The results suggest that high tunnels with appropriate vermicompost can enhance pitaya growth and development. Full article

Milk jujube has become an increasingly popular tropical fruit. The sugar content, which is commonly represented by the soluble solid content (SSC), is a key indicator of the flavor, internal quality, and market value of milk jujubes. Traditional methods for assessing SSC are time-consuming, labor-intensive, and destructive. These methods fail to meet the practical demands of the fruit market. A rapid, stable, and effective non-destructive detection method based on visible/near-infrared (Vis/NIR) spectroscopy is proposed here. A Vis/NIR reflectance spectroscopy system covering 340–1031 nm was constructed to detect SSC in milk jujubes. A structured spectral modeling framework was adopted, consisting of outlier elimination, dataset partitioning, spectral preprocessing, feature selection, and model construction. Comparative experiments were conducted at each step of the framework. Special emphasis was placed on the impact of outlier detection and dataset partitioning strategies on modeling accuracy. A data-augmentation-based unsupervised anomaly sample elimination (DAUASE) strategy was proposed to enhance the data validity. Multiple data partitioning strategies were evaluated, including random selection (RS), Kennard–Stone (KS), and SPXY methods. The KS method achieved the best preservation of the original data distribution, improving the model generalization. Several spectral preprocessing and feature selection methods were used to enhance the modeling performance. Regression models, including support vector regression (SVR), partial least squares regression (PLSR), multiple linear regression (MLR), and backpropagation neural network (BP), were compared. Based on a comprehensive analysis of the above results, the DAUASE + KS + SG + SNV + CARS + SVR model exhibited the highest prediction performance. Specifically, it achieved an average precision (AP_p) of 99.042% on the prediction set, a high coefficient of determination (R_P²) of 0.976, and a low root-mean-square error of prediction (RMSEP) of 0.153. These results indicate that Vis/NIR spectroscopy is highly effective and reliable for the rapid and non-destructive detection of SSC in milk jujubes, and it may also provide a theoretical basis for the practical application of rapid and non-destructive detection in milk jujubes and other jujube varieties. Full article

The objective of this study was to evaluate the feasibility of using visible, near-infrared, and short-wave infrared (VIS/NIR/SWIR) spectroscopy coupled with chemometrics for non-destructive prediction of nutritional components in Galia-type melon fruit. A total of 175 fully ripened melons were analyzed for soluble solids content (SSC), dry matter (DM), pH, and titratable acidity (TA) using partial least squares regression (PLSR), principal components regression (PCR), and multilinear regression (MLR) models. Reflectance spectra were captured at three fruit locations (pedicel, equatorial, and blossom end) in the 350–2500 nm range. The PLSR models yielded the highest accuracy, particularly for SSC (R = 0.80) and SSC/TA (R = 0.79), using equatorial zone data. Variable selection using the genetic algorithm (GA) successfully identified the spectral regions critical for each nutritional parameter at the pedicel, equatorial, and blossom end areas. Key wavelengths for SSC were found around 670–720 nm and 900–1100 nm, with important wavelengths for pH prediction located near 1450 nm, and, for dry matter, in the ranges 1900–1950 nm. Variable importance in projection (VIP) analysis confirmed that specific wavelengths between 680 and 720 nm, 900 and 1000 nm, 1400 and 1500 nm, and 1900 and 2000 nm were consistently critical in predicting the SSC, DM, and SSC/TA ratio. The highest VIP scores for SSC prediction were noted around 690 nm and 950 nm, while dry matter prediction was influenced most by wavelengths in the 1450 nm to 1950 nm range. This study demonstrates the potential of VIS/NIR/SWIR spectroscopy for rapid, non-destructive melon quality assessment, with implications for commercial postharvest management. Full article

Mandarin fruit detection provides crucial technical support for yield prediction and the precise identification and harvesting of mandarin fruits. However, challenges such as occlusion from leaves or branches, the presence of small or partially visible fruits, and limitations in model efficiency pose significant obstacles in a complex orchard environment. To tackle these issues, we propose ELD-YOLO, a lightweight detection framework designed to enhance edge detail preservation and improve the detection of small and occluded fruits. Our method incorporates edge-aware processing to strengthen feature representation, introduces a streamlined detection head that balances accuracy with computational cost, and employs an adaptive upsampling strategy to minimize information loss during feature scaling. Experiments on a mandarin fruit dataset show that ELD-YOLO achieves a precision of 89.7%, a recall of 83.7%, an mAP@50 of 92.1%, and an mAP@50:95 of 68.6% while reducing the parameter count by 15.4% compared with the baseline. These results demonstrate that ELD-YOLO provides an effective and efficient solution for fruit detection in complex orchard scenarios. Full article

Destructive methods, though traditionally used to evaluate fruit safety, frequently do not deliver complete and detailed information. Non-destructive methods, especially spectroscopy, provide an effective solution for fast, efficient, and non-invasive assessments of quality and safety. This study utilized visible and near-infrared (Vis-NIR) spectroscopy to quantify the nitrate content in three cultivars of bell pepper—orange, yellow, and red—across a spectral range spanning 350 to 1150 nanometers. The nitrate content was assessed destructively, and spectral data were examined through partial least squares regression (PLSR). Model efficacy was measured using the root mean square error (RMSE) and coefficient of determination (R²). The R² values, indicative of the model’s predictive efficacy, were determined to be 0.77, 0.85, and 0.81 for the yellow, red, and orange types, respectively. To optimize wavelength selection and improve model performance, a hybrid approach was utilized, integrating a support vector machine (SVM) with four meta-heuristic algorithms: particle swarm optimization (PSO), genetic algorithm (GA), imperialistic competitive algorithm (ICA), and ant colony optimization (ACO). The SVM-PSO approach proved to be the most efficient in pinpointing 15 key wavelengths. Following this, three modeling techniques—PLSR, multiple linear regression (MLR), and artificial neural network (ANN)—were utilized with the identified wavelengths. Among these, ANN represented the best performance, achieving validation R² values of 0.99, 0.97, and 0.92 for the yellow, red, and orange varieties, respectively. Compared to traditional PLSR and MLR models, which reached validation R² values up to 0.93, the ANN model demonstrated a significant improvement in prediction accuracy. This quantitative improvement highlights the advantage of combining hybrid meta-heuristic wavelength selection with ANN modeling. The results underscore the promise of visible/near-infrared (Vis/NIR) spectroscopy, integrated with sophisticated modeling approaches, as an effective non-invasive method for estimating nitrate concentrations in bell peppers. This technique represents a significant advancement in supporting food safety measures and quality assurance processes. Full article

Moldy core is a common disease of apples, and non-destructive, rapid and accurate detection of moldy core apples is essential to ensure food safety and reduce post-harvest economic losses. In this study, the acoustic method was used for the first time for the online detection of moldy core apples, and we explore the feasibility of integrating acoustic and visible–near-infrared spectroscopy (Vis–NIRS) technologies for precise, real-time detection of moldy core in apples. The sound and Vis–NIRS signals of apples were collected using a novel acoustic online detection device and a traditional Vis–NIRS online sorter, respectively. Based on this, traditional machine learning and deep learning classification models were developed for the prediction of healthy, mild, moderate, and severe moldy apples. The results show that the acoustic detection method significantly outperforms the Vis–NIRS method in terms of moldy apple identification accuracy, and the fusion of acoustic and Vis–NIRS data can further improve the model prediction performance. The MLP-Transformer shows the best prediction performance, with the overall classification accuracies for the fusion of Vis–NIRS, acoustic, Vis–NIRS and acoustic reached 89.66%, 96.55%, and 98.62%, respectively. This study demonstrates the excellent performance of acoustic online detection for intra-fruit lesion identification and shows the potential of the fusion of acoustics and Vis–NIRS. Full article

A detailed description of the components of the CRD technique is presented and applied to the detection of organic esters. These molecules typically have a pleasant smell resembling the aroma of flowers and fruits and are responsible for many distinct odors in plants. They are emitted into the atmosphere by natural sources and human production. The weak absorption spectrum of the fifth vibrational overtone of ethyl, ethyl trimethyl, and tert-butyl acetate are recorded to show the sensitivity of the CRD technique. A description of a compact instrument to be used in the near-IR and visible regions will be presented for measurements of ester detection in the field. Potential chemical reactions of esters induced by visible light absorption in the atmosphere are discussed. Full article