Search Results (65)

To ensure the normal water status of apple rootstock softwood cuttings during the initial stage of cutting, a drought stress monitoring model was designed. The model is optimized based on the YOLOv11n-seg instance segmentation model, using the leaf curl degree of cuttings as the classification basis for drought-stress grades. The backbone structure of the IMYOLOv11n-seg model is improved by the C3K2_CMUNeXt module and the multi-head self-attention (MHSA) mechanism module. The neck part is optimized by the KFHA module (Kalman filter and Hungarian algorithm model), and the head part enhances post-processing effects through HIoU-SD (hierarchical IoU–spatial distance filtering algorithm). The IMYOLOv11-seg model achieves an average inference speed of 33.53 FPS (frames per second) and the mean intersection over union (MIoU) value of 0.927. The average recognition accuracies for cuttings under normal water status, mild drought stress, moderate drought stress, and severe drought stress are 94.39%, 93.27%, 94.31%, and 94.71%, respectively. The IMYOLOv11n-seg model demonstrates the best comprehensive performance in ablation and comparative experiments. The automatic humidification system equipped with the IMYOLOv11n-seg model saves 6.14% more water than the labor group. This study provides a design approach for an automatic humidification system in protected agriculture during apple rootstock cutting propagation. Full article

This study examines students’ perceptions of the subjects geography, mathematics, and computer science in integrated science, technology, engineering, and mathematics (STEM) lessons. Although the importance of an integrated approach in STEM education is emphasized, researchers are not clear about whether students perceive connections between the subjects on the one hand and subject-specific working methods and content in integrated lessons on the other. Data was collected in an integrated teaching unit on the sustainability of apples using an open-ended digital questionnaire in to two ninth grade classes in Hamburg, Germany (n = 38); this data was analyzed using qualitative content analysis. The results reveal that students perceive the subjects differently, but similarities can also be identified. While subject-specific content is perceived—such as the use of maps in geography, the calculation of volumes in mathematics, and Dijkstra’s algorithm in computer science—methodological connections, such as calculating, analyzing diagrams, or solving problems, are anchored across disciplines. This suggests that the subject-specific contents are not lost in integrating lessons, and that connections among the subjects are, to a certain extent, promoted. Full article

In order to maintain adequate hydration of apple rootstock softwood cuttings during the initial stage of cutting, a drought stress grading model based on machine vision was designed. This model was optimized based on the U-Net (U-shaped Neural Network), and the petiole morphology of the cuttings was used as the basis for classifying the drought stress levels. For the CU-ICA-Net model, which is obtained by improving U-Net with the ICA (Improved Coordinate Attention) module designed using a cascaded structure and dynamic convolution, the average accuracy rate of the predictions for the three parts of the cuttings, namely the leaf, stem, and petiole, is 93.37%. The R² values of the prediction results for the petiole curvature k and the angle α between the petiole and the stem are 0.8109 and 0.8123, respectively. The dataset used for model training consists of 1200 RGB images of cuttings under different grades of drought stress. The ratio of the training set to the test set is 1:0.7. A humidification test was carried out using an automatic humidification system equipped with this model. The MIoU (Mean Intersection over Union) value is 0.913, and the FPS (Frames Per Second) value is 31.90. The test results prove that the improved U-Net model has excellent performance, providing a method for the design of an automatic humidification control system for industrialized cutting propagation of apple rootstocks. Full article

While educational excursions are widely acknowledged to enhance student learning through immersive, real-world experiences, there is limited research on how students can best capture and retain knowledge during such activities. Traditional note-taking methods, such as pen and paper or digital devices, may be inadequate for recording spatial or multimodal information encountered in these dynamic environments. With the emergence of mixed reality (MR) technologies, there is an opportunity to explore spatial, immersive note-taking that aligns with the dynamic nature of field-based learning. This study compares the effectiveness of mixed reality, pen and paper, and digital note-taking during educational excursions. A total of 50 participants in grades 7 through 12 used the Apple Vision Pro headset for mixed reality notes, mobile phones for digital notes, and clipboards paired with a pen and paper for traditional notes. The information encountered was categorised as physical, textual, or video-based. The effectiveness was evaluated through three measures: content extracted and organised in notes, post-activity quizzes on retention and critical thinking, and participant feedback. For physical information, mixed reality significantly improved the content extraction and retention. For textual information, mixed reality yielded more content, but pen and paper outperformed it in terms of organisation. Statistically, all the note-taking methods were equally effective in the remaining aspects. Although mixed reality shows potential to be integrated into educational excursions, participant feedback highlighted discomfort with the headset, suggesting that mixed reality should complement, not replace, traditional approaches. Full article

The successful introduction of new cultivars depends on the evaluation of complex parameters essential for the consumers, market, and fruit producers. A new scab-resistant apple cultivar, ‘Wuranda’ (SQ159/Natyra^®/Magic Star^® × Honeycrisp), recently introduced in Norway and managed under the name Fryd^©, is prone to biennial bearing. Therefore, one of the first tasks, investigated in Southwestern Norway by the Norwegian Institute of Bioeconomy Research, NIBIO-Ullensvang in 2021–2024, was the establishment of optimal crop load level based on the combination of productivity, fruit quality, and return bloom. The apple cultivar Fryd (‘Wuranda’) was propagated on ‘M.9’ rootstock and planted in 2019. The trial was performed in the same orchard for four consecutive years, starting three years after planting. Crop load level affected average fruit mass but had no impact on cv. Fryd fruit quality parameters at harvest such as blush, ground color, firmness, soluble solid content, or starch degradation. Fruit size variation was diminished by crop load regulation, and most fruits fell into 2–3 grading classes. Crop load, not the yield per tree, was the determining factor for the return bloom. The optimal crop load level depended on the orchard age. To guarantee a regular bearing mode of cv. Fryd planted on M.9 rootstock at a 3.5 × 1 m distance and trained as slender spindle, crop load of 5.5–6 fruits cm⁻² TCSA (trunk cross-sectional area) in the 3rd year, 7.5–8 fruits cm⁻² TCSA in the 4th year, and 6.5–7 fruits cm⁻² TCSA in the 5th year should be maintained. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms, with tremors being one of the most prominent. Traditional assessment methods, such as the Unified Parkinson’s Disease Rating Scale (UPDRS), rely on subjective, intermittent evaluations, which can miss symptom fluctuations. This study presents the development and validation of the ELENA system, an IoT-based wearable device designed for the continuous monitoring of tremors in PD patients and medication tracking in PD patients. Named in honor of a 67-year-old woman who has lived with Parkinson’s since 2011 and inspired the project, the ELENA system integrates an MPU6050 accelerometer, an ESP32 microcontroller, and cloud-based data analysis and MATLAB. The ELENA system was calibrated and validated against an Apple Watch, demonstrating high accuracy with frequency deviations under 0.5% and an average percentage error of −0.37%. Unlike commercial devices, ELENA offers a clinical-grade solution with customizable data access and visualization tailored for healthcare providers. Participants, including PD patients and a non-PD control group, completed a series of clinical tasks to evaluate tremor monitoring capabilities. The results showed that the system effectively captured tremor frequency and amplitude, enabling the analysis of resting, action, and postural tremors. This study highlights the ELENA system’s potential to enhance PD management by providing real-time, remote monitoring of tremors. The scalable, cost-effective solution supports healthcare professionals in tracking disease progression and optimizing treatment plans, paving the way for improved patient outcomes. Full article

This study addresses inefficiencies in manual apple harvesting and high damage rates in mechanical methods by developing an airflow-cushioned conveyor to minimize postharvest losses. Analyzing apple dynamics in pipelines and collision mechanics identified three key damage factors: fruit size, conveyor linear velocity, and airflow speed. A Box–Behnken-designed response surface model linked these parameters to damage area and collision force. The results showed optimal settings for small (grade III: 11 m/min, 18.2 m/s; 34.24 mm², 8.7 N), medium (grade II: 11 m/min, 19.01 m/s; 48.62 mm², 9.52 N), and large apples (grade I: 11 m/min, 19.3 m/s; 67.01 mm², 10.34 N). Under the optimal parameters, the damage rate for grade I apples was only 12%, while grade II apples had a 0% damage rate, fully meeting the grade II standards. This damage rate was significantly lower than the over 50% damage rate observed in vibration harvesting. Additionally, the harvesting speed using the optimized device increased by more than twice compared to traditional manual harvesting. The findings provide an engineering case for balancing fruit quality maintenance and harvesting speed improvement. Full article

To address the issues of insufficient rigor in existing methods for quantifying apple watercore severity and the complexity and low accuracy of traditional classification models, this study proposes a method for watercore quantification and a classification model based on a deep convolutional neural network. Initially, visible/near-infrared transmission spectral data of apple samples were collected. The apples were then sliced into 4.5 mm thick sections using a specialized tool, and image data of each slice were captured. Using BiSeNet and RIFE algorithms, a three-dimensional model of the watercore regions was constructed from the apple slices to calculate the watercore severity, which was subsequently categorized into five distinct levels. Next, methods such as the Gramian Angular Summation Field (GASF), Gram Angular Difference Field (GADF), and Markov Transition Field (MTF) were applied to transform the one-dimensional spectral data into two-dimensional images. These images served as input for training and prediction using the ConvNeXt deep convolutional neural network. The results indicated that the GADF method yielded the best performance, achieving a test set accuracy of 98.73%. Furthermore, the study contrasted the classification and prediction of watercore apples using traditional methods with the existing quantification approaches for watercore levels. The comparative results demonstrated that the proposed GADF-ConvNeXt model is more straightforward and efficient, achieving superior performance in classifying watercore grades. Furthermore, the newly proposed quantification method for watercore levels proved to be more effective. Full article

Precise photogrammetric mapping of preharvest conditions in an apple orchard can help determine the exact position and volume of single apple fruits. This can help estimate upcoming yields and prevent losses through spatially precise cultivation measures. These parameters also are the basis for effective storage management decisions, post-harvest. These spatial orchard characteristics can be determined by low-cost drone technology with a consumer grade red-green-blue (RGB) sensor. Flights were conducted in a specified setting to enhance the signal-to-noise ratio of the orchard imagery. Two different altitudes of 7.5 m and 10 m were tested to estimate the optimum performance. A multi-seasonal field campaign was conducted on an apple orchard in Brandenburg, Germany. The test site consisted of an area of 0.5 ha with 1334 trees, including the varieties ‘Gala’ and ‘Jonaprince’. Four rows of trees were tested each season, consisting of 14 blocks with eight trees each. Ripe apples were detected by their color and structure from a photogrammetrically created three-dimensional point cloud with an automatic algorithm. The detection included the position, number, volume and mass of apples for all blocks over the orchard. Results show that the identification of ripe apple fruit is possible in RGB point clouds. Model coefficients of determination ranged from 0.41 for data captured at an altitude of 7.5 m for 2018 to 0.40 and 0.53 for data from a 10 m altitude, for 2018 and 2020, respectively. Model performance was weaker for the last captured tree rows because data coverage was lower. The model underestimated the number of apples per block, which is reasonable, as leaves cover some of the fruits. However, a good relationship to the yield mass per block was found when the estimated apple volume per block was combined with a mean apple density per variety. Overall, coefficients of determination of 0.56 (for the 7.5 m altitude flight) and 0.76 (for the 10 m flights) were achieved. Therefore, we conclude that mapping at an altitude of 10 m performs better than 7.5 m, in the context of low-altitude UAV flights for the estimation of ripe apple parameters directly from 3D RGB dense point clouds. Full article

In the domain of food science, apple grading holds significant research value and application potential. Currently, apple grading predominantly relies on manual methods, which present challenges such as low production efficiency and high subjectivity. This study marks the first integration of advanced computer vision, image processing, and machine learning technologies to design an innovative automated apple grading system. The system aims to reduce human interference and enhance grading efficiency and accuracy. A lightweight detection algorithm, FDNet-p, was developed to capture stem features, and a strategy for auxiliary positioning was designed for image acquisition. An improved DPC-AWKNN segmentation algorithm is proposed for segmenting the apple body. Image processing techniques are employed to extract apple features, such as color, shape, and diameter, culminating in the development of an intelligent apple grading model using the GBDT algorithm. Experimental results demonstrate that, in stem detection tasks, the lightweight FDNet-p model exhibits superior performance compared to various detection models, achieving an mAP@0.5 of 96.6%, with a GFLOPs of 3.4 and a model size of just 2.5 MB. In apple grading experiments, the GBDT grading model achieved the best comprehensive performance among classification models, with weighted Jacard Score, Precision, Recall, and F1 Score values of 0.9506, 0.9196, 0.9683, and 0.9513, respectively. The proposed stem detection and apple body classification models provide innovative solutions for detection and classification tasks in automated fruit grading, offering a comprehensive and replicable research framework for standardizing image processing and feature extraction for apples and similar spherical fruit bodies. Full article

The immobilisation of essential oil components (EOCs) on food-grade supports is a promising strategy for preserving liquid foods without the drawbacks of direct EOC addition such as poor solubility, high volatility, and sensory alterations. This study presents a novel method for covalently immobilising EOCs, specifically thymol and carvacrol, on SiO₂ particles (5–15 µm) using the Mannich reaction. This approach simplifies conventional covalent immobilisation techniques by reducing the steps and reagents while maintaining antimicrobial efficacy and preventing compound migration. The antimicrobial effectiveness of the EOC–SiO₂ system, applied as an additive, was tested against foodborne pathogens (Escherichia coli, Salmonella enterica, Staphylococcus aureus, and Listeria monocytogenes) inoculated into phosphate buffer solution and fresh apple juice. The results showed high antimicrobial activity, with inactivation exceeding 4-log reductions, depending on the EOC type, target microorganism, and medium. Moreover, the addition of functionalised particles did not affect the juice organoleptic properties. This study demonstrates that the Mannich reaction is an effective method for developing antimicrobial systems based on the covalent immobilisation of EOCs on silica particles, and offers a practical solution for food preservation without compromising food quality. Full article

Aiming at the problem of high false detection and missed detection rate of apple surface defects in complex environments, a new apple surface defect detection network: space-to-depth convolution-Multi-scale Empty Attention-Context Guided Feature Pyramid Network-You Only Look Once version 8 nano (SMC-YOLOv8n) is designed. Firstly, space-to-depth convolution (SPD-Conv) is introduced before each Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) in the backbone network as a preprocessing step to improve the quality of input data. Secondly, the Bottleneck in C2f is removed in the neck, and Multi-scale Empty Attention (MSDA) is introduced to enhance the feature extraction ability. Finally, the Context Guided Feature Pyramid Network (CGFPN) is used to replace the Concat method of the neck for feature fusion, thereby improving the expression ability of the features. Compared with the YOLOv8n baseline network, mean Average Precision (mAP) 50 increased by 2.7% and 1.1%, respectively, and mAP50-95 increased by 4.1% and 2.7%, respectively, on the visible light apple surface defect data set and public data set in the self-made complex environments.The experimental results show that SMC-YOLOv8n shows higher efficiency in apple defect detection, which lays a solid foundation for intelligent picking and grading of apples. Full article

Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for fruit sorting by applying hyperspectral analysis and machine learning to improve product quality and reduce losses. The employed hyperspectral technologies and machine learning algorithms enable the rapid and accurate detection of defects on the surface of fruits, enhancing product quality and reducing the number of rejects, thereby contributing to the sustainability of agriculture. This study seeks to advance commercial fruit quality control by comparing hyperspectral image classification algorithms to detect apple lesions caused by pathogens, including sunburn, scab, and rot, on three apple varieties: Honeycrisp, Gala, and Jonagold. The lesions were confirmed independently using expert judgment, real-time PCR, and 3D fluorimetry, providing a high accuracy of ground truth data and allowing conclusions to be drawn on ways to improve the sustainability and safety of the agrocenosis in which the fruits are grown. Hyperspectral imaging combined with mathematical analysis revealed that Venturia inaequalis is the main pathogen responsible for scab, while Botrytis cinerea and Penicillium expansum are the main causes of rot. This comparative study is important because it provides a detailed analysis of the performance of both supervised and unsupervised classification methods for hyperspectral imagery, which is essential for the development of reliable automated grading systems. Support Vector Machines (SVM) proved to be the most accurate, with the highest average adjusted Rand Index (ARI) scores for sunscald (0.789), scab (0.818), and rot (0.854), making it the preferred approach for classifying apple lesions during grading. K-Means performed well for scab (0.786) and rot (0.84) classes, but showed limitations with lower metrics for other lesion types. A design and technological scheme of an optical system for identifying micro- and macro-damage to fruit tissues is proposed, and the dependence of the percentage of apple damage on the rotation frequency of the sorting line rollers is obtained. The optimal values for the rotation frequency of the rollers, at which the damage to apples is less than 5%, are up to 6 Hz. The results of this study confirm the high potential of hyperspectral data for the non-invasive recognition and classification of apple diseases in automated sorting systems with an accuracy comparable to that of human experts. These results provide valuable insights into the optimization of machine learning algorithms for agricultural applications, contributing to the development of more efficient and accurate fruit quality control systems, improved production sustainability, and the long-term storage of fruits. Full article

Realizing the integration of intelligent fruit picking and grading for apple harvesting robots is an inevitable requirement for the future development of smart agriculture and precision agriculture. Therefore, an apple maximum diameter estimation model based on RGB-D camera fusion depth information was proposed in the study. Firstly, the maximum diameter parameters of Red Fuji apples were collected, and the results were statistically analyzed. Then, based on the Intel RealSense D435 RGB-D depth camera and LabelImg software, the depth information of apples and the two-dimensional size information of fruit images were obtained. Furthermore, the relationship between fruit depth information, two-dimensional size information of fruit images, and the maximum diameter of apples was explored. Based on Origin software, multiple regression analysis and nonlinear surface fitting were used to analyze the correlation between fruit depth, diagonal length of fruit bounding rectangle, and maximum diameter. A model for estimating the maximum diameter of apples was constructed. Finally, the constructed maximum diameter estimation model was experimentally validated and evaluated for imitation apples in the laboratory and fruits on the Red Fuji fruit trees in modern apple orchards. The experimental results showed that the average maximum relative error of the constructed model in the laboratory imitation apple validation set was ±4.1%, the correlation coefficient (R²) of the estimated model was 0.98613, and the root mean square error (RMSE) was 3.21 mm. The average maximum diameter estimation relative error on the modern orchard Red Fuji apple validation set was ±3.77%, the correlation coefficient (R²) of the estimation model was 0.84, and the root mean square error (RMSE) was 3.95 mm. The proposed model can provide theoretical basis and technical support for the selective apple-picking operation of intelligent robots based on apple size grading. Full article

Lipids are emerging as important potential targets for the early diagnosis and prognosis of several inflammatory diseases. Studying the lipid profiles is important for understanding cellular events such as low-grade inflammation, a condition common to many human diseases, including cancer, neurodegenerative diseases, diabetes, and obesity. This work aimed to explore lipid signatures in an inflammation cellular model using an advanced bioanalytical approach complemented by Machine Learning techniques. Analyses based on the high-resolution mass spectrometry of extracted lipids in TNF-α inflamed cells (R3/1 NF-κB reporter cells) versus lipids in control cells resulted in 469 quantified lipids, of which 20% were phosphatidylcholines (PCs) and phosphatidylethanolamines (PEs), 10% were sphingomyelins (SMs), 6% were phosphatidylinositols (PIs), 7% were ceramides (Cer), 6% were phosphatidylglycerols (PGs), and 5% were phosphatidylserines (PSs). TNF-α induced a significant alteration compared to the control, with a fold change higher than 1.5; of the 88 lipids, 71 were upregulated and 17 were downregulated, impacting various pathways as revealed by network analyses. To validate the inflammation model, the TNF-α induced cells were treated with polyphenols from thinned young apples (TAPs), which are known to have anti-inflammatory properties. The dysregulation of ceramides (Cer(d18:1/23:0), Cer(d18:1/23:0), and Cer(d18:1/22:0)) observed in TNF-α inflamed cells was completely reverted after TAP treatment. Network analyses showed the alteration of arachidonic acid and TNF signaling, which were modulated by polyphenols from thinned young apples. The results highlighted the potentiality of the inflammatory model and the bioanalytical approach to describe lipid profiles in complex biological matrices and different states. In addition, the quantified lipids were interpreted by an Artificial Intelligence approach to identify relevant signatures and clusters of lipids that can impact cellular states. Lastly, this study underlines both the potential applications of lipidomics combined with Machine Learning and how to build and validate Machine Learning models to predict inflammation based on lipid-related pattern signatures. Full article