Search Results (229)

The Korla fragrant pear is a highly valued economic fruit in China’s Xinjiang region. However, biomechanical data on the fruit-bearing branches and pedicels of this species remain incomplete, which to some extent hinders the advancement of harvesting equipment and techniques. Therefore, refining these data is of great significance for the development of efficient and non-destructive harvesting strategies. This study aims to elucidate the mechanical properties of the fruiting branches and peduncles of Korla fragrant pears, thereby establishing a theoretical foundation for the future development of intelligent harvesting technology for this variety. The research utilized axial and radial compression tests, along with three-point bending test methods, to quantitatively analyze the elastic modulus and shear modulus of the branches and peduncles. The test results reveal that the elastic modulus of the fruiting branches under axial compression is 263.51 ± 76.51 MPa, while under radial compression, it measures 135.53 ± 73.73 MPa (where ± represents the standard deviation). In comparison, the elastic modulus of the peduncles is recorded at 152.96 ± 119.95 MPa. Additionally, the three-point bending test yielded a shear modulus of 75.48 ± 32.84 MPa for the branches and 30.23 ± 8.50 MPa for the peduncles. Using finite element static structural analysis, the simulation results aligned closely with the experimental data, falling within an acceptable error range, thus validating the reliability of the testing methods and outcomes. The mechanical parameters obtained in this study are critical for modeling the stress and deformation behaviors of pear-bearing structures during mechanical harvesting. These findings provide valuable theoretical support for the optimization of harvesting device design and operational strategies, with the aim of reducing fruit damage and improving harvesting efficiency in pear orchards. Full article

Accurate estimation aboveground biomass (AGB) in winter wheat is crucial for yield assessment but remains challenging to achieve non-destructively. Unmanned aerial vehicle (UAV)-based remote sensing offers a promising solution at the plot level. Traditional field sampling methods, such as random plant selection or full-quadrat harvesting, are labor intensive and may introduce substantial errors compared to the canopy-level estimates obtained from UAV imagery. This study proposes a novel method using Fractional Vegetation Coverage (FVC) to adjust field-sampled AGB to per-plant biomass, enhancing the accuracy of AGB estimation using UAV imagery. Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. The aim was to evaluate the performance of multimodal data in estimating winter wheat leaves, spikes, stems, and total AGB. Results demonstrated that (1) FVC-adjusted per-plant biomass significantly improved correlations with most indicators, particularly during the filling stage, when the correlation between leaf biomass and NDVI increased by 56.1%; (2) RF and NN models outperformed SVM, with the optimal accuracies being R² = 0.709, RMSE = 0.114 g for RF, R² = 0.66, RMSE = 0.08 g for NN, and R² = 0.557, RMSE = 0.117 g for SVM. Notably, the RF model achieved the highest prediction accuracy for leaf biomass during the flowering stage (R² = 0.709, RMSE = 0.114); (3) among different water treatments, the R² values of water and drought treatments were higher 0.723 and 0.742, respectively, indicating strong adaptability. This study provides an economically effective method for monitoring winter wheat growth in the field, contributing to improved agricultural productivity and fertilization management. Full article

Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible–near-infrared (VIS–NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods—Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)—were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R² average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting. Full article

The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an ${\mathrm{R}}_V^2$ of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the ${\mathrm{R}}_V^2$ to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the ${\mathrm{R}}_V^2$ to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes. Full article

Model updating is a challenging task with regard to maintaining the performance of non-destructive detection models while using hyperspectral imaging techniques for detecting the internal quality of fresh fruits like blueberries. Different sample batches and differences in hyperspectral image acquisition environments may lead to a significant decline in the performance of hyperspectral detection models. This study investigated the transferability of a hyperspectral model for the quantitating soluble solid content of blueberries across different batches for two harvest years. Hyperspectral images and SSC values of blueberries were collected from two batches, including 364 samples from 2024 and 175 samples from 2025. The differences between SSC measurements and spectral data across these two batches were analyzed. Based on the sample dataset of the year 2024, a high-performance quantitative model for detecting SSC values was established by combining it with partial least squares regression (PLSR) and competitive adaptive reweighted sampling (CARS). This high-performance model could achieve a high determination coefficient (${\mathrm{R}}_{\mathrm{P}}^2$) of 0.8965 and a low root mean square error of prediction (RMSEP) of 0.3707 °Brix. Using the sample dataset for the year 2025, the hyperspectral model was updated by the semi-supervised parameter-free calibration enhancement (SS-PFCE) algorithm. The updated model performed better than those established using individual datasets from 2024 and 2025, and obtained an ${\mathrm{R}}_{\mathrm{P}}^2$ of 0.8347 and an RMSEP of 0.4930 °Brix. This indicates that the calibration transfer strategy is superior in improving hyperspectral model performance. This study demonstrated that the SS-PFCE algorithm, as a calibration transfer strategy, could effectively improve the transferability of the established model for detecting the SSC of blueberries across different sample batches. Full article

Rainwater harvesting (abbreviation: RWH) presents a valuable alternative water source for agriculture, particularly in regions facing water scarcity. However, contaminants leaching from roofing materials, such as asbestos cement (abbreviation: AC), may compromise water quality and affect plant physiological responses. This paper aimed to assess how simulated rainwater, reflecting the different levels of contamination (1, 2, 5, 10, and 20 mg/L), influences leaf temperature in tomato plants (Solanum lycopersicum), a known non-invasive indicator of plant water stress. The treatments were applied over a four-week period under controlled greenhouse conditions. Leaf temperature was monitored using infrared thermography. Results showed that higher treatment concentrations led to a significant increase in leaf temperature, indicating elevated water stress. These findings suggest that even low levels of contaminants originating from roofing materials can induce detectable physiological stress in plants. Monitoring leaf temperature offers a rapid and non-destructive method for assessing environmental water quality impacts on crops. The outcomes of this research have direct applicability in the safer design of RWH systems and in evaluating the suitability of collected rainwater for irrigation use. Full article

Rhizopus stolonifer causes soft rot disease in strawberry and is considered one of the most destructive pathogens affecting strawberries worldwide. This study investigated the efficacy of three atmospheric non-thermal plasmas (NTPs) consisting of gliding arc (GA), Tesla coil (TC) and dielectric barrier discharge (DBD) for controlling R. stolonifer infection. Fungal mycelial discs were exposed to these plasmas for 10, 15 or 20 min, whereas conidial suspensions were treated for 1, 3, 5 or 7 min. Morphological alterations following non-thermal plasma exposure were studied using scanning electron microscopy (SEM). Exposure to GA and DBD plasmas for 20 min completely inhibited mycelial growth. SEM analysis revealed significant structural damage to the mycelium, sporangia and sporangiospores of treated samples compared to untreated controls. Complete inhibition of sporangiospore germination was achieved with treatments for at least 3 min for all NTPs. Pathogenicity assays on strawberry fruit showed that 15 min exposure to any of the tested NTPs completely prevented the development of soft rot disease. Importantly, NTP treatments did not adversely affect the external or internal characteristics of treated strawberries. These findings suggest that atmospheric non-thermal plasmas offer an effective approach for controlling R. stolonifer infection in strawberries, potentially providing a non-chemical alternative for post-harvest disease management. Full article

Holy basil (Ocimum tenuiflorum L.) is a medicinal herb rich in bioactive flavonoids with therapeutic properties. Traditional quantification methods rely on time-consuming and destructive extraction processes, whereas hyperspectral imaging provides a rapid, non-destructive alternative by analysing spectral signatures. However, effectively linking hyperspectral data to flavonoid levels remains a challenge for developing early detection tools before harvest. This study integrates deep learning with hyperspectral imaging to quantify flavonoid contents in 113 samples from 26 Thai holy basil cultivars collected across diverse regions of Thailand. Two deep learning architectures, ResNet1D and CNN1D, were evaluated in combination with feature extraction techniques, including wavelet transformation and Gabor-like filtering. ResNet1D with wavelet transformation achieved optimal performance, yielding an area under the receiver operating characteristic curve (AUC) of 0.8246 and an accuracy of 0.7702 for flavonoid content classification. Cross-validation demonstrated the model’s robust predictive capability in identifying antioxidant-rich samples. Samples with the highest predicted flavonoid content were identified, and cultivars exhibiting elevated levels of both flavonoids and phenolics were highlighted across various regions of Thailand. These findings demonstrate the predictive capability of hyperspectral data combined with deep learning for phytochemical assessment. This approach offers a valuable tool for non-destructive quality evaluation and supports cultivar selection for higher phytochemical content in breeding programs and agricultural applications. Full article

The accurate classification of cocoa pod ripeness is critical for optimizing harvest timing, improving post-harvest processing, and ensuring consistent quality in chocolate production. Traditional ripeness assessment methods are often subjective, labor-intensive, or destructive, highlighting the need for automated, non-invasive solutions. This study evaluates the performance of R-CNN-based deep learning models—Faster R-CNN and Mask R-CNN—for the detection and segmentation of cocoa pods across four ripening stages (0–2 months, 2–4 months, 4–6 months, and >6 months) using the RipSetCocoaCNCH12 dataset, which is publicly accessible, comprising 4116 labeled images collected under real-world field conditions, in the context of precision agriculture. Initial experiments using pretrained weights and standard configurations on a custom COCO-format dataset yielded promising baseline results. Faster R-CNN achieved a mean average precision (mAP) of 64.15%, while Mask R-CNN reached 60.81%, with the highest per-class precision in mature pods (C4) but weaker detection in early stages (C1). To improve model robustness, the dataset was subsequently augmented and balanced, followed by targeted hyperparameter optimization for both architectures. The refined models were then benchmarked against state-of-the-art YOLOv8 networks (YOLOv8x and YOLOv8l-seg). Results showed that YOLOv8x achieved the highest mAP of 86.36%, outperforming YOLOv8l-seg (83.85%), Mask R-CNN (73.20%), and Faster R-CNN (67.75%) in overall detection accuracy. However, the R-CNN models offered valuable instance-level segmentation insights, particularly in complex backgrounds. Furthermore, a qualitative evaluation using confidence heatmaps and error analysis revealed that R-CNN architectures occasionally missed small or partially occluded pods. These findings highlight the complementary strengths of region-based and real-time detectors in precision agriculture and emphasize the need for class-specific enhancements and interpretability tools in real-world deployments. Full article

A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). The models were evaluated using a tenfold cross-validation approach. Non-destructive discrimination of health from all disordered and healthy fruit from fruit with specific disorders was achieved with an accuracy ranging from 72.3 to 97.0% when using spectra collected at harvest and 63.7 to 96.2% when using spectra collected after ripening. No one machine learning algorithm out-performed other methods—for spectra collected at harvest, the highest discrimination accuracy was achieved with RF and MLP for black flesh, J48 for spongy tissue, and LibSVM for soft nose and jelly seed. For spectra collected of stored fruit, the highest discrimination accuracy was achieved with SMO for jelly seed and RF for soft nose. A recommendation is made for the consideration of ensemble models in future. The ability to predict the development of the disorder using spectra of at-harvest fruit offers the potential to guide postharvest practices and reduce incidence of internal disorders in mangoes. Full article

Post-harvest processing (PHP) is a key determinant of coffee quality, flavor profile, and market classification, yet verifying PHP claims remains a significant challenge in the specialty coffee industry. This study introduces near-infrared spectroscopy (NIRS) coupled with chemometrics as a rapid, non-destructive approach to classify green coffee beans based on PHP. For the first time, seven distinct PHP categories—Alchemy, Anaerobic Processing (Deep Fermentation), Dry-Hulled, Honey, Natural, Washed, and Wet-Hulled—were discriminated using NIRS, encompassing 20 different processing protocols under varying environmental and fermentation conditions. The NIR spectra (350–2500 nm) of 524 green Arabica coffee samples were analyzed using PCA-LDA models (750–2450 nm), achieving classification accuracies up to 100% for underrepresented categories and strong performance (91–95%) for dominant PHP groups in an independent test set. These results demonstrate that NIRS can detect subtle chemical signatures associated with diverse PHP techniques, offering a scalable tool for quality assurance, fraud prevention, and traceability in global coffee supply chains. While limited sample sizes for some PHP categories may influence model generalization, this study lays the foundation for future work involving broader datasets and integration with digital traceability systems. The approach has direct implications for producers, traders, and certifying bodies seeking reliable, real-time PHP verification. Full article

Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. To address these challenges, this study investigates a non-destructive approach integrating X-ray imaging and multi-criteria decision (MCD) theory for non-destructive internal defect detection in pears. Internal defects were identified by analyzing grayscale variations in X-ray images. The proposed method combines manual feature-based classifiers, including Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), with a deep convolutional neural network (DCNN) model within an MCD-based fusion framework. Experimental results demonstrated that the fused model achieved a detection accuracy of 97.1%, significantly outperforming individual classifiers. This approach effectively reduced misclassification caused by structural similarities in X-ray images. The study confirms the efficacy of X-ray imaging coupled with multi-classifier fusion for accurate and non-destructive internal quality evaluation of pears, offering practical value for fruit grading and post-harvest management in the pear industry. Full article

The main goal of this study was to investigate and better understand the evolution of the main non-destructive and destructive quality parameters of peach ‘Redhaven’ during ripening process. This study was conducted from 8 to 21 July 2023, during which peaches ‘Redhaven’ were harvested each second day from a commercial orchard located in Novaki Bistranjski. Maturity categories were defined according to different firmness thresholds: maturity for long-distance chain stores (H1), maturity for medium-distance chain stores (H2), maturity below the defined maximum firmness in order to preserve optimal quality traits (H3), ready to buy (H4), ready to eat (H5), and overripe (H6). The chlorophyll absorbance index was the non-destructive parameter that was mostly distinguished between maturity categories (r = 0.78 with firmness), followed by a* and h° ground colour parameters. During the first three maturity categories (H1–H3), firmness had a notably smaller correlation with titratable acidity and the ratio of total soluble solids and titratable acidity, which is not the case for a* and h° ground colour parameters, chlorophyll absorbance index, and the share of additional colour. During the last three maturity categories (H4–H6), non-destructive parameters are not reliable for maturity prediction. When ground colour parameters are measured near petiole insertion, mostly smaller segregation between maturity categories is obtained compared to when measured at the rest of the fruit. Total polyphenol and flavonoid content in peach juice notably corelated only in the last two maturity categories with L* ground colour parameter. Full article

This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900–1700 nm) was used to develop predictive models for soluble solids content (SSC) and firmness (FF), testing multiple preprocessing methods within a Partial Least Squares Regression (PLSR) framework. SNV preprocessing achieved the best predictions for FF (R²P = 0.74, RMSEP = 12.342 ± 0.274 N), while the Raw-PLS model showed optimal performance for SSC (R²P = 0.93, RMSEP = 1.142 ± 0.022°Brix). SSC was more robustly predicted than FF, as reflected by RPD values of 2.6 and 1.7, respectively. For ripening stage classification, an Artificial Neural Network (ANN) outperformed other models, correctly classifying 97.8% of samples (R² = 0.95, RMSE = 0.08, MAE = 0.03). These results demonstrate the potential of combining NIR spectroscopy with AI techniques for non-destructive quality assessment and accurate ripeness discrimination. The integration of regression and classification models further supports the development of intelligent decision-support systems to optimize harvest timing and postharvest handling. 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