Measuring Internal Maturity Parameters Contactless on Intact Table Grape Bunches Using NIR Spectroscopy

Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy

Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible-near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry.

Rapid Determination of Wine Grape Maturity Level from pH, Titratable Acidity, and Sugar Content Using Non-Destructive In Situ Infrared Spectroscopy and Multi-Head Attention Convolutional Neural Networks

In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building upon our previous work focused on estimating sugar content (∘Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties-Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah-during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR-SWIR spectrum (350-2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (TSS) (∘Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input-multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean R2 values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices.

Temperature variability during the growing season affects the quality attributes of table grapes in Pothwar-insight from a new emerging viticulture region in South Asia

Rising air temperature due to climate change has posed a mammoth challenge to global viticulture and key berry quality traits are compromised. Exploring the effects of seasonal temperature variability on berry ripening and quality attributes in different viticulture regions may help in sustainable viticulture industry. The present research was designed to explore the effect of temperature variables on key quality attributes of table grape cultivars in Pothwar region of Pakistan. Key berry quality traits such as total soluble solids (TSS), titratable acidity (TA), maturity indices (MI), ascorbic acid, sugars, total polyphenol contents (TPC) and total anthocyanin contents (TAC) were unlocked for four important table grape cultivars under varying environmental conditions at Chakwal and Islamabad districts for two consecutive vintages of 2019 and 2020. The district Chakwal has up to 0.92 °C, 1.35 °C, 1.12°C and 0.81°C higher Tmin, Tmax, Tmean and diurnal temperature variation (DTV) respectively, compared to Islamabad particularly for the 2019 vintage. The results of the present study revealed that the warmer site (Chakwal) has significantly (P ≤0.05) higher juice pH, TSS (°brix) and maturity indices (MI) particularly for the relatively hotter vintage of 2019. Interestingly, MI was 33% higher for the relatively warmer vintage of 2019 compared to 2020 with relatively lower acidity (up to 38%). Moreover, higher titratable acidity (11.2%), ascorbic acid (28.5%), polyphenols (20.3%) and anthocyanins (10.6%) were noticed for the colder Islamabad compared to Chakwal. Although elevated temperature for warmer location and vintage favoured berry ripening, however key biochemical attributes such as titratable acidity, ascorbic acid, polyphenols and anthocyanins were negatively affected. The findings of the present research provide useful insight into the impact of growing season temperature on key berry attributes and may help devise adaptation strategies to improve berry quality.

Development of a smart spectral analysis method for the determination of mulberry (Morus alba var. nigra L.) juice quality parameters using FT-IR spectroscopy

Recently, the application of Fourier transform infrared (FT-IR) spectroscopy as a noninvasive technique combined with chemometric methods has been widely noted for quality evaluation of agricultural products. Mulberry (Morus alba var. nigra L.) is a native fruit of Iran and there is limited information about its quality characteristics. The present study aims at assessing a nondestructive optical method for determining the internal quality of mulberry juice. To do so, first, FT-IR spectra were acquired in the spectral range 1000-8333 nm. Then, the principal component analysis (PCA) was used to extract the principal components (PCs) which were given as inputs to three predictive models (support vector regression (SVR), partial least square (PLS), and artificial neural network (ANN)) to predict the internal parameters of the mulberry juice. The performance of predictive models showed that SVR got better results for the prediction of ascorbic acid (R 2 = .84, RMSE = 0.29), acidity (R 2 = .71, RMSE = 0.0004), phenol (R 2 = .35, RMSE = 0.19), total anthocyanin (R 2 = .93, RMSE = 5.85), and browning (R 2 = .89, RMSE = 0.062) compared to PLS and ANN. However, the ANN predicted the parameters TSS (R 2 = .98, RMSE = 0.003) and pH (R 2 = .99, RMSE = 0.0009) better than the other two models. The results indicated that a good prediction performance was obtained using the FT-IR technique along with SVR and this method could be easily adapted to detect the quality parameters of mulberry juice.

Assessment of Physicochemical Parameters in Two Winegrapes Varieties after Foliar Application of ZnSO4 and ZnO

One-third of the world's population is suffering from "hidden hunger" due to micronutrient deficiency. Zinc is acquired through diet, leading its deficiency to the development of disorders such as retarded growth, anorexia, infections, and hypogeusia. Accordingly, this study aimed to develop an agronomic workflow for Zn biofortification on two red winegrapes varieties (cv. Castelão and Syrah) and determine the physicochemical implications for winemaking. Both varieties produced in Setúbal (Portugal) were submitted to four foliar applications of ZnSO₄ or ZnO (900 and 1350 g ha^-1, respectively), during the production cycle. At harvest, Zn biofortification reached a 4.3- and 2.3-fold increase with ZnO 1350 g ha^-1 in Castelão and Syrah, respectively (although, with ZnSO₄ 1350 g ha^-1 both varieties revealed an increase in Zn concentration). On a physiological basis, lower values of NDVI were found in the biofortified grapes, although not reflected in photosynthetic parameters with cv. Syrah shows even a potential benefit with the use of Zn fertilizers. Regarding physical and chemical parameters (density, total soluble solids, dry weight, and color), relative to the control no significant changes in both varieties were observed, being suitable for winemaking. It was concluded that ZnSO₄ and ZnO foliar fertilization efficiently increased Zn concentration on both varieties without a negative impact on quality, but cv. Castelão showed a better index of Zn biofortification and pointed to a potentially higher quality for winemaking.

A Survey of Robotic Harvesting Systems and Enabling Technologies

This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made.

Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review

Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.

Effect of Melatonin on Fruit Quality via Decay Inhibition and Enhancement of Antioxidative Enzyme Activities and Genes Expression of Two Mango Cultivars during Cold Storage

The postharvest deterioration of mango fruits is a critical issue limiting mango storage and preservation due to its climacteric nature. This study evaluated the storage behavior of two mango cultivars and their response to exogenous melatonin (MT, 1000 μmol L⁻¹) treatment in attenuating fruit decay and enhancing fruits’ physiological and metabolic processes and gene relative expression subjected to cold storage. MT treatment in both mango cultivars significantly delayed weight loss, firmness, respiration rate, and decay incidence. However, MT did not influence the TSS, TA, and TSS:TA ratio regardless of the cultivar. Moreover, MT inhibited the decrease in total phenol and flavonoid content and AsA content while delaying the increase in the MDA content of mango during storage in both cultivars. In addition, MT dramatically inhibited the enzyme activity of PPO. In contrast, an increase in the activities of antioxidant enzymes (SOD and APX) and PAL and their genes’ relative expression was noticed in MT-treated fruits versus control in both cultivars. However, MT treatment was cultivar dependent in most parameters under study. These results demonstrated that MT treatment could be an essential postharvest treatment in minimizing decay, maintaining fruit quality, and extending mango fruits’ postharvest shelf life by enhancing the physiological and metabolic processes during cold storage.

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

The qualitative and quantitative evaluation of agricultural products has often been carried out using traditional, i.e., destructive, techniques. Due to their inherent disadvantages, non-destructive methods that use near-infrared spectroscopy (NIRS) coupled with chemometrics could be useful for evaluating various agricultural products. Advancements in computational power, machine learning, regression models, artificial neural networks (ANN), and other predictive tools have made their way into NIRS, improving its potential to be a feasible alternative to destructive measurements. Moreover, the incorporation of suitable preprocessing techniques and wavelength selection methods has arguably proven its practical feasibility. This review focuses on the various computation methods used for processing the spectral data collected and discusses the potential applications of NIRS for evaluating the quality and safety of agricultural products. The challenges associated with this technology are also discussed, as well as potential future perspectives. We conclude that NIRS is a potentially useful tool for the rapid assessment of the quality and safety of agricultural products.

Involvement of Anthocyanin Biosynthesis of Cabernet Sauvignon Grape Skins in Response to Field Screening and In Vitro Culture Irradiating Infrared Radiation

To study the role of infrared (IR) radiation in the color change of the grape berry, field screening (IR-) and in vitro culture irradiation (IR+) were used. Acylated anthocyanin biosyntheses, including the biosynthesis of malvidin 3-O-glucoside, peonidin 3-O-glucoside, and their derivatives (acetylation and p-coumaroylation), were inhibited by IR-. IR+ promoted the biosynthesis of malvidin 3-O-glucoside and its derivatives, and IR+ inhibited the biosynthesis of peonidin 3-O-glucoside and its derivatives. WGCNA analysis revealed that the red module positively correlated with the flavonoid pathway. The hub genes were related to the anthocyanin pathway, including VvF3'5'H, VvANS, VvOMT1, VIT_18s0001g09400, and VvGST4. Further, the results revealed that transcription factors like RLK-Pelle, MYB, and C2H2 families were involved in response to IR radiation. Therefore, these results provide a complete understanding of IR radiation in grape skin color formation and the prospect of using supplemental light to improve the overall color of berries.

Relationships between Instrumental and Sensory Quality Indices of Shine Muscat Grapes with Different Harvesting Times

The effects of instrumental quality indices on the sensory properties of Shine Muscat grapes harvested 16, 18, 20, and 22 weeks after full bloom (WAFB) were investigated. The berries harvested at 20 and 22 WAFB gained higher sweetness scores than those harvested at 16 and 18 WAFB, showing similar trends to that of total soluble solids (TSS) content. The sourness, astringency, and firmness scores were not significantly different among the samples. The flavor score showed a trend similar to that of sweetness perception. The higher flavor score in the berries harvested at 20 and 22 WAFB seemed to be derived from the development of floral aroma compounds, including linalool and its derivatives, with ripening. Consumer acceptance was highly correlated with sweetness and flavor perceptions. It was concluded that the TSS content and development of floral aroma compounds are the key quality parameters for Shine Muscat grapes, satisfying consumer acceptability in the market.

Classification of Browning on Intact Table Grape Bunches Using Near-Infrared Spectroscopy Coupled With Partial Least Squares-Discriminant Analysis and Artificial Neural Networks

Table grape browning is a complex physiological disorder that occurs during cold storage. There is a need to investigate novel and innovative ways to manage the problem that hampers the progressive and sustainable growth of table grape industries. Given the complex nature of the browning phenomenon, techniques such as near-infrared (NIR) spectroscopy can be utilized for the non-destructive classification of different browning phenotypes. In this study, NIR coupled with partial least squares discriminant analysis (PLS-DA) and artificial neural networks (ANN) were used to classify bunches as either clear or as having chocolate browning and friction browning based on the spectra obtained from intact 'Regal Seedless' table grape bunches that were cold-stored over different periods. Friction browning appears as circular spots close to the pedicel area that are formed when table grape berries move against each other, and chocolate browning appears as discoloration, which originates mostly from the stylar-end of the berry, although the whole berry may appear brown in severe instances. The evaluation of the models constructed using PLS-DA was done using the classification error rate (CER), specificity, and sensitivity and for the models constructed using ANN, the kappa score was used. The CER for chocolate browning (25%) was better than that of friction browning (46%) for weeks 3 and 4 for both class 0 (absence of browning) and class 1 (presence of browning). Both the specificity and sensitivity of class 0 and class 1 for friction browning were not as good as that of chocolate browning. With ANN, the kappa score was tested to classify table grape bunches as clear or having chocolate browning or friction browning and showed that chocolate browning could be classified with a strong agreement during weeks 3 and 4 and weeks 5 and 6 and that friction browning could be classified with a moderate agreement during weeks 3 and 4. These results open up new possibilities for the development of quality checks of packed table grape bunches before export. This has a significant impact on the table grape industry for it will now be possible to evaluate bunches non-destructively during packaging to determine the possibility of these browning types being present when reaching the export market.

Machine Vision for Ripeness Estimation in Viticulture Automation

Ripeness estimation of fruits and vegetables is a key factor for the optimization of field management and the harvesting of the desired product quality. Typical ripeness estimation involves multiple manual samplings before harvest followed by chemical analyses. Machine vision has paved the way for agricultural automation by introducing quicker, cost-effective, and non-destructive methods. This work comprehensively surveys the most recent applications of machine vision techniques for ripeness estimation. Due to the broad area of machine vision applications in agriculture, this review is limited only to the most recent techniques related to grapes. The aim of this work is to provide an overview of the state-of-the-art algorithms by covering a wide range of applications. The potential of current machine vision techniques for specific viticulture applications is also analyzed. Problems, limitations of each technique, and future trends are discussed. Moreover, the integration of machine vision algorithms in grape harvesting robots for real-time in-field maturity assessment is additionally examined.

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches-K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)-were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.