Search Results (44)

The primary purpose of this study was to improve our understanding of remote sensing technologies and their potential application in vineyards to monitor yields and fruit composition, which could then be used for selective harvesting and winemaking. For yield and berry composition data collection, representative vines from the vineyard block were selected and geolocated, and the same vines were surveyed for remote sensing data collection by the multispectral and thermal sensors in the RPAS in 2015 and 2016. The spectral reflectance data were further analyzed for vegetation indices to evaluate the correlation between the variables. Moran’s global index and map analysis were used to determine spatial clustering patterns and correlations between variables. The results of this study indicated that remote sensing data in the form of vegetation indices from the RPAS were positively correlated with yield and berry weight across sites and years. There was a positive correlation between the thermal emission and berry pH, berry phenols, and anthocyanins in certain sites and years. Overall, remote sensing technology has the potential to monitor and predict grape quality and yield, but further research on the efficacy of this data is needed for selective harvesting and winemaking. Full article

Lycium barbarum L. (goji), as an economic crop, has a high added value. However, the tender and fragile fruits are easily damaged during harvesting and transportation, leading to fruit bruising, which can cause rotting or black–brown spots after drying, seriously affecting the quality and price. In this study, two varieties of goji were used to determine and evaluate fruit bruising using a pendulum impact test, and the impact process was recorded using a high-speed camera and impact force sensor. This study discussed the energy changes during the impact process of fruits and conducted a correlation analysis of the impact energy, absorbed energy, restitution coefficient, impact force, and other indicators, analyzing the changes in each indicator with the falling height. The results showed that 0.2 m could be considered a critical height for damaging the fruit of goji. Furthermore, this study calculated the bruise susceptibility of the different varieties at different heights, which can be used for predicting bruising during the harvesting and collection of goji berries and ultimately for estimating the damage caused by mechanical harvesting. Full article

Time to maturity and yield are important traits for highbush blueberry (Vaccinium corymbosum) breeding. Proper determination of the time to maturity of blueberry varieties and breeding lines informs the harvest window, ensuring that the fruits are harvested at optimum maturity and quality. On the other hand, high-yielding crops bring in high profits per acre of planting. Harvesting and quantifying the yield for each blueberry breeding accession are labor-intensive and impractical. Instead, visual ratings as an estimation of yield are often used as a faster way to quantify the yield, which is categorical and subjective. In this study, we developed and shared a high-throughput phenotyping method using neural networks to predict blueberry time to maturity and to provide a proxy for yield, overcoming the labor constraints of obtaining high-frequency data. We aim to facilitate further research in computer vision and precision agriculture by publishing the labeled image dataset and the trained model. In this research, true-color images of blueberry bushes were collected, annotated, and used to train a deep neural network object detection model [You Only Look Once (YOLOv11)] to detect mature and immature berries. Different versions of YOLOv11 were used, including nano, small, and medium, which had similar performance, while the medium version had slightly higher metrics. The YOLOv11m model shows strong performance for the mature berry class, with a precision of 0.90 and an F1 score of 0.90. The precision and recall for detecting immature berries were 0.81 and 0.79. The model was tested on 10 blueberry bushes by hand harvesting and weighing blueberries. The results showed that the model detects approximately 25% of the berries on the bushes, and the correlation coefficients between model-detected and hand-harvested traits were 0.66, 0.86, and 0.72 for mature fruit count, immature fruit count, and mature ratio, respectively. The model applied to 91 blueberry advance selections and categorized them into groups with diverse levels of maturity and productivity using principal component analysis (PCA). These results inform the harvest window and yield of these breeding lines with precision and objectivity through berry classification and quantification. This model will be helpful for blueberry breeders, enabling more efficient selection, and for growers, helping them accurately estimate optimal harvest windows. This open-source tool can potentially enhance research capabilities and agricultural productivity. Full article

Accurately predicting grapevine yield and quality is critical for optimising vineyard management and ensuring economic viability. Numerous studies have reported the complexity in modelling grapevine yield and quality due to variability in the canopy structure, challenges in incorporating soil and microclimatic factors, and management practices throughout the growing season. The use of multimodal data and machine learning (ML) algorithms could overcome these challenges. Our study aimed to assess the potential of multimodal data (hyperspectral vegetation indices (VIs), thermal indices, and canopy state variables) and ML algorithms to predict grapevine yield components and berry composition parameters. The study was conducted during the 2019/20 and 2020/21 grapevine growing seasons in two South Australian vineyards. Hyperspectral and thermal data of the canopy were collected at several growth stages. Simultaneously, grapevine canopy state variables, including the fractional intercepted photosynthetically active radiation (fiPAR), stem water potential (Ψ_stem), leaf chlorophyll content (LCC), and leaf gas exchange, were collected. Yield components were recorded at harvest. Berry composition parameters, such as total soluble solids (TSSs), titratable acidity (TA), pH, and the maturation index (IMAD), were measured at harvest. A total of 24 hyperspectral VIs and 3 thermal indices were derived from the proximal hyperspectral and thermal data. These data, together with the canopy state variable data, were then used as inputs for the modelling. Both linear and non-linear regression models, such as ridge (RR), Bayesian ridge (BRR), random forest (RF), gradient boosting (GB), K-Nearest Neighbour (KNN), and decision trees (DTs), were employed to model grape yield components and berry composition parameters. The results indicated that the GB model consistently outperformed the other models. The GB model had the best performance for the total number of clusters per vine (R² = 0.77; RMSE = 0.56), average cluster weight (R² = 0.93; RMSE = 0.00), average berry weight (R² = 0.95; RMSE = 0.00), cluster weight (R² = 0.95; RMSE = 0.13), and average berries per bunch (R² = 0.93; RMSE = 0.83). For the yield, the RF model performed the best (R² = 0.97; RMSE = 0.55). The GB model performed the best for the TSSs (R² = 0.83; RMSE = 0.34), pH (R² = 0.93; RMSE = 0.02), and IMAD (R² = 0.88; RMSE = 0.19). However, the RF model performed best for the TA (R² = 0.83; RMSE = 0.33). Our results also revealed the top 10 predictor variables for grapevine yield components and quality parameters, namely, the canopy temperature depression, LCC, fiPAR, normalised difference infrared index, Ψ_stem, stomatal conductance (g_s), net photosynthesis (P_n), modified triangular vegetation index, modified red-edge simple ratio, and ANT_gitelson index. These predictors significantly influence the grapevine growth, berry quality, and yield. The identification of these predictors of the grapevine yield and fruit composition can assist growers in improving vineyard management decisions and ultimately increase profitability. Full article

The monitoring of grape quality parameters within viticulture using airborne remote sensing is an increasingly important aspect of precision viticulture. Airborne remote sensing allows high volumes of spatial consistent data to be collected with improved efficiency over ground-based surveys. Spectral data can be used to understand the characteristics of vineyards, including the characteristics and health of the vines. Within viticultural remote sensing, the use of cover-crop spectra for monitoring is often overlooked due to the perceived noise it generates within imagery. However, within viticulture, the cover crop is a widely used and important management tool. This study uses multispectral data acquired by a high-resolution uncrewed aerial vehicle (UAV) and Sentinel-2 MSI to explore the benefit that cover-crop pixels could have for grape yield and quality monitoring. This study was undertaken across three growing seasons in the southeast of England, at a large commercial wine producer. The site was split into a number of vineyards, with sub-blocks for different vine varieties and rootstocks. Pre-harvest multispectral UAV imagery was collected across three vineyard parcels. UAV imagery was radiometrically corrected and stitched to create orthomosaics (red, green, and near-infrared) for each vineyard and survey date. Orthomosaics were segmented into pure cover-crop_uav and pure vine_uav pixels, removing the impact that mixed pixels could have upon analysis, with three vegetation indices (VIs) constructed from the segmented imagery. Sentinel-2 Level 2a bottom of atmosphere scenes were also acquired as close to UAV surveys as possible. In parallel, the yield and quality surveys were undertaken one to two weeks prior to harvest. Laboratory refractometry was performed to determine the grape total acid, total soluble solids, alpha amino acids, and berry weight. Extreme gradient boosting (XGBoost v2.1.1) was used to determine the ability of remote sensing data to predict the grape yield and quality parameters. Results suggested that pure cover-crop_uav was a successful predictor of grape yield and quality parameters (range of R² = 0.37–0.45), with model evaluation results comparable to pure vine_uav and Sentinel-2 models. The analysis also showed that, whilst the structural similarity between the both UAV and Sentinel-2 data was high, the cover crop is the most influential spectral component within the Sentinel-2 data. This research presents novel evidence for the ability of cover-crop_uav to predict grape yield and quality. Moreover, this finding then provides a mechanism which explains the success of the Sentinel-2 modelling of grape yield and quality. For growers and wine producers, creating grape yield and quality prediction models through moderate-resolution satellite imagery would be a significant innovation. Proving more cost-effective than UAV monitoring for large vineyards, such methodologies could also act to bring substantial cost savings to vineyard management. Full article

The quality of wine depends upon the quality of the grapes, which, in turn, are affected by different viticulture aspects and the climate during the grape-growing season. Obtaining wine professionals’ judgments of the intrinsic qualities of selected wine products is a time-consuming task. It is also expensive. Instead of waiting for the wine to be produced, it is better to have an idea of the quality before harvesting, so that wine growers and wine manufacturers can use high-quality grapes. The main aim of the present study was to investigate the use of machine learning aspects in predicting Pinot Noir wine quality and to develop a pipeline which represents the major steps from vineyards to wine quality indices. This study is specifically related to Pinot Noir wines based on experiments conducted in vineyards and grapes produced from those vineyards. Climate factors and other wine production factors affect the wine quality, but our emphasis was to relate viticulture parameters to grape composition and then relate the chemical composition to quality as measured by the experts. This pipeline outputs the predicted yield, values for basic parameters of grape juice composition, values for basic parameters of the wine composition, and quality. We also found that the yield could be predicted because of input data related to the characteristics of the vineyards. Finally, through the creation of a web-based application, we investigated the balance of berry yield and wine quality. Using these tools further developed, vineyard owners should be able to predict the quality of the wine they intend to produce from their vineyards before the grapes are even harvested. Full article

Grapevine (Vitis vinifera) is one of the major economic fruit crops but suffers many diseases, causing damage to the quality of grapes. Strain G166 was isolated from the rhizosphere of grapevine and was found to exhibited broad-spectrum antagonistic activities against fungal pathogens on grapes in vitro, such as Coniella diplodiella, Botrytis cinerea, and Colletotrichum gloeosporioides. Whole-genome sequencing revealed that G166 contained a 6,613,582 bp circular chromosome with 5749 predicted coding DNA sequences and an average GC content of 60.57%. TYGS analysis revealed that G166 belongs to Pseudomonas viciae. Phenotype analysis indicated that P. viciae G166 remarkably reduced the severity of grape white rot disease in the grapevine. After inoculation with C. diplodiella, more H₂O₂ and MDA accumulated in the leaves and resulted in decreases in the Pn and chlorophyll content. Conversely, G166-treated grapevine displayed less oxidative damage with lower H₂O₂ levels and MDA contents under the pathogen treatments. Subsequently, G166-treated grapevine could sustain a normal Pn and chlorophyll content. Moreover, the application of P. viciae G166 inhibited the growth of mycelia on detached leaves and berries, while more disease symptoms occurred in non-bacterized leaves and berries. Therefore, P. viciae G166 served as a powerful bioagent against grape white rot disease. Using antiSMASH prediction and genome comparisons, a relationship between non-ribosomal peptide synthase clusters and antifungal activity was found in the genome of P. viciae G166. Taken together, P. viciae G166 shows promising antifungal potential to improve fruit quality and yield in ecological agriculture. Full article

Wine grape quality heavily influences the price received for a product. Hyperspectral imaging has the potential to provide a non-destructive technique for predicting various enological parameters. This study aims to explore the feasibility of applying hyperspectral imaging to measure the total soluble solids (TSS) and titratable acidity (TA) in wine grape berries. A normalized difference spectral index (NDSI) spectral preprocessing method was built and compared with the conventional preprocessing method: multiplicative scatter correction and Savitzky–Golay smoothing (MSC+SG). Different machine learning models were built to examine the performance of the preprocessing methods. The results show that the NDSI preprocessing method demonstrated better performance than the MSC+SG preprocessing method in different classification models, with the best model correctly classifying 93.8% of the TSS and 84.4% of the TA. In addition, the TSS can be predicted with moderate performance using support vector regression (SVR) and MSC+SG preprocessing with a root mean squared error (RMSE) of 0.523 °Brix and a coefficient of determination (R²) of 0.622, and the TA can be predicted with moderate performance using SVR and NDSI preprocessing (RMSE = 0.19%, R² = 0.525). This study demonstrates that hyperspectral imaging data and NDSI preprocessing have the potential to be a method for grading wine grapes for producing quality wines. Full article

Background: Viticulture bioclimatic indexes like the Heliothermal Index (HI), Cool Night Index (CI), and Dryness Index (DI), can be used to assess the influence of climate on grapes’ quality. Methods: HI, CI, and DI + total seasonal irrigation were utilized to assess the effect of climate on the flavonoids content and composition of two Vitis vinifera seedless varieties, ‘Summer Royal’ and ‘Crimson Seedless’, both grown in Apulia (Southern Italy). Results: The flavonoids content was significantly affected by variety and climate conditions on the base of HI, CI, and DI + total seasonal irrigation. Factor analysis applied to climate indexes and flavonoids showed that anthocyanins and flavonols were negatively and positively correlated to CI in both varieties, respectively. Additionally, warmer night temperatures determined higher fla-van-3-ols. HI increase promoted anthocyanins, flavonols, and flavan-3-ols content in Crimson Seedless, whilst it induced an opposite trend in Summer Royal. Finally, DI + total seasonal irrigation showed to be positively linked to flavonols content and negatively linked to anthocyanins content just in the case of Crimson Seedless. Significant regression models were also determined between climate indexes and productive parameters (i.e., yield, TSS, TA, pH, bunch, and berry weight). Conclusions: Climate indexes HI, CI, and DI + total seasonal irrigation showed an effect on quality grape parameters like flavonoids and contributed to building predictive models when new climatic zones are going to be evaluated for the production of table grapes. Full article

Sustainable and efficient agricultural production is a growing priority in modern society. Viticulture, an important agricultural and food sector, also faces this challenge. Precision Viticulture (PV) has gained prominence as it aims to foster high-quality, efficient, and environmentally sustainable practices. The Soluble Solids Content (SSC) is essential for assessing grape ripeness and quality in the winemaking process. Conventional methods for determining SSC values (expressed in °Brix) are invasive, expensive, and labour-intensive, necessitating sample preparation, making large-scale analysis impractical. In response to these limitations, this study presents an innovative approach within the field of Precision Viticulture. It focuses on the non-invasive prediction of SSC using low-cost proximal hyperspectral optical sensors. These sensors rely on spectral reflectance measurements in the range of 340–850 nm. This study was conducted in a commercial vineyard in the Demarcated Douro Region, Cima-Corgo sub-region, Portugal, over six weeks during ripening. In total, 169 grape berries from Touriga Nacional vines were analysed under three irrigation regimes (no irrigation, 30% ETc, and 60% ETc). After organising and preprocessing the data, machine learning algorithms, namely Partial Least Squares Regression (PLS), Random Forest (RF), and the Generalised Linear Model (GLM), were applied to predict SSC values. These models’ performance was thoroughly evaluated using cross-validation techniques. The performance of different models was evaluated, showing significant differences according to the metrics used (R2, RMSE, and MAPE). The RF model demonstrated effectiveness and precision. A high R² value of 0.9312, coupled with low RMSE (0.9199 °Brix) and MAPE (3.88%), signifies a strong fit to the data and accurate predictive capabilities. The results of this benchmarking study on predictive models of SSC provide valuable insights into the performance of various models, aiding winegrowers and winemakers in decision making. Full article

Blackberries are a valuable crop that has a positive effect on human health due to its fruits’ antioxidant and antihyperglycemic properties. The main goal of the research was to compare the fruit parameters of modern blackberry varieties. The experiment involved six varieties of blackberries: ‘Natchez’, ‘Loch Tay’, ‘Brzezina’, ‘Black Gem’, ‘Heaven Can Wait’, and ‘Ouachita’. The data were collected in 2021–2022 in the Rostov region of Russia. On one hectare, 3000 plants with trellises were planted. To prevent winter damage, the plants were covered for the winter period with a non-woven covering material with a density of 60 g/m². To assess the quality of the fruits, harvesting was carried out from seven to nine in the morning once every 5–6 days. The results showed that the most productive varieties are the ‘Loch Tay’ (4.8 kg/bush), ‘Black Gem’ (4.2 kg/bush), ‘Heaven Can Wait’ (3.9 kg/bush), and ‘Ouachita’ (3.8 kg/bush) varieties. The heaviest fruits are as follows: ‘Natchez’ (13.3 g), ‘Black Gem’ (11.2 g), and ‘Ouachita’ (10.3 g). The varieties with the highest amount of sugar are the following: ‘Black Gem’ (14.7 Brix), ‘Ouachita’ (13.4 Brix), ‘Loch Tay’ (12.9 Brix), and ‘Heaven Can Wait’ (11.6 Brix). In terms of the combination of the parameters, the most promising varieties for industrial production in this region are the ‘Ouachita’, ‘Black Gem’, ‘Loch Tay’, and ‘Heaven Can Wait’ varieties. Medium and strong relationships were established between the parameters of the fruits and the elemental composition of the leaves. During the study, we also developed systems of equations for predicting the parameters of a berry based on the content of one or another macro and microelement of a leaf; these systems are suitable for both the manual calculations in nurseries and the correcting of programs for the automated determination of fruit quality and the calculation of productivity in large industrial farms. The obtained data will make it possible to increase the blackberry production area in Russia from 4.34% to 22.06% in various growing regions. Full article

Coffee is a relevant agricultural product in the global economy, with the amount and quality of the bean being seriously affected by the coffee berry borer Hypothenemus hampei (Ferrari), CBB, its principal pest. One of the ways to deal with this beetle is through biological control agents, like ants (Hymenoptera: Formicidae), some of which are characterized by naturally inhabiting coffee plantations and feeding on CBB in all their life stages. Our paper considers a predator–prey interaction between these two insects through a novel mathematical model based on ordinary differential equations, where the state variables correspond to adult CBBs, immature CBBs, and ants from one species, without specifying whether preying on the CBB is among their feeding habits, in both adult and immature stages. Through this new mathematical model, we could qualitatively predict the different dynamics present in the system as some meaningful parameters were varied, filling the existing gap in the literature and envisioning ways to manage pests. Mathematically, the system’s equilibrium points were determined, and its stability was studied through qualitative theory. Bifurcation theory and numerical simulations were applied to illustrate the stability of the results, which were interpreted as conditions of the coexistence of the species, as well as conditions for eradicating the pest, at least theoretically, through biocontrol action in combination with other actions focused on eliminating only adult CBBs. Full article

An automatic determination of grape must ingredients during the harvesting process would support cellar logistics and enables an early termination of the harvest if quality parameters are not met. One of the most important quality-determining characteristics of grape must is its sugar and acid content. Among others, the sugars in particular determine the quality of the must and wine. Chiefly in wine cooperatives, in which a third of all German winegrowers are organized, these quality characteristics serve as the basis for payment. They are acquired upon delivery at the cellar of the cooperative or the winery and result in the acceptance or rejection of grapes and must. The whole process is very time-consuming and expensive, and sometimes grapes that do not meet the quality requirements for sweetness, acidity, or healthiness are destroyed or not used at all, which leads to economic loss. Near-infrared spectroscopy is now a widely used technique to detect a wide variety of ingredients in biological samples. In this study, a miniaturized semi-automated prototype apparatus with a near-infrared sensor and a flow cell was used to acquire spectra (1100 nm to 1350 nm) of grape must at defined temperatures. Data of must samples from four different red and white Vitis vinifera (L.) varieties were recorded throughout the whole growing season of 2021 in Rhineland Palatinate, Germany. Each sample consisted of 100 randomly sampled berries from the entire vineyard. The contents of the main sugars (glucose and fructose) and acids (malic acid and tartaric acid) were determined with high-performance liquid chromatography. Chemometric methods, using partial least-square regression and leave-one-out cross-validation, provided good estimates of both sugars (RMSEP = 6.06 g/L, R² = 89.26%), as well as malic acid (RMSEP = 1.22 g/L, R² = 91.10%). The coefficient of determination (R²) was comparable for glucose and fructose with 89.45% compared to 89.08%, respectively. Although tartaric acid was predictable for only two of the four varieties using near-infrared spectroscopy, calibration and validation for malic acid were accurate for all varieties in an equal extent like the sugars. These high prediction accuracies for the main quality determining grape must ingredients using this miniaturized prototype apparatus might enable an installation on a grape harvester in the future. Full article

The variability in grape ripening is associated with the fact that each grape berry undergoes its own biochemical processes. Traditional viticulture manages this by averaging the physicochemical values of hundreds of grapes to make decisions. However, to obtain accurate results it is necessary to evaluate the different sources of variability, so exhaustive sampling is essential. In this article, the factors “grape maturity over time” and “position of the grape” (both in the grapevine and in the bunch/cluster) were considered and studied by analyzing the grapes with a portable ATR-FTIR instrument and evaluating the spectra obtained with ANOVA–simultaneous component analysis (ASCA). Ripeness over time was the main factor affecting the characteristics of the grapes. Position in the vine and in the bunch (in that order) were also significantly important, and their effect on the grapes evolves over time. In addition, it was also possible to predict basic oenological parameters (TSS and pH with errors of 0.3 °Brix and 0.7, respectively). Finally, a quality control chart was built based on the spectra obtained in the optimal state of ripening, which could be used to decide which grapes are suitable for harvest. Full article

Fruit quality attributes interrelate with their dielectric properties, but such interrelationships in sea buckthorn berries under differing freezing regimes remain uninvestigated. Sea buckthorn (Hipophae rhamnoides L., cv. Shenqiuhong) berries were frozen at different temperatures (−13, −30, −35 and −40 °C) and stored for different periods (15, 30, 45, 60, 75 and 90 d). Seven quality attributes and nine dielectric parameters were measured to evaluate the effect of different frozen storage regimes on those attributes and parameters. The results showed that shorter time and lower temperature contributed to the preservation of berries quality. The dielectric parameters values increased with decreasing temperature and with the increase of freezing duration. The quality prediction models were established by the principal component analysis of the dielectric properties at characteristic frequency. The results are expected to provide a way to evaluate quality of frozen sea buckthorn berries by dielectric properties. Full article