AgriEngineering

Strawberries are highly perishable fruits prone to postharvest losses due to defects, diseases, and uneven ripening. This study proposes a deep learning-based approach for automated quality assessment using the YOLOv8n object detection model. A custom dataset of 5663 annotated strawberry images was compiled, covering eight quality categories, including anthracnose, gray mold, powdery mildew, uneven ripening, and physical defects. Data augmentation techniques, such as rotation and Gaussian blur, were applied to enhance model generalization and robustness. The model was trained over 100 and 200 epochs, and its performance was evaluated using standard metrics: Precision, Recall, and mean Average Precision (mAP). The 200-epoch model achieved the best results, with a mAP50 of 0.79 and an inference time of 1 ms per image, demonstrating suitability for real-time applications. Classes with distinct visual features, such as anthracnose and gray mold, were accurately classified. In contrast, visually similar categories, such as ‘Good Quality’ and ‘Unripe’ strawberries, presented classification challenges. Full article

In this study, we evaluated the spatial variability in droplet deposition in herbicide applications using a remotely piloted aircraft (RPA) in pasture areas. The investigation was conducted in a square grid (50.0 m × 50.0 m), with 121 sampling points, at two operational flight heights (3.0 and 4.0 m). Droplet deposition was quantified using the fluorescent dye rhodamine B, and the droplet spectrum was characterised using water-sensitive paper tags. Geostatistical analysis was implemented to characterise spatial dependence, complemented by multivariate statistical analysis. Droplet deposition ranged from 1.01 to 9.02 and 1.10–6.10 μL cm⁻² at 3.0 and 4.0 m flight heights, respectively, with the coefficients of variation between 19.72 and 23.06% for droplet spectrum parameters. All droplet spectrum parameters exhibited a moderate to strong spatial dependence (relative nugget effect ≤75%) and a predominance of adjustment to the exponential model, with spatial dependence indices ranging from 12.55 to 47.49% between the two flight heights. Significant positive correlations were observed between droplet deposition and droplet spectrum parameters (r = 0.60–0.79 at 3.0 m; r = 0.37–0.66 at 4.0 m), with the correlation magnitude decreasing as the operational flight height increased. Cross-validation indices demonstrated acceptable accuracy in spatial prediction, with a mean estimation error ranging from −0.030 to 0.044 and a root mean square error ranging from 0.81 to 2.25 across parameters and flight heights. Principal component analysis explained 99.14 and 85.72% of the total variation at 3.0 and 4.0 m flight heights, respectively. The methodological integration of geostatistics and multivariate statistics provides a comprehensive understanding of the spatial variability in droplet deposition, with relevant implications for the optimisation of phytosanitary applications performed using RPAs. Full article

In response to the need for separation and utilization of residual film mixtures after transformation of protaetia brevitarsis larvae, a pneumatic drum-sieve-type separator for transforming mixtures of protaetia brevitarsis larvae was designed. First, the suspension velocity of each component was determined by the suspension speed test. Secondly, the separation process of residual film, larvae, and insect sand was formulated on the basis of biological activities, shape differences, and aerodynamic response characteristics. Eventually, the main structural parameters and working parameters of the machine were determined. In order to optimize the separation effect, a single-factor experiment and a quadratic regression response surface experiment containing three factors and three levels were carried out, and the corresponding regression model was established. The experimental results showed that the effects of the air speed at the inlet, inclination angle of the sieve cylinder, and rotational speed of the sieve cylinder on the impurity rate of the residual film decreased in that order, and that the effects of the rotational speed of the sieve cylinder, inclination angle of the sieve cylinder, and air speed at the inlet on the inactivation rate of the larvae decreased in that order. Through parameter optimization, a better combination of working parameters was obtained: the rotational speed of the sieve cylinder was 24 r/min, the inclination angle of the sieve cylinder was −0.43°, and the air speed at the inlet was 5.32 m/s. The average values of residual film impurity rate and larval inactivation rate obtained from the material sieving test under these parameters were 8.74% and 3.18%, with the relative errors of the theoretically optimized values being less than 5%. The results of the study can provide a reference for the resource utilization of residual film and impurity mixtures and the development of equipment for the living body separation of protaetia brevitarsis. Full article

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

Accurate body condition score (BCS) monitoring in dairy cows is essential for optimizing health, productivity, and welfare. Traditional manual scoring methods are labor-intensive and subjective, driving interest in automated imaging-based systems. This study evaluated the effectiveness of 2D imaging and deep learning for BCS classification using three camera perspectives—front, back, and top-down—to identify the most reliable viewpoint. The research involved 56 Norwegian Red milking cows at the Center for Livestock Experiments (SHF) of Norges Miljo-og Biovitenskaplige Universitet (NMBU) in Norway. Images were classified into BCS categories of 2.5, 3.0, and 3.5 using a YOLOv8 model. The back view achieved the highest classification precision (mAP@0.5 = 0.439), confirming that key morphological features for BCS assessment are best captured from this angle. Challenges included misclassification due to overlapping features, especially in Class 2.5 and background data. The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. Integration with precision farming tools enables continuous monitoring and early detection of health issues. This research highlights the potential of 2D imaging as a cost-effective alternative to 3D systems, particularly for small and medium-sized farms, supporting more effective herd management and improved animal welfare. Full article

This research presents a comprehensive methodology for calibrating Discrete Element Method (DEM) parameters governing rice grain interactions with biodegradable Polylactic Acid (PLA) components in agricultural bucket elevator systems. Rice grains, a critical global food staple requiring efficient post-harvest handling, were modeled as three-sphere clusters to accurately represent their physical dimensions (6.5 mm length), while the Hertz–Mindlin contact model provided the theoretical framework for particle interactions. The calibration process employed a multi-phase experimental design integrating Plackett–Burmann screening, steepest ascent method, and Face Central Composite Design to systematically identify and optimize critical micro-mechanical parameters for agricultural material handling. Statistical analysis revealed the coefficient of static friction between rice and PLA as the dominant factor, contributing 96.49% to system performance—significantly higher than previously recognized in conventional agricultural processing designs. Response Surface Methodology generated predictive models achieving over 90% correlation with experimental results from 3D-printed PLA shear box tests. Validation through comparative velocity profile analysis during bucket elevator discharge operations confirmed excellent agreement between simulated and experimental behavior despite a 20% discharge velocity variance that warrants further investigation into agricultural material-specific phenomena. The established parameter set enables accurate virtual prototyping of sustainable agricultural handling equipment, offering post-harvest processing engineers a powerful tool for optimizing bulk material handling systems with reduced environmental impact. This integrated approach bridges fundamental agricultural material properties with sustainable engineering design principles, providing a scalable framework applicable across multiple agricultural processing operations using biodegradable components. Full article

The aim of the research was to reduce the irregularity of mineral fertilizer granule flow by developing a tedder-vaulting breaker that prevents the formation of vaults over the sowing windows of the seeder hopper. Existing dosing devices for mineral fertilizers do not provide stable application of the required doses of mineral fertilizers due to vaulting as well as accumulation and sticking of fertilizers in hoppers. In order to achieve a stable and precise metering of high fertilizer doses, a crank tedder is suggested to be mounted inside the hopper. Its function is to break the constantly appearing dynamic vaults above the sowing windows and to crush the fertilizer clods, i.e., to provide the fertilizer sowing units with a continuous flow of material. Theoretical studies were carried out using methods of classical and applied mechanics, special sections of higher mathematics. The following optimal parameters were established: the tedder blade width 0.05–0.09 m, the radius of the elbow 0.028–0.034 m, the blade installation angle 23–27°, and the kinematic mode of the tedder k = 1.5–1.9. Experimental studies have shown that the use of a crank tedder provides a stable flow of mineral fertilizer granules through sowing windows and reduces the sowing unevenness between seeding units by 12–15% and sowing instability by 7–10%. At the same time, the degree of damage to granules of 1–5 mm size is insignificant and is within 2.8–3.5%. Full article

Monitoring the growth status of crop leaves is an integral part of agricultural management and involves important tasks such as leaf shape analysis and area calculation. To achieve this goal, accurate leaf segmentation is a critical step. However, this task presents a challenge, as crop leaf images often feature substantial overlap, obstructing the precise differentiation of individual leaf edges. Moreover, existing segmentation methods fail to preserve fine edge details, a deficiency that compromises precise morphological analysis. To overcome these challenges, we introduce MSFUnet, an innovative network for semantic segmentation. MSFUnet integrates a multi-path feature fusion (MFF) mechanism and an edge-detail focus (EDF) module. The MFF module integrates multi-scale features to improve the model’s capacity for distinguishing overlapping leaf areas, while the EDF module employs extended convolution to accurately capture fine edge details. Collectively, these modules enable MSFUnet to achieve high-precision individual leaf segmentation. In addition, standard image augmentations (e.g., contrast/brightness adjustments) were applied to mitigate the impact of variable lighting conditions on leaf appearance in the input images, thereby improving model robustness. Experimental results indicate that MSFUnet attains an MIoU of 93.35%, outperforming conventional segmentation methods and highlighting its effectiveness in crop leaf growth monitoring. Full article

Previous work used machine learning (ML) to estimate past and current alfalfa yields and showed that domain adaptation (DA) with data synthesis shows promise in classifying yields as high, medium, or low. The current work uses similar techniques to forecast future alfalfa yields. A novel technique is proposed for forecasting alfalfa time series data that exploits stationarity and predicts differences in yields rather than the yields themselves. This forecasting technique generally provides more accurate forecasts than the established ARIMA family of forecasters for both univariate and multivariate time series. Furthermore, this ML-based technique is potentially easier to use than the ARIMA family of models. Also, previous work is extended by showing that DA with data synthesis also works well for predicting continuous values, not just for classification. The novel scale-invariant tabular synthesizer (SITS) is proposed, and it is competitive with or superior to other established synthesizers in producing data that trains strong models. This synthesis algorithm leads to R scores over 100% higher than an established synthesizer in this domain, while ML-based forecasters beat the ARIMA family with symmetric mean absolute percent error (sMAPE) scores as low as 12.81%. Finally, ML-based forecasting is combined with DA (ForDA) to create a novel pipeline that improves forecast accuracy with sMAPE scores as low as 9.81%. As alfalfa is crucial to the global food supply, and as climate change creates challenges with managing alfalfa, this work hopes to help address those challenges and contribute to the field of ML. Full article

This study presents an intelligent method for evaluating the risk of Asian rust (Phakopsora pachyrhizi) based on its development stage in soybean crops (Glycine max (L.) Merrill). It has been designed using smart computer systems supported by image processing, environmental sensor data, and an embedded model for evaluating favorable conditions for disease progression within crop areas. The approach also includes the use of machine learning techniques and a Markov chain algorithm for data fusion, aimed at supporting decision-making in agricultural management. Rules derived from time-series data are employed to enable scenario prediction for risk evaluation related to disease development. Measured data are stored in a customized system designed to support virtual monitoring, facilitating the evaluation of disease severity stages by farmers and enabling timely management actions. Full article

This paper presents, within an arable farming context, a predictive logic for the on- and off-switching of a set of nozzles. The predictive logic is tailored to a specific path planning pattern. The nozzles are assumed to be attached to a boom aligned along a working width and carried by a piece of machinery with the purpose of applying spray along the working width. The machinery is assumed to be travelling along the specific path planning pattern. The concatenation of multiple path patterns and the corresponding concatenation of the proposed switching logics enable nominal lossless spray application for area coverage tasks. The proposed predictive switching logic is compared to the common and state-of-the-art reactive switching logic for Boustrophedon-based path planning for area coverage. The trade-off between a reduction in pathlength and increase in the number of required on- and off-switchings for the proposed method is discussed. Full article

Increasing the shelf-life of fruits and vegetables using edible natural substances after harvest is economically important and can be useful for human health. Postharvest techniques help maintain the quality of edible tissues resulting in extended marketing periods and reduced food waste. The edible coating on perishable commodities is a common technique used by the food industry during the postharvest supply chain. The objective of this research was to study the effect of edible oil to minimize the loss of postharvest physio-chemical and nutritional attributes of bananas. The study selected two banana cultivars (Musa, ‘Cavendish’ and ‘Milk’) to conduct this experiment, and two edible oils (olive oil (Olea europaea) and moringa oil (Moringa peregrina)) were applied as an edible coating under two different storage conditions (15 and 25 °C). The fruit’s physio-chemical properties including weight loss, firmness, color, total soluble solids (TSS), pH, titratable acidity (TA), TSS: TA ratio, and mineral content were assessed. The experiment lasted for 12 days. The physicochemical properties of the banana coated with olive and moringa oils were more controlled than the non-coated (control) banana under both storage temperatures (15 °C and 25 °C). Coated bananas with olive and moringa oils stored at 15 °C resulted in further inhibition in the ripening process. There was a decrease in weight loss, retained color, and firmness, and the changes in chemical parameters were slower in banana fruits during storage in the olive and moringa oil-coated bananas. Minerals were highly retained in coated Cavendish bananas. Overall, the coated samples visually maintained acceptable quality until the final day of storage. Our results indicated that olive and moringa oils in this study have the potential to extend the shelf-life and improve the physico-chemical quality of banana fruits. Full article

The identification and prediction of losses, along with environmental and behavioral analyses and animal welfare monitoring, are key drivers for the use of technologies in poultry farming which help characterize the productive environment. Among these technologies, robotics emerges as a facilitator as it provides space for the use of several computing tools for capture, analysis and prediction. This study presents the full methodology for building a robot (so called RobôFrango) to its application in poultry farming. The construction method was based on evolutionary prototyping that allowed knowing and testing each physical component (electronic and mechanical) for assembling the robotic structure. This approach made it possible to identify the most suitable components for the broiler production system. The results presented motors, wheels, chassis, batteries and sensors that proved to be the most adaptable to the adversities existing in poultry farms. Validation of the final constructed structure was carried out through practical execution of the robot, seeking to understand how each component behaved in a commercial broiler aviary. It was concluded that it was possible to identify the best electronic and physical equipment for building a robotic prototype to work in poultry farms, and that a final product was generated. Full article

Accurate detection of cotton pests and diseases is essential for agricultural productivity yet remains challenging due to complex field environments, the small size of pests and diseases, and significant occlusions. To address the challenges presented by these factors, a novel cotton disease and pest detection method is proposed. This method builds upon the YOLOv8 baseline model and incorporates a Multi-Scale Sliding Window Attention Module (MSFE) within the backbone architecture to enhance feature extraction capabilities specifically for small targets. Furthermore, a Depth-Separable Dilated Convolution Module (C2f-DWR) is designed to replace the existing C2f module in the neck of the network. By employing varying dilation rates, this modification effectively expands the receptive field and alleviates the loss of detailed information associated with the downsampling processes. In addition, a Multi-Head Attention Detection Head (MultiSEAMDetect) is introduced to supplant the original detection head. This new head utilizes diverse patch sizes alongside adaptive average pooling mechanisms, thereby enabling the model to adjust its responses in accordance with varying contextual scenarios, which significantly enhances its ability to manage occlusion during detection. For the purpose of experimental validation, a dedicated dataset for cotton disease and pest detection was developed. In this dataset, the improved model’s mAP50 and mAP50:95 increased from 73.4% and 46.2% to 77.2% and 48.6%, respectively, compared to the original YOLOv8 algorithm. Validation on two Kaggle datasets showed that mAP50 rose from 92.1% and 97.6% to 93.2% and 97.9%, respectively. Meanwhile, mAP50:95 improved from 86% and 92.5% to 87.1% and 93.5%. These findings provide compelling evidence of the superiority of the proposed algorithm. Compared to other advanced mainstream algorithms, it exhibits higher accuracy and recall, indicating that the improved algorithm performs better in the task of cotton pest and disease detection. Full article

Heat stress has been identified as one of the main challenges for dairy production systems, particularly in the context of global warming. This one-year study aimed to evaluate the impact of heat stress on milk yield and composition in a dairy farm located in the Elvas region of Portugal. A pack of electronic sensors was installed in the lactating animal facilities, allowing continuous recording of environmental data (temperature, humidity, ammonia and carbon dioxide). Based on these data, the Temperature-Humidity Index (THI) was automatically calculated on a daily basis, with the values subsequently aggregated into 7-day moving averages and integrated with milk production records, somatic cell count, and milk fat and protein content. The results indicate a significant influence of THI on both milk yield and composition, particularly on protein and fat content. The relationships between the variables were found to be non-linear, which contrasts with some results described in the literature. These discrepancies may be related to genetic differences between animals, variations in diets, production levels, management conditions, or the statistical models used in previous studies. Dry matter intake proved to be an important predictive variable. These findings reinforce the importance of ensuring animal welfare through continuous environmental monitoring and the implementation of effective heat stress mitigation strategies in the dairy sector. Full article

Agriculture faces a dual challenge in the context of climate change, serving as both a significant contributor to greenhouse gas (GHG) emissions and a sector highly vulnerable to its impacts. Addressing this requires a transition toward climate-resilient agriculture (CRA). Emerging technologies, including geospatial tools (e.g., Geographic Information Systems (GISs) and remote sensing (RS)), as well as artificial intelligence (AI), offer promising methods to support this transition. However, their individual capabilities, limitations, and appropriate applications are not always well understood or clearly delineated in the literature. A common issue is the frequent overlap between GISs and RS, with many studies assessing GIS contributions while concurrently employing RS techniques, without explicitly distinguishing between the two (or vice versa). In this sense, the objective of this review is to conduct a critical analysis of the existing state of the art in terms of the distinct roles, limitations, and complementarities of GISs, RS, and AI in advancing CRA, guided by an original definition we propose for CRA (structured around three key dimensions and their corresponding targets). Furthermore, this review introduces a synthesis matrix that integrates both the individual contributions and the synergistic potential of these technologies. This synergy-focused matrix offers not just a summary, but a practical decision support matrix that could be used by researchers, practitioners, and policymakers in selecting the most appropriate technological configuration for their objectives in CRA-related work. Such support is increasingly needed, especially considering that RS and AI have experienced exponential growth in the past five years, while GISs, despite being the more established “big brother” among these technologies, remain underutilized and is often insufficiently understood in agricultural applications. Full article

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

There are many products obtained from grapes. The early detection of diseases in an economically important fruit is important, and the spread of disease significantly increases financial losses. In recent years, it is known that artificial intelligence techniques have achieved very successful results in image classification. Therefore, the early detection and classification of grape diseases with the latest artificial intelligence techniques and feature reduction techniques was carried out within the scope of this study. The most well-known convolutional neural network (CNN) architectures, texture-based Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods, Neighborhood Component Analysis (NCA), feature reduction methods, and machine learning (ML) techniques are the methods used in this article. The proposed hybrid model was compared with two texture-based and four CNN models. The features from the most successful CNN model and texture-based architectures were combined. The NCA method was used to select the best features from the obtained feature map, and the model was classified using the best-known ML classifiers. Our proposed model achieved an accuracy value of 99.1%. This value shows that our model can be used in the detection of grape diseases. Full article