AgriEngineering

The design and technological scheme of a small-sized forage harvester with a capture width of 1.35 m equipped with a device oriented along the length of the stems was developed in this study. As a result of theoretical studies, the process of the movement of mass into the chamber of the mowing rotor due to centrifugal forces was revealed. The speed of mass movement and the average size of crushed particles with the mowing rotor were determined. The oriented feeding process of stems in the chamber of the chopping rotor is mathematically described in this paper. An analytical expression is obtained for determining the average size of crushed particles by the forage harvester, that is, a mathematical model of the processes of mowing, oriented feeding, and the chopping of stem fodder by the forage harvester. Laboratory and field tests of a forage harvester equipped with a device oriented along the length of the stems were conducted. The combine harvester’s productivity was 6.14 t/h when mowing alfalfa. Special experiments were conducted to determine the average size of crushed particles after the mowing rotor. The average size of crushed particles with the mowing rotor was 147.4 mm, while the theoretical value was 144 mm. The difference between these values was only 2.31%. A special experiment was conducted on the combine without an orienting device to compare the quality indicators. The mass fractions of crushed particles of up to 50 mm in length when the combine was operating with and without an orienting device were 79.3 and 46.7, respectively. Accordingly, the average length of crushed particles was 33.79 mm and that without an orienting device was 77.07 mm. The theoretical value of the average length of crushed particles was 34.9 mm (i.e., the difference between the theoretical and actual value of the average size of the crushed particles was only 3.25%). All this proves that when the combine harvester was operated with an orienting device, there was a significant increase in the quality indicators of the chopped feed, and the reliability of the theoretical studies and the resulting mathematical model were determined. Full article

The use of summarized spectral data in bands obtained by hyperspectral sensors can make it possible to obtain biochemical information about seeds and, thus, relate the results to seed viability and vigor. Thus, the hypothesis of this work is based on the possibility of obtaining information about the physiological quality of seeds through hyperspectral bands and distinguishing seed lots regarding their quality through wavelengths. The objective was then to evaluate the possibility of differentiating soybean genotypes regarding the physiological quality of seeds using spectral data. The experiment was conducted during the 2021/2022 harvest at the Federal University of Mato Grosso do Sul in a randomized block design with four replicates and 10 F3 soybean populations (G1, G8, G12, G15, G19, G21, G24, G27, G31, and G36). After the maturation of each genotype, seeds were harvested from the central rows of each plot, which consisted of five one-meter rows. Seed samples from each experimental unit were placed in a Petri dish to collect spectral data. Readings were performed in the laboratory at a temperature of 26 °C and using two 60 W halogen lamps as the light source, positioned 15 cm between the sensor and the sample. The sensor used was the Ocean Optics (Florida, USA) model STS-VIS-L-50-400-SMA, which captured the reflectance of the seed sample at wavelengths between 450 and 824 nm. After readings from the hyperspectral sensor, the seeds were subjected to tests for water content, germination, first germination count, electrical conductivity, and tetrazolium. The data obtained were subjected to an analysis of variance and the means were compared by the Scott–Knott test at 5% probability, analyzed using R software version 4.2.3 (Auckland, New Zealand). The data on the physiological quality of the seeds of the soybean genotypes were subjected to principal component analysis (PCA) and associated with the K-means algorithm to form groups according to the similarity and distinction between the genetic materials. After the formation of these groups, spectral curve graphs were constructed for each soybean genotype and for the groups that were formed. The physiological quality of the soybean genotypes can be differentiated using hyperspectral bands. The spectral bands, therefore, provide important information about the physiological quality of soybean seeds. Through the use of hyperspectral sensors and the observation of specific bands, it is possible to differentiate genotypes in terms of seed quality, complementing and/or replacing traditional tests in a fast, accurate, and non-destructive way, reducing the time and investment spent on obtaining information on seed viability and vigor. The results found in this study are promising, and further research is needed in future studies with other species and genotypes. The interval between 450 and 649 nm was the main spectrum band that contributed to the differentiation between soybean genotypes of superior and inferior physiological quality. Full article

Stocking density (SD) may affect broiler productivity and welfare. This study investigated the performance and welfare of Ross 708 and Cobb 700 broilers as affected by four SDs (27, 29, 32, and 44 kg/m²) until day 56. A total of 432 birds per strain were used, with 10, 12, 14, and 18 birds per pen (1.1 m × 1.5 m), corresponding to the respective SDs. Each SD treatment comprised eight replicates. The target SD was determined based on the projected market weight of 4 kg at 56 days of age. The average body weight (BW), feed intake, and feed conversion ratio (FCR) were measured biweekly. Welfare indicators (four broilers per pen), including gait score, feather cleanliness, feather coverage, body temperature, and footpad condition, were evaluated on days 28 and 56. Tibia strength (two broilers per pen) was measured on day 56. The results show that the BW and FCR of both broiler strains were not affected by SD. For both strains, the male broilers exhibited greater bone strength compared to that of the female broilers (129.06 lbf M vs. 91.70 lbf F for Ross, and 130.86 lbf M vs. 117.40 lbf F for Cobb), but the influence of SD on bone strength was found to be significant only for the Ross male broilers. Most welfare indicators were not affected by the SD on days 28 and 56 for either broiler strain, except for feather cleanliness in Ross broilers and footpad in Cobb broilers on day 56, which improved at lower SDs. Strong age and sex effects on the welfare indicators were also identified for both broiler strains. It was concluded that the SD is not a significant factor for broiler productivity, and it has a minor influence on broiler welfare compared to those of age and sex. Full article

Rice is a water-intensive crop, conventionally grown under submerged conditions, with standing water for about 80% of its growth period. There is an urgent need for water-saving technologies to address challenges associated with conventional irrigation techniques for rice. The alternate wetting and drying (AWD) technique is one of these water-saving techniques; however, it requires continuous monitoring of water levels in the field. The implementation of real-time, electronic sensor-based precision irrigation technology may address the problems associated with conventional irrigation systems and AWD leading to high water use efficiency. Therefore, a study was undertaken to develop a suitable sensor-based automated irrigation system to maintain optimal water levels in rice fields. This study conceptualized an electronic sensor-based automated irrigation system for rice cultivated under the AWD technique. In this method, the rice field is initially flooded to a maximum depth of 5 cm. Irrigation is reapplied once the water level reduces to 10 cm below the soil surface. This developed system helps address water scarcity by regulating water levels, preventing excess ponding. It uses magnetic float-based sensors and electronic circuits to detect water levels, converting them into electronic signals transmitted wirelessly via radio frequency (RF) to a controller. The controller has been programmed for different growth stages that need to be set manually during the cropping period. The system is designed primarily for the AWD method but includes an option for continuous ponding (CP), needed during the flowering stage. The maximum water level at full maturity is set at 5 cm above the soil surface, while irrigation with the AWD method begins when the water level falls 10 cm below the soil surface. The developed system was tested during the Kharif season of 2018–19; the irrigation water productivity was 6.15 kg ha⁻¹mm⁻¹ with the automated system, compared to 3.06 kg ha⁻¹mm⁻¹ in the control (continuous ponding). Total water productivity was 4.80 kg ha⁻¹mm⁻¹ for the automated system and 2.63 kg ha⁻¹mm⁻¹ for the control. The automated system achieved 36% more water savings over the control, which used continuous ponding as farmers practice. The developed system supports AWD, a proven water-saving technique in rice cultivation. Full article

Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles (UAVs). Drone-based images were captured during the late growth stage of wheat across 15 fields in Bosnia and Herzegovina. The images, processed to a resolution of 1024 × 1024 pixels, were manually annotated with regions of interest (ROIs) containing wheat ears. A dataset consisting of 556 high-resolution images was compiled, and advanced models including Faster R-CNN, YOLOv8, and RT-DETR were utilised for ear detection. The study found that although lower-quality images had a minor effect on detection accuracy, they did not significantly hinder the overall performance of the models. This research demonstrates the potential of digital technologies, particularly machine learning and UAVs, in transforming traditional agricultural practices. The novel application of automated ear counting via machine learning provides a scalable, efficient solution for yield prediction, enhancing sustainability and competitiveness in agriculture. Full article

Currently, intelligent pest monitoring systems transmit entire monitoring images to cloud servers for analysis. This approach not only consumes significant bandwidth and increases monitoring costs, but also struggles with accurately recognizing small-target and overlapping pests. To overcome these challenges, this paper introduces a two-stage multi-feature fusion small-target pest detection algorithm based on edge computing devices, termed TFEMRNet. The algorithm initially conducts semantic segmentation on an edge processor, followed by uploading the segmented images to a cloud server for target identification. Specifically, the semantic segmentation model TFENet incorporates a Multi-Attention Channel Aggregation (MACA) module, which integrates semantic features from EfficientNet-Pest and Swin Transformer, thereby enhancing the model’s ability to extract features of small-target pests. Experimental results demonstrate that TFEMRNet surpasses models such as YOLOv11, Fast R-CNN, and Mask R-CNN on small-target pest datasets, achieving precision of 96.75%, recall of 96.45%, and an F1 score of 95.60%. Notably, the TFENet model within TFEMRNet attains an IoU of 91.63% in semantic segmentation accuracy, outperforming other segmentation models such as U-Net and PSPNet. These findings affirm TFEMRNet’s superior efficacy in small-target pest detection, offering an effective solution for agricultural pest monitoring. Full article

Several years have passed since the linear economy model proved unsustainable, leading to the transition toward the circular economy (CE) model. Significant amounts of agricultural residues and waste from livestock farming units remain unutilized in fields. The anaerobic digestion (AD) method addresses this issue by generating energy in the form of thermal (TE) and electrical energy (EE). This article examines greenhouse heating using thermal energy from a biogas plant. For this purpose, a thermal load model is developed and applied in two regions, northern (Florina) and central Greece (Trikala), to assess the greenhouse’s energy requirements in areas with differing characteristics, especially during the winter months. Additionally, the economic benefits of a biogas plant from selling electricity to the grid are analyzed. Thermal energy constitutes 59.7% of the system’s total energy output. On average, the generated electrical energy amounts to 518 MW h per month, while thermal energy reaches 770 MW h per month. The biogas plant’s daily electricity consumption ranges from 1564 kW h to 2173 kW h, depending on its needs. Ambient temperatures vary between 0 °C and 37 °C, significantly influencing the greenhouse heating system’s efficiency. The biogas plant also demonstrates financial profitability, earning 504,549 € annually from the sale of surplus electricity. Furthermore, the article explores greenhouse crops in the broader Thessaly region, where tomato cultivation seems to be dominant. Greenhouse heating requirements depend on crop type, location, weather conditions, sunlight exposure, and heat loss based on covering materials. Meanwhile, the thermal energy output that can heat a given greenhouse area is directly proportional to the biogas plant’s capacity. Full article

This paper presents a banana ripening chamber system for Romania. The system comprises two main parts: the refrigerating unit, with a cooling capacity of 47.5 kW, and a fresh air supply system for ethylene exhaust during the ripening process (1000 m³/h). The proposed solution replaces the classical one-stage vapor compression with R134a. The new solution presented in this study has a proven fruit ripening solution that includes the 3Es; it is eco-friendly (low GWP refrigerant R1234ze(E)), economical, and energy efficient (AHU with heat recovery). The advantage of the new system results from an increasing coefficient of performance, with 7.34% owing to decreasing the power consumption of the compressors. Regarding heat recovery, the annual energy consumption for ventilation is lower, using (annual average) 41% less energy than without heat recovery. Full article

Considerable variation in soil often occurs within and across production fields, which can significantly impact farming input management strategies. Optimizing resource utilization while enhancing crop productivity is critical for achieving Sustainable Development Goals (SDGs). This paper proposes a low-cost retrofittable Variable Rate Applicator Controller (VRAC) designed to leverage soil variability and facilitate the adoption of Variable Rate Technologies. The controller operates using a Raspberry Pi platform, RTK—Global Navigation Satellite System (GNSS), a stepper motor, and an anti-slip wheel encoder. The VRAC allows precise, on-the-fly control of the Variable Rate application of farming inputs utilizing an accurate GNSS to pinpoint geographic coordinates in real time. A wheel encoder measures accurate distance travel, providing a real-time calculation of speed with a slip-resistant wheel design for precise RPM readings. The Raspberry Pi platform processes the data, enabling dynamic adjustments of variability based on predefined maps, while the motor driver controls the motor’s RPM. It is designed to be plug-and-play, user-friendly, and accessible for a broader range of farming practices, including seeding rates, dry fertilizer, and liquid fertilizer application. Data logging is performed from various field sensors. The controller exhibits an average of 0.864 s for rate changes from 267 to 45, 45 to 241, 241 to 128, 128 to 218, and 218 to 160 kg/ha at speeds of 8, 11, 16, 19, 24, and 32 km/h. It has an average coefficient of variation of 4.59, an accuracy of 97.17%, a root means square error (RMSE) of 4.57, an R square of 0.994, and an average standard deviation of 1.76 kg for seeding discharge. The cost-effectiveness and retrofitability of this technology offer an increase in precision agriculture adoption to a broader range of farmers and promote sustainable farming practices. Full article

Achieving high yields in the large-scale agricultural systems that dominate the US landscape requires critical machine-enabled field operations to be executed in narrow windows of time. Novel cropping systems hold great promise to increase ecosystem services from these large-scale systems, but researchers and end-users need effective methods of representing the timing requirements of such systems. This gap prompted our exploration of approaches to visualize the timing of critical machine-enabled field operations. We refer to the resulting graphic as a field operations visualizer (FOV). We iterated multiple versions of the FOV through a user-centered process involving extensive stakeholder feedback. The resulting FOV version offers a straightforward method of visualizing operation sequences and identifying potential conflicts. Survey results suggest that the FOV provides significant operational insights to users about the timing challenges (or benefits) of novel cropping systems. The FOV may therefore be useful in guiding efforts to improve novel cropping systems and to thereby ultimately increase their deployment to deliver ecosystem services. Full article

Ensuring the quality of medicinal herbs in the herbal market is crucial. However, the genetic and physical similarities among medicinal materials have led to issues of mixing and counterfeit distribution, posing significant challenges to quality assurance. Recent advancements in deep learning technology, widely applied in the field of computer vision, have demonstrated the potential to classify images quickly and accurately, even those that can only be distinguished by experts. This study aimed to develop a classification model based on deep learning technology to distinguish RGB images of seeds from Ziziphus jujuba Mill. var. spinosa, Ziziphus mauritiana Lam., and Hovenia dulcis Thunb. Using three advanced convolutional neural network (CNN) architectures—ResNet-50, Inception-v3, and DenseNet-121—all models demonstrated a classification performance above 98% on the test set, with classification times as low as 23 ms. These results validate that the models and methods developed in this study can effectively distinguish Z. jujuba seeds from morphologically similar species. Furthermore, the strong performance and speed of these models make them suitable for practical use in quality inspection settings. Full article

This study presents a comparative analysis of YOLO detection models for the accurate identification of bean leaf diseases caused by Coleoptera pests in natural environments. By using a manually collected dataset of healthy and infected bean leaves in natural conditions, we labeled at the leaf level and evaluated the performance of the YOLOv5, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 models. Mean average precision (mAP) was used to assess the performance of the models. Among these, YOLOv9e exhibited the best performance, effectively balancing precision and recall for datasets with limited size and variability. In addition, we integrated the Sophia optimizer and PolyLoss function into YOLOv9e and enhanced it, providing even more accurate detection results. This paper highlights the potential of advanced deep learning models, optimized with second-order optimizers and custom loss functions, in improving pest detection, crop management, and overall agricultural yield. Full article

Machine learning algorithms are increasingly used to enhance agricultural productivity cost-effectively. A critical task in precision agriculture is locating a plant’s root collar. This is required for the site-specific fertilization of the plants. Though state-of-the-art machine learning models achieve stellar performance in object detection, they are often sensitive to noisy inputs and variation in environment settings. In this paper, we propose an innovative technique of smooth perturbations to improve the robustness of root collar detection tasks using the YOLOv5 neural network model. We train a YOLOv5 model on blueberry image data for root collar detection. A small amount noise is added as a smooth perturbation to the bounding box of dimensions 50× 50, and this perturbed image is fed for training. Furthermore, we introduce an additional test set that represents the out-of-distribution (O.O.D.) case by applying Gaussian blur on test images to simulate particle situation. We use three different image datasets to train our model, the (i) Estonian blueberry, (ii) Serbian blueberry image, and (iii) public dataset sourced from Roboflow datasets, of sample size 118, 2779, and 2993, respectively. We achieve an overall precision of 0.886 on perturbed blueberry images compared to 0.871 on original (unperturbed) images for the O.O.D. test set. Similarly, our smooth perturbation training has achieved an mAP of 0.828, which significantly increases against the result of normal training, which only reaches 0.794. The result proves that our proposed smooth perturbation is an effective method to increase the robustness and generalizability of the object detection task. Full article

Efficiently managing agricultural systems necessitates accurate data collection from crops to examine phenotypic characteristics and improve productivity. Traditional data collection processes for specialty horticultural crops are often subjective, labor-intensive, and may not provide accurate information for precise management decisions in phenotypic studies and crop production. Reliable and standardized techniques to record and evaluate crop features using agricultural technology are essential for improving agricultural systems. The objective of the research was to develop a methodology for accurate measurement of blackberry flowers and vegetation coverage using UAV remote sensing and image analysis. The UAV captured 20,812 images in the visible spectrum, and ImageJ software (version 1.54k) was used for segmenting floral and vegetative coverage to calculate variety-specific flower coverage. A moderately strong positive correlation (r = 0.71) was found between flower-to-vegetation ratio (FVR) and visually estimated flower area, validating UAV-derived flower coverage as a reliable method for estimating flower density in blackberries. The regression model explained 51% of the variance in flower estimates (R² = 0.51), with a root mean square error (RMSE) of 2.79 flower/cm². Additionally, distinct temporal flowering patterns were observed between primocane- and floricane fruiting blackberries. Vegetative growth also exhibited stability, with strong correlations between consecutive weeks. The temporal analysis provided insight into growth phases and flowering peaks critical for time-sensitive management practices. UAV computer vision for quantifying blackberry phenotypic features is an effective tool and a unique methodology that speeds up the data collection process at high accuracy for breeding research and farm data management. Full article

Oulema melanopus L., 1758 (Coleoptera: Chrysomelidae) is one of the significant pests affecting cereal crops in Europe. Its damage is evident in the destruction of leaves during the spring growing season, leading to substantial impacts on both the quantity and quality of the harvested yields. The study aimed to evaluate the extent of leaf surface damage, changes in chlorophyll content caused by this pest, and the subsequent effects on yield quality. To achieve this, two experimental parcels were established, each subjected to different pesticide treatments during the spring vegetation cycle, but notably, with the difference that one parcel did not receive insecticide applications. The phytosanitary status, yield quantity, and quality parameters of thes parcels were compared. Chlorophyll content in damaged and undamaged plants was monitored in vivo using SPAD measurements, while the extent of leaf surface damage was assessed through image analysis using GIMP software 2.10.32. Harvested grain underwent milling and baking analysis, with milling and baking-quality indicators measured using a NIR grain analyzer. The results revealed that omitting springtime insecticide treatments during the emergence of O. melanopus led to significant reductions in leaf area and yield quality. In untreated parcels, leaf decession followed linear progression, reaching 35–40% within 20 days. This damage correlated with the decline in SPAD index values, indicating a 40–50% reduction in chlorophyll content dependent photosynthetic activity. Consequently, there were substantial decreases in milling and baking qualities, including nearly 30% reductional protein-content indicators and 10% in the Hagberg falling number. In summary, our large-scale field experiments demonstrated that persistent O. melanopus damage in wheat fields significantly reduced both the quantity and quality of yields, particularly protein content. These facts underscore the economic importance of timely pest-control measures to mitigate damage and preserve crop value. Full article

This study evaluated the germination of Cinchona spp. seeds under controlled environmental conditions within a geodesic dome equipped with photovoltaic energy. The main objective was to assess how stable temperature and humidity, along with potassium nitrate (KNO₃) and specific LED light treatments, affect the germination rate and plant growth. The results indicate that Cinchona spp. seeds germinate effectively inside the dome, even under temperature and humidity conditions that differ from their natural habitat. Among the evaluated conditions, the treatment with 1000 ppm of KNO₃ and white LED light (LM 1000 ppm) showed the highest germination rate, achieving 72.5% with an average of 1.5 seeds germinated per day. Agronomic evaluations showed that this treatment also led to superior growth metrics, including an average plant height of 2.1 cm, an average leaf count of 3.6, and a dry weight of 0.0013 g. This research highlights the potential of controlled environments, such as geodesic domes, to optimize germination and early growth in endangered plant species. The combination of environmental control with KNO₃ treatments offers a valuable approach to enhancing the propagation of Cinchona spp., providing practical implications for conservation and reforestation efforts. This work provides a foundation for further studies on optimizing germination and growth conditions for other native and endangered species in controlled environments. Full article

One of the oldest techniques for preserving food is drying. Dehydrating foods reduces their moisture content and increases their shelf life by preventing microbiological activity. Food placed on the ground to dry in the sun is a common sight in rural areas of low- and middle-income countries but requires a large amount of land and can lead to food degradation by overexposure to the sun, insects, and vermin. This study designed, fabricated, and evaluated the performance of a solar dryer in comparison to direct sun drying for efficiency and product quality, utilizing bananas and potatoes as representative foods. The dryer was produced and tested within the context of Bangladesh, unlike other commercial devices. With its mild steel frame, fan, solar collector, and DC battery, the dryer achieved a drying efficiency of 49.2% by reaching a drying chamber temperature of 71 °C, which is 30 °C higher than ambient. Drying times were decreased, and samples of potatoes and bananas reached equilibrium moisture content in 6 h as opposed to 9 h for direct sun drying. The moisture content of solar-dried foods was between 12 and 13 percent, making them appropriate for long-term storage. Bioactive substances such as phenolic content and DPPH scavenging activity were reduced by 18% and 21%, respectively, in comparison to direct sun drying. Quality assessments showed that there was little loss in color and nutrients for solar-dried samples. With a one-year payback period, an economic attribute of 3.26, and a life cycle benefit of BDT 310,651 (USD 2597.68), economics show the dryer’s feasibility. The solar dryer functioned faster than direct sun drying due to its significantly higher heat generation. The solar dryer was more efficient, effective, and economic within the context of Bangladesh and other low- and middle-income countries. Full article

Identifying machine learning models that are capable of classifying soybean genotypes according to micronutrient content using only spectral data as input is relevant and useful for plant breeding programs and agricultural producers. Therefore, our objective was to classify soybean genotypes according to leaf micronutrient levels using multispectral images. In the 2019/20 crop year, a field experiment was carried out with 103 F2 soybean populations in the experimental area of the Federal University of Mato Grosso do Sul, in Chapadão do Sul, Brazil. The data were subjected to machine learning analysis using algorithms to classify genotypes according to leaf micronutrient content. The spectral data were divided into three distinct input groups to be tested in the machine learning models: spectral bands (SBs), vegetation indices (VIs), and combining VIs and SBs. The algorithms tested were: J48 Decision Tree (J48), Random Forest (RF), Support Vector Machine (SVM), Perceptron Multilayer Neural Network (ANN), Logistic Regression (LR), and REPTree (DT). All model parameters were set as the default settings in Weka 3.8.5 software. The Random Forest (RF) algorithm outperformed (>90 for CC and >0.9 for Kappa and Fscore) regardless of the input used, demonstrating that it is a robust model with good data generalization capacity. The DT and J48 algorithms performed well when using VIs or VIs+SBs inputs. The SVM algorithm performed well with VIs+SBs as input. Overall, inputs containing information about VIs provided better results for the classification of soybean genotypes. Finally, when deciding which data should serve as input in scenarios of spectral bands, vegetation indices or the combination (VIs+SBs), we suggest that the ease and speed of obtaining information are decisive, and, therefore, a better condition is achieved with band-only inputs. This allows for the identification of genetic materials that use micronutrients more efficiently and the adaptation of management practices. In addition, the decision to be made can be made quickly, without the need for chemical evaluation in the laboratory. Full article

The application of hyperspectral data in machine learning models can contribute to the rapid and accurate determination of caffeine content in coffee beans. This study aimed to identify the machine learning algorithm with the best performance for predicting caffeine content and to find input data for these models that can improve the accuracy of these algorithms. The coffee beans were harvested one year after the seedlings were planted. The fresh beans were taken to the spectroscopy laboratory (Laspec) at the Federal University of Mato Grosso do Sul, Chapadão do Sul campus, for spectral evaluation using a spectroradiometer. For the analysis, the dried coffee beans were ground and sieved for the quantification of caffeine, which was carried out using a liquid chromatograph on the Waters Acquity 1100 series UPLC system, with an automatic sample injector. The spectral data of the beans, as well as the spectral data of the roasted and ground coffee, were analyzed using machine learning (ML) algorithms to predict caffeine content. Four databases were used as input: the spectral information of the bean (CG), the spectral information of the bean with additional clone information (CG+C), the spectral information of the bean after roasting and grinding (CGRG) and the spectral information of the bean after roasting and grinding with additional clone information (CGRG+C). The caffeine content was used as an output to be predicted. Each database was subjected to different machine learning models: artificial neural networks (ANNs), decision tree (DT), linear regression (LR), M5P, and random forest (RF) algorithms. Pearson’s correlation coefficient, mean absolute error, and root mean square error were tested as model accuracy metrics. The support vector machine algorithm showed the best accuracy in predicting caffeine content when using hyperspectral data from roasted and ground coffee beans. This performance was significantly improved when clone information was included, allowing for an even more accurate analysis. Full article