Early and reliable detection of plant wilting is crucial for maintaining crop productivity and ensuring sustainable agricultural practices. This study aimed to evaluate the effectiveness of machine learning models in detecting wilting in Vinca rosea plants using simple geometric features extracted from digital images. The extracted features included leaf width, length, area, aspect ratio, and area coverage. Four machine learning classifiers, Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbours (kNN), and Naive Bayes (NB) were trained and validated to classify plants as wilted or healthy. Results showed that SVM achieved the highest performance, with 0.98 accuracy, 0.99 precision, 0.95 recall, 0.97 F1 Score, and 0.99 area under the curve (AUC), outperforming the other models. DT and kNN achieved competitive accuracy (0.97 and 0.95, respectively) but exhibited weaker recall, while NB showed high recall (0.92) but reduced precision (0.87). The findings highlight the robustness of SVM and the potential of simple geometric features combined with machine learning for rapid and reliable wilting detection. Moreover, integrating such image-based wilting detection with irrigation control can optimise water use while sustaining plant health and yield.
Efficient irrigation is vital for crop cultivation, particularly for eggplant (Solanum melongena), which ranks as Malaysia‘s fourth most important vegetable crop. This study evaluates the effectiveness of an Internet of Things (IoT)-based smart irrigation system in enhancing eggplant growth under rain shelter conditions. The system monitored environmental and soil parameters, including air temperature, relative humidity, soil pH, electrical conductivity (EC), and soil moisture and temperature, enabling remote irrigation adjustments to optimise plant health and resource use. Plant growth and environmental data were collected using both manual and IoT-based monitoring systems. IoT sensor measurements were calibrated against manual sensors at specified intervals. Results revealed a 32-day time series of sensor data and calibration curves, with R-squared values ranging from 0.78 to 0.98, indicating strong agreement between manual and IoT measurements. Eggplants irrigated using the IoT system showed significant improvements in plant height, leaf length, leaf diameter, stem thickness and root biomass compared to manual irrigation. The IoT-based monitoring system also produced reliable and consistent measurements, supporting its accuracy and applicability. In conclusion, the IoT-based smart irrigation system enhanced eggplant growth, reduced resource wastage, and improved overall efficiency, highlighting its potential for sustainable agricultural practices.
A variable seed rate system was integrated and evaluated using Trimble technology. Laboratory tests were conducted to assess system performance on a seeder test bench, with seed delivery rates ranging from 180 to 800 seeds per minute. The system was tested using two corn seed plates: a 30-cell Precision Planting plate (PP) and a 40-cell John Deere (JD) plate. The laboratory results showed a cell fill rate of 98.6% for the PP plate and 104.5% for the JD plate. Field performance was evaluated at four sowing densities (100,000; 80,000; 60,000; and 40,000 seeds·ha−1) and three travel speeds (6, 8, and 10 km·h−1). Results showed no significant differences in dosing uniformity due to sowing density otr speed. The system achieved 100% dose uniformity with a coefficient of variation (CV) of just 1.36%, demonstrating high operational reliability. Regarding seed delivery uniformity, an analysis of variance of seed fall timing revealed a significant difference between the plates. The PP plate outperformed the JD plate, with a CV of 10.82% compared to 14.387%, respectively. The system‘s response time to changes in seeding density ranged from 43 milliseconds (from 100,000 to 80,000 seeds·ha−1 at 10 km·h−1) to 232 milliseconds (from 40,000 to 100,000 seeds·ha−1 at 6 km·h−1). In conclusion, the integrated variable seed rate system demonstrated high operational reliability. However, an average variability of 12.5% in planter performance was observed, highlighting an opportunity for further optimisation and system improvement.
Soybean is a key crop due to its nutritional, industrial, and agricultural benefits. Its production performance faces challenges such as improper storage, seed damage, and moisture imbalance. This paper aims to classify soybean seeds into five categories – healthy, damaged, immature, broken, and stained – using image processing and deep learning. Four classifiers DenseNet, MobileNet, ResNet, and EfficientNet were used to classify seeds on a dataset of 5513 soybean seed images. The best performance among the models used was obtained from DenseNet with an accuracy of 96% and an F1-score of 94%. The results also showed that all models achieved an AUC value above 0.99, indicating high performance of the models. This research highlights the potential of machine learning and artificial intelligence in optimising soybean classifiers.
The aim of this study was to analyse long-term changes in diesel engines used in agricultural tractors between 2015 and 2030, with particular attention to displacement, cylinder configuration, power output, power density, efficiency and emission compliance. Technical data from tractor catalogues published in 2015 and 2024 were examined for standard agricultural models representing 33 manufacturers and power ratings from 34 kW to 527 kW. Descriptive statistics, comparative analysis and linear trend modelling were used to identify design tendencies and to forecast changes up to 2030. The results showed a systematic increase in average power and torque, accompanied by a moderate reduction in average displacement and the almost complete standardisation of turbocharging. Downsizing, based on smaller displacement and higher specific output, was most evident up to approximately 2020. Later developments indicate a gradual shift towards rightsizing, in which engine size is better matched to sustained agricultural workloads, thermal robustness and durability requirements. This shift is linked to wider implements, higher working speeds, heavier tractors and the integration of emission-control systems required by Stage V regulations. The findings indicate that modern tractor engine development is moving from aggressive displacement reduction towards balanced optimisation of efficiency, torque stability, durability and emission performance.
This study focuses on the conversion of a turbocharged compression-ignition (diesel) engine UR-II 8602.12, originally installed in the Zetor 16145 tractor, into a spark-ignition (gas) unit operating on gaseous fuel. The study presents experimental results of performance, fuel economy, and emission characteristics under full-load speed sweep as well as part-load operation. The converted engine achieved a maximum corrected power output of 106.5 kW at 2200 rpm, representing an 8.5% reduction relative to the original diesel engine (116.5 kW). The corrected peak torque reached 462 N·m, which is 21% lower than the baseline diesel torque of 583 N·m. From an environmental perspective, carbon monoxide emissions remained very low (~0.05 vol. %), while hydrocarbon concentrations decreased with increasing mixture enrichment (from 377 ppm to 245 ppm). However, nitrogen oxides (NOx) exceeded the threshold of 500 ppm across the entire speed range, peaking at 2282 ppm at the maximum speed. Exhaust gas temperatures behind the turbine reached up to 650 °C, which, after accounting for thermal gradient, implies that the recommended limit of 630 °C at exhaust valves is exceeded at higher loads. The findings demonstrate that natural gas conversion can retain favourable power characteristics while lowering carbon-based emissions, yet further optimisation is required to control thermal stress and mitigate NOx formation.
Fruit detection and segmentation are key components of agricultural automation pipelines, enabling tasks such as yield estimation, quality sorting, and robotic harvesting in resource-constrained environments. This study evaluates four colour spaces – RGB, HSV, CIE L*a*b* (LAB), and YCbCr – for threshold-based fruit segmentation in precision agriculture and robotic harvesting. It specifically investigates which colour space maintains the highest accuracy and stability under variable indoor illumination. A dataset of 144 images featuring fruits from four colour categories (red, green, orange, and yellow) was utilised. Colour thresholds were calibrated through manual region-of-interest selection and applied using chromatic thresholding and morphological refinement, resulting in 2304 mask evaluations based on intersection over union (IoU), precision, recall, and F1-score. Clear and statistically significant differences were found among colour spaces: HSV achieved the highest overall performance (F1-score = 0.972, IoU = 0.947), followed by RGB (F1-score = 0.823), LAB (F1-score = 0.714), and YCbCr (F1-score = 0.682), confirmed by non-parametric statistical testing. HSV is established as the most accurate and consistent colour representation for fruit segmentation under the proposed experimental protocol, providing practical guidance for lightweight, real-time segmentation pipelines in agricultural automation systems operating under variable illumination conditions.
This experimental study systematically explores the effects of elevation height and ground surface condition on the thermal and electrical performance of photovoltaic modules based on monocrystalline silicon photovoltaic modules under field conditions in a subtropical climate. Three identical 60 W monofacial PV modules were installed at heights of 0.7 m, 1.1 m, and 1.6 m above dried grass and vegetation-covered surfaces to assess the influence of airflow, albedo, and microclimatic cooling. Voltage, current, ambient air speed, ambient pressure, temperature, and irradiance were continuously recorded using high-precision dataloggers. Results show that module elevation strongly affects operating temperature and electrical efficiency, with the 1.1 m height yielding the highest power output (maximum 45.2 W) and conversion efficiency (7.0%). Vegetation reduced cell temperature through evapotranspiration, enhancing performance, while dried grass increased heat accumulation. Despite slightly higher irradiance above dried grass, modules over vegetation performed more efficiently. Overall, a moderate elevation and vegetative cover improve PV cooling and efficiency, offering an effective strategy for optimising agrivoltaic and eco-integrated PV systems.
Modern food safety standards require environmentally friendly storage solutions, especially for oil-rich crops like walnuts, which are highly susceptible to oxidation and microbial spoilage. This study aims to develop and optimise a technology for the long-term, safe storage of Kazakhstani walnuts using ion-ozone treatment. Organoleptic, chemical (acid number), and microbiological indicators were evaluated before and after treatment. Processing parameters - ion concentration, ozone level, temperature, and exposure time - were optimised using mathematical modelling, including regression equations and response surface analysis. Optimal treatment conditions were established: ion concentration = 10(-4) units & centerdot;m-3, ozone = 1.0 g & centerdot;m(-3), temperature = 25 degrees C, and duration = 60 minutes. The resulting acid number (2.48 mg KOH & centerdot;g(-1)) met quality standards, while the walnuts retained their sensory qualities and microbiological safety. Ion-ozone technology is an effective, eco-friendly method for extending walnut shelf life. The findings provide a scientific basis for industrial applications in storing walnuts and other oil-containing crops.
Random traffic farming (RTF) is an approach in Iraq's cropping practices where uncontrolled machinery traffic frequently causes soil compaction. Predicting machinery draft force and resulting compaction under random traffic farming is therefore essential for improving machine efficiency and enhancing long-term soil sustainability. This study applies artificial neural networks (ANNs) for predicting draft force, soil penetration resistance, and bulk density under various operational parameters. These parameters included tractor mass (3000 kg vs 6000 kg), traffic intensity (0-3 passes), and tillage depth (150 mm vs 250 mm). Experimental fieldwork was conducted on silty clay soil at Ninawa governorate. Draft force (DF) was directly measured, while soil compaction was evaluated using soil penetration resistance (SPR) and bulk density (BD). Field experiment results indicated that machinery traffic was the most influential factor, a first pass increased SPR and DF by up to 76% and 113%, respectively. The ANN model demonstrated high predictive accuracy for DF (R = 0.985) and SPR (R = 0.858), though BD predictions were less accurate (R = 0.631). These findings highlight that ANN modelling is an effective tool for optimizing machinery use and traffic management in RTF systems, thereby supporting sustainable soil management in arid regions.
The assessment of in-vehicle comfort using physiological measurements has gained increasing attention in recent years. Skin resistance (electrodermal activity, EDA) represents a promising indicator of sympathetic nervous system activity and thermal comfort perception. This study examines the influence of sampling frequency on the analysis of skin resistance in relation to cabin temperature, providing insights relevant for adaptive HVAC systems. Data were collected during winter test drives inSkoda Kodiaq in collaboration with Mendel University. Electrodermal activity was recorded in four participants at 1 Hz and subsequently subsampled to lower frequencies (0.5 Hz, 0.2 Hz, and 0.1 Hz). Analytical methods included correlation analysis, entropy, and variability assessment. The results demonstrated that reducing the sampling frequency led to lower correlation strength and diminished signal information content. Sampling frequency therefore has a significant impact on the accuracy and interpretability of physiological signal analysis. An optimal balance between data quality and computational efficiency was observed in the range of 0.2-0.5 Hz. These findings provide a valuable basis for developing intelligent HVAC systems that integrate physiological feedback.
To address the high excavation resistance and root damage in mechanised Panax notoginseng (Sanqi) harvesting, this study designed a specialised biomimetic digging shovel inspired by the drag-reducing morphology of the two-horned rhinoceros beetle (Allomyrina dichotoma). The shovel structure was designed based on 3D-scanned geometric features of the beetle and the agronomic requirements of raised-bed mulching cultivation. A root-soil-shovel interaction model was established using the Discrete Element Method (DEM) to simulate the effects of shovel shape and operational parameters on digging resistance and soil-root separation. Key structural parameters were optimised via Response Surface Methodology (RSM). Soil-bin tests confirmed the simulation results and drag-reduction performance. Compared to a conventional flat shovel, the biomimetic shovel achieved an average drag reduction of over 23%, with a maximum reduction of 31.4% at a 25 degrees entry angle, 0.8 m & centerdot;s-1 working speed, and 30 mm blade spacing. The optimal parameters were a vertical spacing of 4.13 mm, horizontal spacing of 3.71 mm, and a top angle of 130 degrees, yielding a theoretical digging resistance of 162.15 N and an experimental value of 165.39 N (deviation 1.96%). This study provides a reliable biomimetic design method and engineering solution for low-damage, high-efficiency mechanised harvesting of Panax notoginseng.
Beans have become a popular food choice in most countries around the world due to their nutritional value and their high protein, fibre, and mineral content. However, like other high-consumption products, beans are not immune to food adulteration. Fresh beans are more popular and more expensive due to their high quality and short cooking time, which results in reduced energy consumption that is a tempting reason for adulteration. Machine learning is one of the emerging approaches to non-destructive detection of food adulteration. This study investigates the application of supervised learning algorithms - AdaBoost, random forest, and ridge classifiers - to distinguish fresh and stale beans based on spectral data. The data were collected using a VISNIR spectrometer, and the models were evaluated under different noise conditions using metrics such as precision, accuracy, recall, F1 score, and ROC/PR curves. Ridge is the most robust model to noise in all four metrics (Accuracy > 0.95, Precision > 0.92, Recall > 0.97, and F-Score > 0.95). Adaboost and RF perform well in low noise, but their performance degrades as noise increases. Ridge is a better choice for applications with noisy environments or data quality issues.
This study evaluated the potential of near-infrared spectroscopy (NIRS) combined with multivariate analysis to predict soil nitrogen (N) content in both wetland and dryland systems of Java Island, Indonesia. A total of 145 soil samples were collected across four provinces and analysed using a Frontier FT-NIR spectrometer in the 750-2500 nm range. Spectral data were pre-processed using several methods, and partial least squares regression (PLSR) models were calibrated and validated against reference Kjeldahl measurements. Among all approaches, asymmetric least-squares (ALS) baseline correction coupled with PLSR achieved the best predictive performance, with R & sup2; = 0.882, RMSE = 0.0401% N, and RPD = 2.84 on the independent validation set. Characteristic wavelengths were identified around 1400, 1900, and 2200-2330 nm, corresponding to overtone and combination bands of N-H and O-H vibrations. The results confirm that NIRS offers a rapid, non-destructive, and cost-effective alternative for soil nitrogen assessment under diverse agroecosystems. This approach can support precision nutrient management in tropical smallholder farming systems and provides a foundation for developing portable, field-ready detection tools.
Most of tillage research focuses on designing more efficient tools to operate with low energy consumption. Also, developing tools to preserve conservation tillage is a priority for researchers. The aim of the study was to compare a paraplough with and without wings with conventional mouldboard plough in terms of loosened area, fuel consumption, and specific fuel consumption. Field trials were conducted using an MF 285 tractor at three working depths of 10, 20, and 30 cm and at three forward speeds of 2, 5, and 7 km & centerdot;h(-1) for conventional mouldboard plough, paraplough without wings, paraplough with backward-bent wings, and paraplough with forward-bent wings. Two flow sensors made by Oval Company in Japan were used to measure the fuel consumption of the tractor. The highest fuel consumption of 14.58 L & centerdot;h(-1) occurred when using the mouldboard plough and the least fuel consumption of 5.849 L & centerdot;h(-1)was obtained using the paraplough without wings. Increasing travel speed and working depth increased fuel consumption significantly. Adding both backward- and forward-bent wings to the paraplough increased the loosened area by 17.25 and 7.4%, respectively. Also, adding wings increased specific fuel consumption by 54 and 74% in comparison with the paraplough without wings. Both loosened area and specific fuel consumption parameters showed using the backward-bent wing is more effective than the forward-bent wing in terms of energy efficiency. Using the backward- and forward-bent wings decreased specific fuel consumption by 34.1 and 18.7% in comparison with the mouldboard plough, respectively. It was concluded that for soil loosening using tines with wings is more effective in terms of energy saving.
Bio pellets serve as an eco-friendly alternative for energy production, utilising renewable crops to produce clean energy while supporting agricultural sustainability. This study aims to design and test the performance of a roller-type pellet press for sorghum biopellets. The machine was designed to mix sorghum with other ingredients to increase its calorific value. The testing of performance used sorghum plants of the bioguma variety from sorghum farmers at a seed moisture content of 20-30% after harvest at 105-110 days after planting (DAP) directly from the sorghum field. The results of testing the performance of the pelletiser moulding machine show that the working capacity of the machine is 100-130 kg & centerdot;h(-1). This capacity is influenced by the roller rotation, the rotation of the drive motor, and the moisture content of the sorghum biopelet raw material. The average pelletising efficiency was 98.80%, while the loss rate ranged from 0 to 1%. In addition, the thermogravimetric and derivative thermogravimetric analysis showed that sorghum, rice husk, and their blends have different thermal behaviours, with the sorghum-rice husk blend showing potential as an efficient biomass feedstock for bioenergy production.
Subsurface application of mineral fertilisers is an effective agricultural technique that improves both the quantity and quality of cultivated crops. To ensure uniform distribution of granular mineral fertilisers across the width of a tillage working body with a working width of 50 cm or more, spreading devices shaped like triangular prisms with curved working faces are installed in the sub-blade area. For uniform fertiliser distribution across the entire width of the sub-blade area, fertiliser particles must follow variable trajectories that evenly intersect the sub-blade area. Our hypothesis is that this can be achieved by modifying the spreading device, cutting off a portion so that the curved edges of its working faces have different lengths. The aim of this study is to determine the optimal angle of inclination of the cutting plane relative to the prismatic spreader's working faces. Our research methods include physical and mathematical modelling, as well as abstraction, analysis, and synthesis. We also conducted experimental studies in accordance with current regulatory documentation. The optimal angle of inclination of the spreader's cutting plane was determined to be 80 degrees. We manufactured several spreader prototypes and conducted experimental studies to evaluate how the cutting plane's angle of inclination affects the unevenness of fertiliser distribution. The minimum unevenness, ranging from 24.5% to 27.8%, was achieved at a cutting plane angle of alpha = 75-80 degrees. The graphical modelling method we developed accurately reflects the actual behaviour of the objects. Our hypothesis was proven valid, and the research aim was successfully achieved.
The study investigates the feasibility of monitoring water content in the topsoil. The study was conducted over five growing seasons from 2019 to 2024. The primary instrument used was an unmanned aerial vehicle (UAV) equipped with a multispectral camera recording reflectance in the near-infrared (NIR) region. Data was collected by flying the UAV at an altitude of 100 m above agricultural areas to capture individual images. These images were then processed to create an orthomosaic, from which the WDVI index (Weighted Difference Vegetation Index) was generated. For comparison, the normalised difference vegetation index (NDVI), which assesses the crop‘s vegetation stage, was also used. The index values were compared with meteorological data collected from the specific field. The study showed a strong correlation before crop coverage reached 50% (R = 0.96 and R2 = 0.94). With dense plant cover, the correlation was weaker (R = 0.87 and R2 = 0.77).
The paper deals with the effects of fuel additive on the performance parameters and emissions of a diesel engine. Experimental measurements were carried out on a Renault Alaskan 2.3 dCi vehicle with 96,500 km mileage, using a MAHA MSR500/3 dynamometer and a MAHA MET MGT 6.3 exhaust gas analyser. The tests compared power, torque, and emissions (CO, CO2, HC, NO2, smoke opacity) when using pure diesel fuel and diesel with additive in a 1 : 1000 ratio. The results showed a statistically significant improvement in performance characteristics: average power increased by 11.4% (from 87.41 kW to 97.34 kW) and torque by 14.2% (from 315.30 Nm to 360.19 Nm), with the maximum difference in the mid-range rpm (2000–3000 min−1). Emissions showed a reduction in CO₂ (by 6.6%) and NO2 (by 10.3%), but the HC content increased almost threefold. Differences in CO and smoke opacity were at the measurement error threshold. The statistical analysis (two-sample t-test) confirmed the high significance of the results (p-values <0.05). The conclusions point to a complex effect of the additive, including an influence on the fuel system and turbocharger, which led to more efficient combustion manifested by higher performance and an altered emission profile.
The accelerating demand for food production driven by economic, political, and social factors has highlighted the need for innovative solutions to increase harvesting efficiency. In response, this work presents the design and development of a prototype robotic arm for fruit harvesting, suitable for medium-sized fruits such as citrus, apples, and pears in semi-controlled environments like greenhouses and experimental orchards. The system integrates four Nema 17 stepper motors (17HS8401) and four 360-degree servomotors (MG996R), structured as a three-degree-of-freedom mechanism to balance simplicity and functionality, enabling easy replication. The arm components were fabricated using 3D printing technology, combining rigid polylactic acid filament for structural parts and flexible thermoplastic polyurethane filament for the adaptive gripper. The unique design eliminates the need for sensors, allowing adaptable and reliable fruit collection using cost-effective actuation while minimising damage to delicate produce. Experimental validation showed success rates of 87% for citrus fruits, 67% for apples, and 60% for pears, highlighting the system‘s adaptability to different fruit geometries and textures. Comprehensive force and torque calculations ensure the system‘s operational viability despite relying on low-cost electronic components. These findings demonstrate that the proposed robotic solution offers a practical, low-cost, and adaptable approach for agricultural automation, bridging the gap between laboratory prototypes and deployable systems for small- and medium-scale producers.
