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This study explores the construction of small reservoirs (SRs) as a strategic solution to address water scarcity in Kashkadarya Province, Uzbekistan, where agricultural productivity is heavily dependent on irrigation. By utilising geographic information system (GIS) and remote sensing (RS) technologies, optimal locations for reservoirs were identified, focusing on improving the water availability for irrigation during critical periods. The research highlights the socio-economic and environmental benefits of SRs, including enhanced agricultural yields, increased employment opportunities, and reduced reliance on energy-intensive pumping stations. The findings indicate that the construction of an 18 Mm³ reservoir in the Ayakchisoy River could supply water to 26.5 thousand hectares, thereby improving the region’s resilience to climate variability. This approach offers a sustainable framework for managing water resources in arid regions, contributing to food security and economic stability.

Octopus (Octopus sp.) is highly perishable marine species for which efficient drying is essential to extend shelf life in tropical climates. The anatomical heterogeneity of the octopus complicates consistent drying. This study systematically evaluated the performance of a mixed solar dryer (MSD) and open solar drying (OSD) across distinct anatomical regions (head, mantle, and tentacles), with emphasis on drying kinetics and quality attributes. Five thin-layer models were applied to characterize moisture reduction, and product quality was assessed by measuring browning, protein, fat, and ash content. The MSD achieved a 20% higher temperature and 29% lower humidity, resulting in a 74% increase in drying rate relative to OSD. The Hasibuan and Daud model exhibited the highest predictive accuracy (coefficient of determination (R²) = 0.9965; root mean square error (RMSE) = 0.0168; sum of squared errors (SSE) = 0.0058). Significant interaction effects between anatomical region and drying method were observed for browning and ash content (P < 0.05), whereas protein and fat content were primarily influenced by anatomical characteristics. Overall, the MSD produced products with reduced browning and enhanced nutrient retention. These results support the implementation of MSD technology by small-scale processors to improve both drying efficiency and product quality in octopus preservation.

This study investigated the effects of temperature on the performance of anaerobic digesters for biogas production. Digesters were filled with a 1 : 1 ratio of substrate to water, containing 15 kg of cow dung and 3 kg of crop waste, and maintained at temperatures of 50 ± 2 °C and 30 ± 2 °C, corresponding to the thermophilic and mesophilic biodigesters, respectively. The experiments run for 75 days, and biogas production rate and purity were measured. The thermophilic digester produced 48.4% more biogas and had a slightly higher pH (7.65) than did the mesophilic digester (7.37) by the end of the observation period. However, gas chromatography revealed that the CH₄ and CO₂ contents did not significantly differ between the two treatments. The CH₄ concentration in the mesophilic environment was 42 ± 10%, whereas that in the thermophilic environment was 53.5 ± 10%. The CO₂ composition was 32.5 ± 1% and 35.5 ± 1% for the mesophilic and thermophilic setups, respectively. These were supported by the wavelength (460 nm to 620 nm) of the flame colour, indicating that the biogas from both setups is predominantly composed of methane. In conclusion, thermophilic anaerobic digesters may have a relatively high biogas production rate, but the biogas purity is not significantly different from that of mesophilic digesters.

The compressibility features (bulk density, tapped bulk density, porosity, coefficient of compressibility and Hauser ratio) of Canarium schweinfurthii engl. nutshell powder as it affects flowability during densification process were investigated. Three different moisture contents (10.13, 15.07 and 20.11% wet basis; w.b.) and particle sizes of 0.659 7, 1.26 and 2.05 mm were considered at pressure range of 2 to 10 MPa. The compressibility relationship with the factors were modelled and the optimum flow conditions were also determined. The obtained results showed that particle size and moisture content had incremental influence on the compression features studied except moisture content on bulk density. The compressibility of the nut shell powder increased from 17.44 to 28.18% and decreased from 29.41%to 18.79% as moisture content and particle size increased respectively. Medium particle size had the least Hausner ratio (1.16) and the best flow behaviour than other sizes for all the studied moisture contents. The linear model developed and its features had significant relationship with compressibility. The optimum values of pressure, moisture content and particle size required to achieve 17.45% compressibility for good flow are 4.88 MPa, 10.91% and 0.863 8 mm respectively.

Recent advancements in remote sensing techniques, especially the combination of hyperspectral imaging with analytical algorithms, have greatly improved precision agriculture. This study introduces some algorithms developed for identifying crops and evaluating their growth conditions, focusing on irrigation and fertilisation. The present approach is based on the concept of identifiability of a family of dynamic systems and the differentiation of plants using their spectral signatures. The method uses a repository of spectral data and applies a developed algorithm to compare the measured spectra with the reference database, enabling the identifiability and the recognition of both known and unknown crops. As an application of our approach, we have considered two different crops: mint and rosemary, under different irrigation and fertilisation conditions. The results show that the algorithm achieved a 100% identification rate across the four unknown samples. The minimum spectral distances obtained are 0.01 and 0.03 for rosemary and mint, respectively. Thus, the family of systems was identifiable with a tolerance of η < 0.03. The study concluded that the algorithm effectively classifies the crop type and deduces its growth conditions, demonstrating its effectiveness for agricultural monitoring.

Reference crop evapotranspiration (ET_o) is a vital hydrological component influenced by various climate variables that impact the water and energy balances. It plays a crucial role in determining crop water requirements and irrigation scheduling. Despite the availability of numerous approaches for estimation, accurate and reliable ET_o estimation is essential for effective irrigation water management. Therefore, this study aimed to identify the most suitable machine learning model for assessing ET_o using observed daily values of limited input parameters in tropical savannah climate regions. Three machine learning models – a long short-term memory (LSTM) neural network, an artificial neural network (ANN), and support vector regression (SVM) – were developed with four different input combinations, and their performances were compared with those of locally calibrated empirical equations. The models were evaluated using statistical indicators such as the root mean square error (RMSE), coefficient of determination (R²), and the Nash-Sutcliffe efficiency (NSE). The results showed that the LSTM model, using the combination of temperature and wind speed, provided more reliable predictions with R² values greater than 0.75 and RMSEs less than 0.63 mm·day^–1 across all the considered weather stations. This study concludes that, especially under limited data conditions, the developed deep learning model improves the ET_o estimation more accurately than empirical models for tropical climatic regions.

Mechanical sugarcane harvesting generates substantial material losses that are associated with the equipment age. This study evaluated the relationship between the harvester service life and the operational efficiency by analysing field losses and product contamination across machines with varying operational histories (1, 14, 16, and 17 years) in Chaiyaphum Province, Thailand, using a randomised complete block design. The results indicate that the 17-year-old machines exhibited 54% higher total losses (241.93 kg·ha^–1) compared to the newer equipment (156.90 kg·ha⁻¹). The field losses were attributed primarily to base cutting operations (36%) and roller mechanisms (34%), collectively accounting for 70% of the total losses. The contamination analysis revealed sugarcane tops as the predominant impurity source (57% The revenue loss analysis indicates excessive field losses from ageing equipment reducing the farm profitability by 12–18%. The non-linear relationship between the equipment age and performance demonstrates that maintenance practices significantly influence degradation patterns, providing critical insights for optimising mechanical harvesting systems.

Conventional irrigation practices result in a substantial amount of water loss with okra cultivation. Although micro-irrigation can address this issue by delivering water directly near the rootzone, it requires manual operation. These issues, however, can be resolved with the introduction of a smart micro-irrigation system. This study aims to develop a smart micro-irrigation system for okra, in conjunction with the sub-components of drip irrigation, a microcontroller, and a soil sensor. The experiment was laid out with a randomised complete block design (RCBD) having three treatments: (i) control irrigation (T₁), (ii) drip irrigation (T₂), and (iii) smart micro-irrigation (T₃). The experimental field was irrigated based on soil moisture regimes in the crop rootzone. The plant growth, yield, and water use efficiency were assessed to evaluate the system. The results showed no significant differences among these treatments (at P < 0.05). The best water usage efficiency (15.98 kg·m^–3) was observed in the T₃ treatment, which also provided about 13.10% water savings compared to the conventional irrigation. This study indicates that a smart micro-irrigation system could be a promising technology for water-efficient okra cultivation.

In the context of the modern EU bioeconomy, the use of industrial crops, particularly castor beans (Ricinus communis L.), is relevant for the production of industrial and energy products without competing with food crops. Castor oil is used for the production of biodiesel, lubricants, paints and coatings, cosmetics, and pharmaceuticals. In Ukraine, castor beans can be cultivated on low-yield soils, with seed yields ranging from 1.5 t/ha to 2.1 t/ha depending on the variety. However, the morphological features of the fruits and uneven ripening complicate the mechanisation of harvesting and seed cleaning processes. Existing equipment designed for other crops cannot be applied due to the risk of damaging the castor seeds. A design of a castor seed cleaning machine has been proposed, combining mechanical fruit shelling with the aerodynamic separation of the mixture. The machine is equipped with an eccentric crushing cone, rubber linings, a pneumatic separation channel, a cyclone, and an automated control unit. As a result of numerical modelling and experimental studies of the process of separating and cleaning castor seeds, dependencies were obtained for the productivity of the developed machine Q, power consumption P, specific energy consumption E, fraction of unshelled fruits (segments) ξ_f, content of the clean seed in the seed collector ψ_s-s depending on the distance between the reverse and crushing cones δ, rotation frequency of the crushing cone n, diameter of the feed opening D_in, inclination angle of the crushing cone axis γ, and airflow velocity V. A multi-criteria optimisation method was applied to find the optimal operating modes: δ = 10.8 mm; n = 282 rpm; V = 3.6 m/s; D_in = 98 mm; γ = 3.6°; β = 20.3°. The following results were achieved: E = 0.0394 MJ/kg; Q = 163.4 kg/h; P = 1 861 W; ξ_f = 0.099; ψ_s-s = 0.958. The obtained results confirm the efficiency of the proposed design for industrial implementation.

A combined roaster and oil expeller was developed with locally available and accessible materials and the efficiency of the machine was evaluated. The obtained result shows that the efficiency of the machine is a function of the roasting temperature, the roasting duration, the moisture contents of the processed seeds and the machine feeding rate. The expeller is movable, simple in design for local fabrication, is easy to operate, requires few repairs and little maintenance and is cost effective. It is powered using a gear reduction electric motor of 5.5 Hp, the expeller has an average oil yield of 25.77% and an extraction efficiency of 70.26% and is designed to work for 8 hours per day of operation. The shaft diameter was designed to be 30 mm, while the roaster heater capacity was 2.8 kW. The designed machine is good for castor oil expression for both small- and medium-scale processing among rural and urban communities.

Various proportions of bentonite clay performing as co-catalysts were evaluated for the production of biodiesel from waste cooking oil (WCO). The results showed that the use of bentonite as a heterogeneous co-catalyst could significantly increase the biodiesel yield by approximately 50% of the control. The heterogeneous co-catalyst of bentonite clay improved the properties of the produced biodiesel including acid number, free fatty acids (FFA), relative density, kinematic viscosity and flash point fulfilling with the standard ASTM limits and the European Biodiesel Standard (EN 14214). The use of bentonite clay in the transesterification of WCO could also enhance the conductivity of the produced biodiesel from 11 to 100 µS·m^–1.

This study develops and validates a multilayer diffusion model of wheat grain hydration that incorporates layer-dependent diffusion coefficients for bran, endosperm, and germ. The moisture transport is formulated using Fick’s law with two interface formulations: (i) classical continuity of the concentration and flux and (ii) an interlayer resistance formulation that permits concentration discontinuities. Diffusion coefficients and geometric parameters were determined experimentally; A 3D grain model (structured-light scanning, COMSOL Multiphysics) informed the computational domain. Numerical solutions combined eigenfunction expansions with finite-difference discretisation near the interfaces. Across eight winter wheat varieties, the diffusion coefficients spanned 11.6 – 20.5 × 10–12m2·s–1 (mean 16.27 ± 3.08 × 10–12m2·s–1 ). Relative to the continuity model, the resistance model reduced the early-stage endosperm over-prediction by ~ 0.6–1.0 % (absolute) and lowered the whole-grain RMSE by ~ 20–30% over 0–240 min. These results support the role of thin moisture-retaining films as active barriers and yield smooth, real-time-ready outputs suitable for the automated control of pre-milling hydration; the framework is extensible to full 3D transient simulations.

A free electricity tandem-twin-hybrid-solar-biomass dryer comprised of two drying rooms and operated with solar and biomass energy combustion of 10 kg rubber wood per hour separately to dry Robusta coffee cherries with 3, 6, 9, and 12 cm bed thicknesses were studied with the drying completion time (tc), number of defects (ND), and colour parameters, i.e., lightness (L*), hue angle [H(^o)], and chroma (C), used as the performance indicators. The experimental results indicated that the drying room, bed thickness, and drying room-bed thickness interaction significantly affected the tc and ND and bed thickness only significantly affected C for both the solar energy drying and the biomass energy drying. The solar energy drying generated a drying air temperature of 44.6 ± 3.5 °C with a tc of 70.9–90.2 h for the front drying room and 40.1 ± 2.8 °C with a tc of 77.2–116.5 h for the rear drying room, whereas the biomass energy drying produced a drying air temperature of 57.2 ± 3.6 °C with a tc of 34.1–44.9 h for the front drying room and 45.6 ± 6.0 °C with a tc of 56.3–96.6 h for the rear drying room. Both drying processes produced coffee beans with the NDs less than 11 qualified for Grade 1 with similar colour characteristics.

A field experiment was conducted in 2023 and 2024 to determine stage-specific crop coefficient values of okra (Abelmoschus esculentus) using the popular F1 hybrid Arka Nikita. Six evapotranspiration (ET_c) based treatments were applied: five under a forced ventilated greenhouse (T₁ = 120% ET_c, T₂ = 100% ET_c, T₃ = 80% ET_c, T₄ = 60% ET_c, T₅ = 100% ET_c in lysimeter) and one under open field (T₆ = 100% ET_c) arranged in a completely randomised block design with three replications. The results showed that T₂ had higher growth parameters, while T₄ and T₆ performed poorly. The yield was significantly higher in T₂ (23.8 t/ha in 2023 and 23.3 t/ha in 2024), whereas T₆ had a lower yield (9.5 t/ha in 2023 and 8.6 t/ha in 2024). Higher water productivity was observed in T₃ (9.85 kg/m³ in 2023 and 8.35 kg/m³ in 2024), while T₆ had lower water productivity (1.83 kg/m³ in 2023 and 1.35 kg/m³ in 2024). Hence, this study recommends using stage-specific crop coefficients of 0.32, 0.63, 0.78, and 0.41 during the initial, development, mid and final stages of 80% ET_c to optimise the water productivity and maximise the yield in the greenhouse-grown okra, respectively.

Moisture content regulation of root crops is crucial in post-harvest processing operations, not only in the price stipulation but also to avoid aflatoxin contamination. To prolong their storage life, they are processed into dried chips to extend their usability in feed formulations and starches. In this study, we use the capacitance-based method to evaluate the performance of an analogue-based electronic meter for the cassava, sweet potato, and taro chips. The meter was calibrated against the oven-drying method, yielding high R² values of the different root crops. The established calibration models were validated and revealed high R² values with 0.9580 for the cassava, 0.9958 for the sweet potato, and 0.9798 for the taro. The trendline equations are y = 59.44x^0.56, y = 54.38x^0.47, and y = 52.94x^0.62, respectively. The results revealed that the moisture meter is capable of reading the moisture content on a weight basis (% MC_wb) with accuracy and reliability at specified limits of 8% < x < 69% for the cassava, 15% < x < 59% for the sweet potato, and 9% < x < 57% for the taro. This study presents the performance of a portable analogue-based moisture meter as a reliable and accessible solution to small-scale operations, especially for farmers, offering an on-site rapid moisture content measurement in root crop processing.

Climate change is speeding up the melting and retreat of glaciers, which is a big threat to water security in dry and semi-dry areas like Uzbekistan. To understand how glaciers affect regional hydrological systems and to come up with adaptive water management strategies, it is important to keep an eye on how they change over time. This study examines the temporal changes of the Khisar Glacier in the Surkhandarya Basin by combining remote sensing data from different times with a GIS-based spatial analysis. We looked at Landsat images from 1990, 2000, 2010, and 2024 to see how the size of the glaciers has changed and how that relates to weather and water variables. The results show that the glacier area has shrunk significantly, from 8.6 km² in 1990 to 5.1 km² in 2024, a 40.7% decrease over the past three decades. The mean annual temperature in the basin rose by about 1.9 °C during the same time, and the Surkhandarya River's average summer discharge fell by about 22%. These results show how closely rising temperatures, melting glaciers, and lower river flow are linked. They also show how vulnerable glacier-fed water systems are to climate change. Combining satellite observations with climate and hydrology data is a good way to keep an eye on glaciers and assess water resources over time. The GIS-based monitoring framework created in this study provides useful tools for planning how to adapt to climate change and manage water resources in a way that is good for the environment in the Surkhandarya region and other glacier-dependent basins in Central Asia.

The tomato occupies a prominent place in the Philippines’ agricultural economy. However, tomato leaf diseases are challenges in tomato crop production leading to economic losses. Among the tomato leaf diseases, early blight and Septoria leaf spot are prevalent in the Philippines due to the climate. Thus, the accurate identification of diseases affecting tomato leaves is essential. Currently, a visual inspection is the primary method for diagnosing tomato leaf diseases which is time-consuming and inefficient. This study aims to develop a quantized Residual Network with convolutional 50 layer (ResNet-50) based model to classify tomato leaves as healthy or affected by Septoria leaf spot or early blight. Furthermore, to enhance the reliability of the models’ classification, gradient-weighted class activation mapping (Grad-CAM) was implemented. In contrast with the visual inspection, a programmed system does not get tired and can provide consistent performance results. As a result, the original 32-bit floating point model attained an accuracy rate of 91.22%. The quantized 16-bit floating point model demonstrated comparable performance with 90.10% accuracy with a 50% reduction in the model size and inference time of 0.3942 seconds. The minimal accuracy loss of the 16-bit model relative to the 32-bit model is due to the post-training quantization. The reduction to 16-bit precision is significant for the future deployment of edge devices where resources are limited.

This study optimised the drying process of kaffir lime leaves (Citrus hystrix DC.) to extend the shelf life and preserve the quality by reducing the moisture content. A Central Composite Design (CCD) under the Response Surface Methodology (RSM) framework was employed, with the temperature (55 °C, 60 °C, 65 °C) and drying time (5, 5.5, 6 h) as the variables. Experiments were conducted with a mechanical food dehydrator, and the parameters evaluated included the water content and drying yields. The optimal condition was identified as 60 °C for 5 h, resulting in a drying yield of 33.3% and a final moisture content of 7.153 (% w.b.), which meets the quality standards for dried products. The novelty of this research lies in the application of RSM to determine effective drying conditions for kaffir lime leaves, which are not widely used, but have good economic potential. The research proved that drying with the right conditions can greatly improve the quality and stability of dried kaffir lime leaves.

One of the main causes of having low crop efficiency in Peru is the poor management of water resources; which is why the main objective of this article is to estimate the amount of irrigation water required in quinoa crops through a comparison between the machine learning and AquaCrop models. For the development of this study, meteorological data from the province of Jauja and descriptive data of quinoa crops were processed and a simulation period was established from June to December 2020. From the simulation carried out, it was determined that the best model to predict the required irrigation water is the Adaptive Boosting (AdaBoost) model in which it was observed that the mean and standard deviation of the AdaBoost models (mean = 19.681 and SD = 4.665) behave similarly to AquaCrop (mean = 19.838 and SD = 5.04). In addition, the result of ANOVA was that the AdaBoost model has the best P-value indicator with a value of 0.962 and a smaller margin of error in relation to the mean absolute error (MAE) indicator with a value of 0.629. Likewise, it was identified that, for the simulation period of 190 days, 472.35 mm of water was required to carry out the irrigation process in red quinoa crops.

Soil water content is critical in plants’ morphological and physiological processes; therefore, water must always be available in appropriate quantities to meet plant growth needs. Soil moisture can be easily detected using sensors, which offer a practical solution for monitoring water content in the soil. However, using sensors for a long time, especially on agricultural land, will reduce sensor accuracy. This research aims to investigate the accuracy of soil moisture sensors during their use for cultivating crops. Using sensors in sandy clay soil can detect soil moisture levels with an accuracy of 93.80% and a precision of 90.81%. A reading deviation (error) of up to 49.74% with a precision level of 75.69% occurred when the sensor had been used for 40 days. Regular cleaning and calibration of the sensor are necessary to obtain accurate soil moisture readings. A copper-based sensor module kit can be used to detect soil moisture with reasonable accuracy during plant growth with a 5–6 weeks harvest time.

Ohmic heating is an emerging technology currently in high demand for application in various processes. In this research, a prototype of a multipurpose ohmic heating apparatus was successfully designed, fabricated, and tested. This apparatus was designed based on low-cost and versatile sensors and components available worldwide. Three independent chambers could be operated parallelly and individually with different treatments. Parameter data, i.e., voltage, electrical current, the temperature of heated material and environmental humidity-temperature, could be recorded by an embedded data logging system. The sensor had been tested and validated by comparing all the sensors used with commercial standard instruments. The result showed that all sensors had high measurement accuracy, indicated by very low mean absolute error (MAE) and mean absolute percentage error (MAPE), with R² > 0.999. The performance test revealed that product temperature could be accurately maintained according to the set point temperature with a deviation value lower than 0.1 °C. The data logging system was able to record and store the parameter data in SD card memory for up  to several days without interruption. The prototype of the ohmic heating apparatus could be  an effective alternative to process many purposes such as pasteurisation, cooking, warming, and fermentation based on the ohmic heating principle.

Banana quality is influenced by many factors, including variety and level of fruit ripeness. This quality can be evaluated from various points of view, one of which is the rheological consideration. Rheological properties are very important to study because they determine the design of equipment and processes, and minimise product damage. The aim of this research was to analyse and model the effect of variety and ripeness level on the rheological properties of banana fruit by applying a creep test. This research was carried out using a factorial experimental design 3 × 3 with 5 replications. Three varieties of banana, namely Ambon (Musa parasidiaca var. sapientum L. Kunt.), Raja (Musa parasidiaca L.), and Kepok (Musa acuminata balbisiana Colla) and each in three levels of ripeness, namely mature green, half ripe, and ripe. It was found that the parameters of the rheological properties of bananas changed according to the ripeness level (P < 0.05). The values of these rheological parameters decreased as the bananas ripened. Meanwhile, the variety and the interaction between variety and ripeness had no significant influence on the rheological parameters measured (P > 0.05). The constants of the four-element Burger model changed with the ripeness in all banana varieties. The Burger model with four elements could accurately predict the strain value of bananas tested in the creep test.

Agricultural machines that are fully autonomous will still need human supervisors to monitor and troubleshoot system failures. Recognising the emergency as soon as possible is crucial to reduce adverse effects. The ability of humans to detect visual, auditory, or tactile cues is usually enabled by warning systems. The effectiveness of different warning cues varies in terms of prompting a quick response. The study’s objective was to compare the effectiveness of two bimodal warnings (i.e., visual-auditory and visual-tactile) at eliciting supervisor perception (which equates to level one situation awareness). Twenty-five participants engaged in an autonomous sprayer simulation. Two realistic remote supervision scenarios (i.e., in-field and close-to-field) were used to examine two bimodal warning cues: (i) visual-auditory and (ii) visual-tactile. The effectiveness of each bimodal warning was assessed based on two measures: (i) response time and (ii) noticeability. There was no significant difference between the bimodal warning cues in terms of response time when tractor sound was present in the experimental environment (reflecting the in-field remote supervision scenario); however, visual-tactile cues yielded shorter response times than visual-auditory cues when the experimental environment was quiet (reflecting the close-to-field remote supervision scenario). There were no statistically significant differences between visual-auditory and visual-tactile warnings concerning noticeability. Participants’ subjective answers indicated they preferred the visual-tactile cues better than the visual-auditory cues. It is concluded that visual-tactile warnings are preferred over visual-auditory warnings to enable perception during remote supervision of autonomous agricultural machines (AAMs).

Cowpea, an annual legume widely grown and consumed in Nigeria, has been observed to contain between 27–33% impurities when freshly harvested and threshed. This poses a threat to humans when consumed and in large-scale agricultural processing. Therefore, this study is aimed at developing and evaluating the performance of a multi-variety cowpea cleaner (MVCC). Using standard methods, some selected engineering properties of the cowpea varieties were examined and used in the design of the MVCC. The cowpea had a moisture content of 8–14%, depending on the locations and varieties. Other properties investigated include the length, width, thickness, sphericity, geometric mean diameter, unit volume, arithmetic mean diameter, aspect ratio, surface area, unit weight, true density, terminal velocity, and angle of repose. The MVCC comprised the hopper, winnower, cleaning unit, fan assembly, and frame. The performance of the MVCC was also evaluated. The efficiency of separating good products of honey, drum, and Sokoto White beans was 95, 91, and 84%, respectively, while separating bad products was 87, 94, and 96%, respectively.

The rapid growth of the food industry necessitates rigorous quality control, particularly in egg production. This study explores advanced methodologies for egg quality assessment by integrating the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbour (KNN) with machine vision techniques. While traditional destructive methods like measuring the Haugh unit (HU) offer direct insights, but render eggs unusable, non-destructive techniques, such as imaging and spectroscopy, allow continuous quality monitoring. Over a 20-day period, egg samples were evaluated using a digital camera to capture key parameters like the albumen and yolk heights. The study’s image processing involved noise reduction, feature extraction, and calibration. The PCA captured 90.18% of the data variability, while LDA achieved 100% classification accuracy, and KNN demonstrated 80% accuracy. These findings underscore the effectiveness of combining machine vision with statistical methods to enhance the egg grading accuracy, contributing to consumer safety and industry standards.

Studded feed rollers are widely used in seed metering units of seed drills. The flow evenness is an important indicator of the performance of studded feed rollers. With this research, the effects of studded feed rollers with different stud numbers (27, 36, and 45 studs) on flow evenness were investigated both in the laboratory and by simulation in case of using different ground speeds (1.5, 2, and 2.5 m·s^–1). While the experiments were carried out on the seed drill model prepared in the laboratory, the simulation was done with the Rocky DEM software program. In the laboratory and simulation studies, it was determined that the flow evenness increased with the increase in the number of studs and the ground speed. The results obtained from the laboratory and simulation studies show parallelism with each other. However, it was seen that the results obtained in the laboratory were slightly higher than the results obtained from the simulation. With this study, it has been seen that it would be very beneficial to use the DEM model to improve the performance of the seed metering unit and to develop a new seed metering unit.

In Europe, the weather patterns require harvested grain crops to be dried before storage to prevent significant quality loss. The uneven movement of grains inside the drying equipment is a key issue affecting the drying process, causing under- or over drying the harvested crops and thus leading to quality degradation and ultimately to financial losses. To characterise the unevenness of material flow, we introduced a dimensionless displacement ratio. This dimensionless parameter was suitable for comparing the uniformity of the material movement processes within the dryer. Using experimental investigations and numerical simulations, we determined the effect of the lamella inclination angle, the friction between the grain-wall and grain-grain on the uniformity of the flow. The linear functions approximating the quantitative relationships were determined in all the cases. Our findings indicate a significant variation in the displacement ratio ξ corresponding to different lamella inclination angles and friction values demonstrating that the discrete element modelling approach provides further opportunities for determining the optimal operating parameters of mixed flow dryers.

The display of the depth of operation on tractors' dashboards facilitates the operator in achieving precise operation. In this study, the depth of operation of a mounted implement was measured and digitally displayed on a tractor's dashboard. The change in depth of operation was sensed by measuring the rotation of the rocker arm of the tractor's hydraulic system. The measured angle of rotation was multiplied by a calibration factor to convert it into the actual depth of operation in centimetres. For the calibration factor, a geometrical analysis of the three-point linkage was carried out, and a mathematical relationship was established based on the length of various linkages and their locations. A computer program was also developed to solve these equations to calculate the calibration factor. The program was validated with six different sizes of three-point linkages and found the maximum root mean square error was within 5%. The developed digital display was evaluated in the laboratory with three different implements and found a maximum error of ± 1 cm. A further evaluation was also carried out in an actual field with implements at three different depth levels, and a deviation of up to ± 13% was found with respect to the manual depth measurement.

The study aimed to optimise foam-mat drying parameters for producing purple-fleshed sweet potato (PFSP) powder. Egg albumin (EA) (5–15%), xanthan gum (XG) (0.1–0.5%), and drying temperature (50–70 °C) were used as independent variables for optimisation via Response Surface Methodology with a Box-Behnken design. The response variables (drying rate and anthocyanin content) were assessed by 18 treatments, which included 6 central points. The analysis of variance showed a high coefficient of determination (> 88%) between predicted and experimental values across all models. Optimal foam-mat drying conditions were 11.02% EA, 0.34% XG, and 65.1 °C to achieve the highest drying rate (2.49 g water.g dry matter^–1.min^–1) and anthocyanin content (1.01 mg.g^–1). After 3.5 h of drying at 65.1 °C, the foam-mat dried PFSP showed a low moisture content (4.35%) and water activity (0.29). Its water solubility index, water absorption index, rehydration ratio, total polyphenols, and antioxidant activity were determined to be 56.49%, 3.55%, 3.82, 3.66 ± 0.06 mg GAE.g^–1, and 58.49 ± 0.88%, respectively. Under these conditions, the powder maintained its natural beautiful and characteristic purple colour. The microstructure of the foam-mat dried PFSP powder (via SEM images) was also observed.

Indonesia, as a leading coconut producer, generates a substantial amount of coconut pulp from coconut milk extraction, which can be further processed into low-fat desiccated coconut. However, the drying process induces browning, reducing its whiteness and overall quality. Sodium metabisulphite is commonly used to prevent browning, but excessive use raises health concerns. In this study, the optimisation of the drying temperature and sodium metabisulphite concentration was conducted to minimise the browning while maintaining the quality of low-fat desiccated coconut. Using Response Surface Methodology (RSM) with a Central Composite Design (CCD), responses such as the browning index (BI), whiteness index (WI), moisture content, yield, free fatty acids (FFAs), ash, fat, protein, total phenolic content (TPC), and crude fibre were examined. The results showed that both the drying temperature and sodium metabisulphite concentration significantly influenced the physicochemical properties. The optimal conditions were identified at 62.505 °C and 380.059 ppm sodium metabisulphite, resulting in a whiteness index of 87.219, browning index of 5.1025, yield of 43.125%, moisture content of 2.3%, and free fatty acid content of 4.45%. These findings highlight an effective strategy for reducing the additive dependency while maintaining the physicochemical quality of low-fat desiccated coconut.