The study analyzed the effect of diffusion path lengths (initial, average, and final half-thicknesses) on the shrinkage, effective moisture diffusivity, activation energy, and pre-exponential factor of thin-layer red delicious apple slices under convective drying conditions (temperature from 40 degrees C to 80 degrees C at 10 h drying time). The results show that the shrinkage increased from 31.09% at 40 degrees C to a maximum of 42.65% at 70 degrees C, then slightly decreased to 36.77% at 80 degrees C, indicating that shrinkage did not increase linearly with drying temperature. The diffusion path lengths yielded effective moisture diffusivities ranging from 1.43 & times; 10-10 to 10.31 & times; 10-10 m2/s, with the average characteristic length providing the most realistic representation of the effective moisture diffusivity. The high coefficient of determination (R2 = 0.965), consistent with the model efficiency value, confirms that the Arrhenius model fits the experimental diffusivity data across the temperature range studied. The mean absolute percentage error of 12% between the experimental and predicted diffusivities confirms the reliability of Fick's and Arrhenius models. The activation energy ranged from 21.56 to 26.03 kJ/mol across diffusion path lengths, indicating a moderate sensitivity of moisture diffusion to temperature.

A continuous, temperature-dependent description of the transient mechanical behaviour of bulk oilseeds under compression is missing. Therefore, the transient uniaxial mechanical behaviour of bulk rapeseed under different temperatures ranging from-20 degrees C to 80 degrees C is investigated. The samples of bulk rapeseed were measured at a pressing height of 70 mm using a pressing vessel with a diameter of 60 mm and a plunger. The samples were compressed with a maximum compressive force of 100 kN and a speed of 10 mm min-1. The determined parameters namely oil yield, deformation energy and unit energy were described theoretically using the Verhulst model. The obtained oil samples at the different temperatures were analysed for several oil quality indicators, namely, the peroxide value, acid value and free fatty acid. Absorbance spectral curves in the wavelength range between 300 and 650 nm were described. Based on the Verhulst model parameters, four distinct regions were characterised under the temperatures as region I (-20-0)degrees C where oil yield decreased significantly, region II (0-20)degrees C where oil yield increased significantly, region III (20-60)degrees C where an increase in oil yield was associated with lower energy demand and region IV (60-80)degrees C where oil yield increased slightly with lower energy demand. A mathematical description of this behaviour provides a fundamental step towards optimising the entire pressing process. In addition, such knowledge provides a basis for more accurate process control and for the development of digital twins in oilseed pressing.

Modelling of the food drying process is dependent on the understanding of the complex moisture transport mechanisms. This study analyzed the effect of drying temperatures ranging from 40 to 80 °C and diffusion path lengths (initial, average and final half-thicknesses) on the shrinkage, effective moisture diffusivity and activation energy of thin-layer red delicious apple samples under convective drying. Fick’s second law and Arrhenius model were utilized to determine the effective moisture diffusivity and activation energy. The mean shrinkage increased from 31.09% at 40 °C to a maximum of 42.65% at 70, then slightly decreased to 36.77% at 80 °C, indicating that shrinkage does not increase linearly with drying temperature. The initial, average and final half-thicknesses yielded effective moisture diffusivities ranging from 1.43×10–10 m2/s to 1.03×10–09 m2/s, with the average dimension providing the most realistic representation of the effective moisture diffusion path during drying. The linear regression models between the natural logarithm of the moisture ratio and drying time showed a strong fit with R2 values ranging from 0.9955 to 0.9971, confirming the reliability of Fick’s second law for describing the effective moisture diffusivity. The mean activation energy ranged from 21.56 to 26.03 kJ/mol across the different characteristic lengths, indicating the minimum energy requirement for moisture diffusion in red delicious apple samples during the convective drying.

This study evaluated vacuum drying (VD) and infrared drying (IRD) methods for persimmon, including individual and two-step sequential processes (VD followed by IRD and vice versa). The combined drying strategies were selected to harness the rapid surface heating of IRD and the low-temperature, low-oxygen benefits of VD, aiming to overcome limitations of single drying methods such as extended drying times and nutrient degradation. Drying experiments were conducted using laboratory-scale equipment at 50-70°C, for VD, with a vacuum pressure of 50 mbar (absolute pressure) and a pump speed of 2 L/s. Results showed a significant effect of drying combination strategies on drying rate, duration, effective moisture diffusivity, shrinkage, activation energy, color characteristics, microstructure, and phytochemical constituents of persimmon. The shortest drying times were recorded for IRD (240 min), followed by VD + IRD (343 min) and IRD + VD (376 min), whereas VD required the longest (520 min). Effective moisture diffusivity ranged from 1.42 × 10-9 m2/s for VD at 50°C (VD-50) to 7.83 × 10-9 m2/s for IRD at 70°C (IRD-70), with both individual IRD-70 and a combination of IRD + VD demonstrating improved moisture transfer performance. The IRD + VD combination resulted in the best microstructure preservation and showed lower shrinkage compared to other drying strategies. Moreover, this combination best preserved the persimmon color with the lowest total color change (ΔE = 5.591), whereas VD showed the highest (ΔE = 35.875). Activation energy was lowest in IRD + VD (13.98 kJ/mol), followed by VD + IRD (18.61 kJ/mol), with higher values in VD (34.08 kJ/mol) and IRD (22.75 kJ/mol). Phytochemical analysis showed IRD (total phenolic content [TPC] = 35.79 mgGAE/g, total flavonoid content [TFC] = 54.83 mgQE/g) and IRD + VD (TPC = 17.02 mgGAE/g, TFC = 58.52 mgQE/g) retaining the highest bioactive compounds. This study contributes to optimizing drying techniques for persimmon, enhancing energy efficiency, preserving nutritional quality, and supporting sustainable food processing, making it relevant for the food industry, food engineering, and food science fields.

Access to clean water remains a critical global challenge, particularly in under-resourced regions. This study introduces an autonomous water treatment system leveraging Industry 4.0 technologies, including advanced smart sensors, real-time monitoring, and automation. The system employs a multi-stage filtration process—mechanical, chemical, and UV sterilization—to treat water with varying contamination levels. Smart sensors play a pivotal role in ensuring precise control and adaptability across the entire process. Experimental validation was conducted on three water types: pond, river, and artificially contaminated water. Results revealed significant reductions in key contaminants such as PPM, pH, and electrical conductivity, achieving water quality standards set by the WHO. Statistical analyses confirmed the system’s reliability and adaptability under diverse conditions. These findings underscore the potential of smart, sensor-integrated, decentralized water treatment systems to effectively address global water security challenges. Future research could focus on scalability, renewable energy integration, and long-term operational durability to enhance applicability in remote areas.

Sago (Metroxylon spp.), a traditional staple food that naturally grows in Papuan forests, plays a vital role in the food security of local inhabitants and is equally important in food and non-food industries. Changes in forest cover to other land uses might lead to shifts in the sago ecosystem, which could also affect sago production and nutrients. Currently, there is a lack of studies correlating vegetation changes and nutrient profiles. This research article aims to explore the vegetation area changes and their potential relationship to the sago forest ecosystem and nutrient profiles of the sago. NDVI information was collected from Mappi and Merauke Regency, Papua Province of Indonesia in 1990, 1996, 2012, 2018, and 2020. Sago samples were gathered from selected sites in Mappi and Merauke. No statistically significant changes in NDVI degradation classes or sago habitat area classifications were observed over the years in each regency. NDVI degradation classes in Merauke showed a significantly higher proportion of degraded areas (>76%) and a more pronounced yellowish color than in Mappi (8%). Approximately 90% of areas in Mappi were categorized as having an increase in trees and no degradation, which was significantly higher than in Merauke (<5%). Sago in Merauke exhibited significantly higher macro and micronutrient content than Mappi. Findings from GLM predictor analysis showed that higher carbohydrate, protein, Ca, Cu, Mg, and Na content of sago samples were positively and significantly associated with collected samples in Merauke. An increase in carbohydrate levels was also positively associated with a higher percentage of NDVI classes related to the yellowish color. The results indicated that sago palms in Merauke are more mature, and therefore, the samples were harvested at an older age than those in Mappi. NDVI can be used to monitor area degradation and predict nutritional quality.

This study optimized the input processing factors, namely compression force, pressing speed, heating temperature, and heating time, for extracting oil from desiccated coconut medium using a vertical compression process by applying a maximum load of 100 kN. The samples’ pressing height of 100 mm was measured using a vessel chamber of diameter 60 mm with a plunger. The Box–Behnken design was used to generate the factors’ combinations of 27 experimental runs with each input factor set at three levels. The response surface regression technique was used to determine the optimum input factors of the calculated responses: oil yield (%), oil expression efficiency (%), and energy (J). The optimum factors’ levels were the compression force 65 kN, pressing speed 5 mm min−1, heating temperature 80 °C, and heating time 52.5 min. The predicted values of the responses were 48.48%, 78.35%, and 749.58 J. These values were validated based on additional experiments producing 48.18 ± 0.45%, 77.86 ± 0.72%, and 731.36 ± 8.04 J. The percentage error values between the experimental and the predicted values ranged from 0.82 ± 0.65 to 2.43 ± 1.07%, confirming the suitability of the established regression models for estimating the responses.

The present study examined the percentage oil output, energy and mechanical properties of selected bulk oil-seeds namely pumpkin, hemp, sesame, milk thistle, cumin and flax by a uniaxial compression process of a maximum load capacity of 500 kN and a preset speed of 5 mm/min. Each sample was measured at 60 mm pressing height with a plunger using the pressing vessel of diameter 60 mm. The results show that milk thistle seeds required the highest force corresponding to the highest stress and energy demand for recovering the oil in both the bulk oilseeds and seedcakes. However, pumpkin seeds produced the maximum residual oil yield of 24.99 +/- 0.04%, followed by sesame seeds at 21.29 +/- 1.82% and then flax seeds at 22.61 +/- 0.31%. The study revealed that higher energy is required to produce the maximum oil yield with minimum residual oil in the seedcake by continuous pressing.

In our study location, Merauke Regency, the easternmost city in Indonesia, the sago palm is associated with different types of ecosystems and other non-sago vegetation. During the harvesting season, the white flowers blossoming between the leaves on the tops of palm trees may be distinguished manually. Four classes were determined to address the visual inspections involving different parameters that were examined through the metric evaluation and then analysed statistically. The computed Kruskal-Wallis test found that the parameters vary in each network with a P-value of 0.00341, with at least one class being higher than the others, i.e., non-sago with a P-value of 0.044 with respect to precision, recall, and F1-score. Thus, the general linear model (GLM) was tested specifically in trained Network-15 and Network-17, which have similar parameters except for the batch size. It indicated the two networks' differences based on their prediction results, classes, and actual images. Accordingly, a combination of learning rate (Lr) and batch size improved the reliability of the training and classification task.

This study addresses the question of how to evaluate the growth stage of food crops, for instance, paddy (Oryza sativa) and maize (Zea mays), from two different sensors in selected developed areas of Papua Province of Indonesia. Level-1 Ground Range Detected (L1 GRD) images from Sentinel-1 Synthetic Aperture Radar (SAR) data were used to investigate the growth of paddy and maize crops. An NGB camera was then used to obtain the Green Normalized Difference Vegetation Index (GNDVI), and the Enhanced Normalized Difference Vegetation Index (ENDVI) as in situ measurement. Afterwards, the results were analyzed based on the Radar Vegetation Index (RVI) and the Vertical-Vertical (VV) and Vertical Horizontal (VH) band backscatters at incidence angles of 30.55°–45.88°, and 30.59°–46.16° in 2021 and 2022, respectively. The findings showed that Sigma0_VV_db and sigma0_VH_db had a strong correlation (R2 above 0.900); however, polarization modification is required, specifically in the maize field. The RVI calculated and backscatter changes in this study were comparable to the in situ measurements, specifically those of paddy fields, in 2022. Even though the results of this study were not able to prove the RVI values from the two relative orbits (orbit31 and orbit155) due to the different angle incidences and the availability of the Sentinel-1 SAR data set over the study area, the division of SAR image data based on each relative orbit adequately represents the development of crops in our study areas. The significance of this study is expected to support food crop security and the implementation of development plans that contribute to the local government’s goals and settings.

This study uses rheological models to describe the mechanical behaviour of oil palm empty fruit bunches (EFB) under compression loading. The oil palm empty fruit bunches were obtained from North Sumatra, Indonesia. The rheological models for different fraction sizes of the mechanical behaviour under compression loading were developed based on a mathematical concept involving spring and dashpot components. The dependencies between fraction size, viscosity, and modulus elasticity were determined and mathematically described for each branch of the rheological model. The general rheological model was developed based on the defined dependencies, considering the deformation and fraction sizes. The determined rheological models and their components could be used as a fundamental building block of digital twins of oil palm empty fruit bunches, and they could be used to optimise the compressing technology and increase the efficiency of the entire pressing process.

In this present study, an oil press was used to process 200 g each of sesame, pumpkin, flax, milk thistle, hemp and cumin oilseeds in order to evaluate the amount of oil yield, seedcake, sediments and material losses (oil and sediments). Sesame produced the highest oil yield at 30.60 ± 1.69%, followed by flax (27.73 ± 0.52%), hemp (20.31 ± 0.11%), milk thistle (14.46 ± 0.51%) and pumpkin (13.37 ± 0.35%). Cumin seeds produced the lowest oil yield at 3.46 ± 0.15%. The percentage of sediments in the oil, seedcake and material losses for sesame were 5.15 ± 0.09%, 60.99 ± 0.04% and 3.27 ± 1.56%. Sediments in the oil decreased over longer storage periods, thereby increasing the percentage oil yield. Pumpkin oil had the highest peroxide value at 18.45 ± 0.53 meq O2/kg oil, an acid value of 11.21 ± 0.24 mg KOH/g oil, free fatty acid content of 5.60 ± 0.12 mg KOH/g oil and iodine value of 14.49 ± 0.16 g l/100 g. The univariate ANOVA of the quality parameters against the oilseed type was statistically significant (p-value < 0.05), except for the iodine value, which was not statistically significant (p-value > 0.05). Future studies should analyze the temperature generation, oil recovery efficiency, percentage of residual oil in the seedcake and specific energy consumption of different oilseeds processed using small-large scale presses.

This present study investigated thin-layer drying characteristics of dried apple slices for a range of temperatures from 40 °C to 80 °C at a constant drying time of 10 h under infrared (IR) and hot air oven (OV) drying methods. The fresh apples were cut into a cylindrical size of thickness of 8.07 ± 0.05 mm and a diameter of 66.27 ± 3.13 mm. Fourteen thin-layer mathematical models available in the literature were used to predict the drying process. The goodness of fit of the drying models was assessed by the root mean square error (RMSE), chi-square (χ2), coefficient of determination (R2) and modelling efficiency (EF). The results showed that the lightness and greenness/redness of the dried sample, total colour change, chroma change, colour index, whiteness index, bulk density, final surface area and final volume significantly (p-value < 0.05) correlated with the drying temperature under IR. Under OV, however, only the final surface area and bulk density of the dried samples showed significant (p-value < 0.05) with the drying temperature. Shrinkage values for OV and IR methods showed both increasing and decreasing trends along with the drying temperatures. The Weibull distribution model proved most suitable for describing the drying processes based on the statistical validation metrics of the goodness of fit. In future studies, the combined effect of the above-mentioned drying methods and other drying techniques on apple slices among other agricultural products should be examined to obtain a better insight into the drying operations and quality improvement of the final product for preservation and consumer acceptability.

The aim of this research was to evaluate the effect of untreated and 5% aqueous NaOH solution-treated filler of the plant Jatropha Curcas L. on the mechanical properties of adhesive bonds, especially in terms of their service life at different amplitudes of cyclic loading. As a result of the presence of phorbol ester, which is toxic, Jatropha oilseed cake cannot be used as livestock feed. The secondary aim was to find other possibilities for the utilization of natural waste materials. Another use is as a filler in polymer composites, that is, in composite adhesive layers. The cyclic loading of the adhesive bonds was carried out for 1000 cycles in two amplitudes, that is, 5–30% of the maximum force and 5–50% of the maximum force, which was obtained by the static tensile testing of the adhesive bonds with unmodified filler. The static tensile test showed an increase in the shear strength of the adhesive bonds with alkali-treated filler compared to the untreated filler by 3–41%. The cyclic test results did not show a statistically significant effect of the alkaline treatment of the filler surface on the service life of the adhesive bonds. Positive changes in the strain value between adhesive bonds with treated and untreated filler were demonstrated at cyclic stress amplitudes of 5–50%. SEM analysis showed the presence of interlayer defects in the layers of the tested materials, which are related to the oil-based filler used.

Vegetable oils represent an important element in protecting a sustainable environment. The pursuit of environmentally friendly solutions and the ever-increasing costs of synthetic oil production are increasing the interest in natural vegetable oils. This paper presents and discusses the possibilities of using the oils obtained from coconuts (Cocos nucifera L.) harvested in Indonesia (North Sumatra region), with three maturity levels (green, yellow, and brown), as lubricants. The specific mechanical energy for linear pressing of the green, yellow, and brown types was 22.3, 20.7, and 18.5 J·goil−1, respectively. The water content of the oils obtained from the green, yellow, and brown types was 1786, 2033, and 1902 mg H2O·g−1, respectively. The mathematical models for linear pressing were established. The sizes of the wear area for the green, yellow, and brown types were 25.7, 24.4, and 34.3 mm2, respectively. The UV–visible spectral curves of the oils, in the range of 180–320 nm, were determined. The results of the lubrication properties of the Reichert test showed that better lubrication properties were exhibited by the green and yellow types, which are comparable to the lubricating properties of engine oils. The results from the SEM images also showed a better structure of the worn surface and fewer traces of abrasive wear.

Maize ( Zea mays L. ) is one of the essential agricultural products in Papua Province of Indonesia, specifically in the three largest maize producing regions, namely Nabire Regency, Biak Numfor Regency and Merauke Regency, with the number of productions of 991 tons, 764 tons, and 751 tons respectively in 2015. Unfortunately, since 2016 the secondary data on food crops productivity, including maize, has not been provided yet in the provinces statistical report, due to manual estimation methods, i.e., visual estimation. On the other side, the number of populations in this Province has a slight increase, from 2.97 million people in 2012 to 3.38 million in 2019. Further, approximately 1.20 million people are employed in the agricultural sector. Considerable population growth will intensify the demand for food stock and other utilization of food crops in this region; hence, relevant research in food crops needs to be considered. One of the dominant factors in the yield potential of maize is plant height, since it is associated with fertilizer, seed, and soil treatment and predicts yield area. Therefore, this study aims to analyse the plant height, particularly maize plant based on a digital surface model (DSM) derived from Unmanned Aerial Vehicle (UAV) Red Green Blue (RGB) images. The crop was monitored during the second and third week of January 2022 and then, processed using pix4d Mapper software to produce the DSM, Digital Terrain Model (DTM), and orthomosaic. Then, the Geographical Information System (GIS) software, and an open-source software, namely Python were used to estimate the plant height. Next, the results were assessed statistically to examine the validation, the strong correlation coefficient of the estimation to the actual height that obtained from UAV and ground-based plant height data. The findings will help to support the prior decision support on estimation of maize production in Papua Province.

Sago palm tree, known as Metroxylon Sagu Rottb, is one of the priority commodities in Indonesia. Based on our previous research, the potential habitat of the plant has been decreasing. On the other hand, while the use of remote sensing is now widely developed, it is rarely applied for detection and classification purposes, specifically in Indonesia. Considering the potential use of the plant, local farmers identify the harvest time by using human inspection, i.e., by identifying the bloom of the flower. Therefore, this study aims to detect sago palms based on their physical morphology from Unmanned Aerial Vehicle (UAV) RGB imagery. Specifically, this paper endeavors to apply the transfer learning approach using three deep pre-trained networks in sago palm tree detection, namely, SqueezeNet, AlexNet, and ResNet-50. The dataset was collected from nine different groups of plants based on the dominant physical features, i.e., leaves, flowers, fruits, and trunks by using a UAV. Typical classes of plants are randomly selected, like coconut and oil palm trees. As a result, the experiment shows that the ResNet-50 model becomes a preferred base model for sago palm classifiers, with a precision of 75%, 78%, and 83% for sago flowers (SF), sago leaves (SL), and sago trunk (ST), respectively. Generally, all of the models perform well for coconut trees, but they still tend to perform less effectively for sago palm and oil palm detection, which is explained by the similarity of the physical appearance of these two palms. Therefore, based our findings, we recommend improving the optimized parameters, thereby providing more varied sago datasets with the same substituted layers designed in this study.

The present study aims to estimate the maximum oil yield of hulled sunflower seed samples in a uniaxial process under a load of 40 kN and speed of 4 mm/min. The oil samples were assessed for their quality parameters and spectra curves within the wavelength range of 325–600 nm. The results show that heating temperatures in the range of 40 °C to 80 °C increased the oil output; however, a maximum oil yield of 48.869 ± 6.023% with a minimum energy of 533.709 ± 65.644 J at the fifth repeated pressing was obtained from the unheated sample compared to the heated samples. The peroxide values ranged from 6.898 ± 0.144 to 7.290 ± 0.507 meq O2/kg, acid values from 1.043 ± 0.166 to 1.998 ± 0.276 mg KOH/g oil and free fatty acid values from 0.521 ± 0.083 to 0.999 ± 0.138 mg KOH/g oil, which were within the recommended quality threshold. There were significant spectral differences among the oil samples. A single absorbance peak was observed at 350 nm for all oil samples, indicating low levels of pigment molecules in the oil. The study revealed the need for repeated pressings to recover the considerable residual oil remaining in the seedcake after the first pressing.