Abstract Accurate and efficient coffee bean grading is crucial for ensuring consistency, quality control, and standardization in the coffee industry. However, traditional manual methods are time-consuming, subjective, and costly. Deep learning approaches, particularly convolutional neural networks (CNNs), have shown remarkable performance in image classification tasks, offering a promising solution for automated coffee bean grading. However, these models encounter significant challenges due to the inherent characteristics of coffee beans, including their small size, limited visual features, and lack of texture. This study aims to address this challenge by comparatively analyzing various CNN models to identify the most effective architecture for automatic coffee bean grading. Specifically, we evaluate the performance of ten models: DenseNet, MobileNet, Inception, InceptionResNet, ResNet50, ResNet101, ResNet152, VGG16, VGG19, and Xception. Our experimental results demonstrate that DenseNet achieves the highest accuracy of 0.989, followed by MobileNet and ResNet152 with 0.982 and 0.980 accuracy, respectively. DenseNet has the highest precision and F1 score among all the models, with a precision of 0.996 and an F1 score of 0.992. VGG19 has the lowest accuracy of 0.902 and the lowest F1 score of 0.899. Overall, our analysis reveals that DenseNet, MobileNet and ResNet152 outperform other models for coffee bean grading accuracy. The findings of this study can contribute to the development of more accurate and efficient coffee bean grading systems that can benefit the coffee industry.

Abstract Mangroves forest play a significant role in reducing tropical carbon emissions and preventing climate change. The objectives of this study are to estimate the aboveground, belowground, and soil carbon storage in mangrove forests. This study was conducted in a mangrove forest in Pitas, Sabah. A transect method for sampling design was used with a total of 3 transects and 15 sub-transects. Forest inventory was done to get the diameter breast height of standing trees and soil sampling with four different depths (0 - 15 cm, 15 - 30 cm, 30 - 50 cm and 50 - 100 cm) were taken for soil analysis and bulk density. Allometric equation was used to calculate aboveground and belowground biomass then its carbon stock was estimated as 50% from its total biomass. CHNS elemental analyzer was used to determine the soil carbon content. A total of 223 individual trees were measured with DBH classification. The AGB and BGB on the study site were 204.53 Mg/ha and 68.18 Mg/ha and estimated the carbon is 50% of the biomass which is AGC 102.26 Mg/ha and BGC 34.09 Mg/ha. The bulk density of the soil ranges from 1.03 - 1.11 g/cm3 and the soil carbon concentration from 15 - 30 cm depth shows the highest with 3.25%. The soil carbon shows the highest carbon storage in the total ecosystem carbon storage with 313.87 Mg/ha. this study reveals that the total carbon stock in mangrove forests at Pitas, Sabah, Malaysia, amounted to 450.22 Mg/ha which soil carbon contributes 69% of total carbon storage.

Abstract This paper obtains analytical solutions for the Schrodinger equations (SEs) using variants of the differential transform method (DTM). The solutions produced by two-dimensional DTM (2D-DTM), reduced DTM (RDTM), and multistep RDTM (MsRDTM) were observed. The outcomes show that the MsRDTM generated more highly accurate solutions to SEs than the 2D-DTM and RDTM. The solutions also show that the MsRDTM is straightforward to use, saves a significant amount of computing work when solving SEs, and has potential for broad application in other complex partial differential equations. Graphical representations are presented to illustrate the different effectiveness and accuracy of the variants of DTM.

Abstract Octomeles sumatrana is a fast-growing indigenous commercial timber species commonly used in enrichment planting in Sabah. The high production corridor (HPC) is an extensive network of forest areas designated for restoration through enrichment planting to fill in forest gaps created during harvesting. The objectives of the paper to assess the early survival and growth performance of O. sumatrana in a spacing and fertiliser trial on a severely degraded forest site. The RCBD trial was established with three block replicates (spacing) and three plot replicates (fertiliser) was established. Six months after planting, the height, collar diameter and survival of seedlings were determined. The two-way ANOVA indicated that the mean height and collar diameter of O. sumatrana differed significantly by both spacing treatment (F(3)=18.73, p< 0.001), (F(3)=15.198, p< 0.001), respectively and by spacing x fertiliser treatments (F(6)= 3.19, p< 0.00471), (F(6)= 3.51, p< 0.00231), respectively. However, the fertiliser treatments were not significantly different (p> 0.05). Tukey’s HSD Test for multiples comparisons found that the mean value of mean height of O. sumatrana was significantly different between spacing S1 and S2 (+ 45.73 cm), between spacing S1 and S3 (+ 78.09 cm), and spacing S1 and S4 (+40.22 cm). Mean collar diameter of O. sumatrana was significantly different between spacing (S1) and S2 (+ 10.21 mm), between spacing S1 and S3 (+ 18.52 mm), and spacing S1 and S4 (+9.77 mm). The mean of mean height of O. sumatrana was significantly different between spacing x fertiliser treatments S1F2 and S1F1 (+ 14.02 cm), and S1F2 and S1F3 (+ 50.59 cm). Mean collar diameter of O. sumatrana was significantly different between spacing x fertiliser treatments S1F2 and S1F1 (+ 5.91 mm), and S1F2 and S1F3 (+ 13.57 mm). There was a positive correlation (r = 0.879) and statistically significance (p < 0.01) between the mean height and collar diameter of O. sumatrana. The highest mean survival of O. sumatrana's was (92.59% ± 26.69), the highest mean height and mean collar diameter were (206.12 ± 68.16 cm) and (43.38 ± 18.82 mm) in the S1F2 treatments, respectively. The early growth performance of O. sumatrana present promising data for forest rehabilitation particularly within open gaps exposed to sunlight in forested areas.

Abstract The Soil-Water Characteristic Curve (SWCC) serves as a fundamental tool for investigating unsaturated soils and comprehending the relationship between soil water content and properties. This study analyses the SWCC for silt clay soil from the Garinono Formation in Sandakan, Sabah. Field sampling and laboratory tests were conducted to gather the soil's physical properties and SWCC data, representing the study area. The primary findings illuminate the unsaturated behaviour of the soil within this formation, providing valuable insights into its water retention capabilities. Through rigorous laboratory testing, the SWCC data reveal how the volumetric water content (VWC) changes concerning varying suction conditions (matric potential). The analysis indicates that the SWCC measured data are best represented by the model, encompassing the entire range of suction from lower to higher values. The findings underscore significant variations in the SWCC shape based on the bulk density of the soil samples, a crucial indicator of mechanical properties such as compaction and strength. Additionally, the study discusses the direct impact of soil composition and porosity on the SWCC, deepening understanding of their interrelationships. The study's outcomes hold implications for diverse fields, including geotechnical engineering, agriculture, soil science, and environmental science.