Research in Agricultural Engineering - In Press
Investigation of Drying Kinetics of Residual Nuts Mixture Using Mathematical Models and Artificial Neural NetworksOriginal Paper
Tran Huu Duy, Nguyen Doan Kim Dang, Dang Nguyen Gia Han, Pham Tran Thanh Vy, Tran Ngoc Giau, Hong Van Hao, Nguyen Minh Thuy, Vo Quang Minh, Ngo Van Tai
The objective of this study was to evaluate the potential for sustainable reuse and the value of residual nuts mixture (RNM) byproducts (cashew nut, peanut, and soybean) after extraction. To investigate the drying kinetics, the RNM was dried at various temperatures (50 to 80 °C). The Balbay and Şahin model, which had a high coefficient of determination (R2) of 99.62–99.96%, a low root mean square error of 0.007–0.021, and χ2 of 0.001–0.005, was the one that best fit the experimental data out of the eight mathematical models that were used. Artificial neural networks showed higher and faster prediction capacity than mathematical models. The effective moisture diffusion coefficient (Deff) increased gradually with temperature, with an activation energy (Ea) of 13.67 kJ×mol-1. The RNM powder produced by the optimal drying process (60 °C for 3.75 h) has a bright color, high polyphenol content (2.68 mgGAE×g-1) and antioxidant activity, low moisture content (4.9%) and relatively high nutritional value, especially protein (27.27%), lipid (40.19%), and fiber (4.2%). Under these conditions, not only is efficient drying and preservation achieved, but the quality of the by-product powder is also maintained.
Thin-layer drying kinetics and quality assessment of octopus (Octopus sp.) using mixed and open solar dryersOriginal Paper
Arina Fatharani, Yuwana Yuwana, Faulina Maissy, Firmansyah Firmansyah, Hilda Maya Sintia Dewi, Ulfah Anis, Fitri Yuwita
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 (R² = 0.9965; RMSE = 0.0168; SSE = 0.0058). Significant interaction effects between anatomical region and drying method were observed for browning and ash content (p
Quality Classification of Mangosteen using Image Processing and Machine LearningOriginal Paper
Usman Ahmad, Mardison Suhil
Mangosteen is one of important fruits for Indonesia with a good economic value both in domestic and international markets. Mangosteen has a soft and watery white flesh, consumed when the fruit is fully ripe, fresh with dark red color of peel and sometimes almost purple. For export quality grade, beside its tandard size and ripening stage, additional citeria are required by market such as the calyx with normal form and green color are necessary. Normal form and green color of calyx has no direct connection to internal quality of mangosteen, however it important for international market because they reflect the fruit freshness and esthetic value. The objective of this research is to develop quality classification of mangosteen into three classes; export, supermarket and others markets. Random Forest and Extreme Gradient Boosting models of machine learning were trained and validated using 300 samples using 26 image features, and then repeated using only 10 and 5 selected features for more robust classifier. The results of classification using all features were 90.0% and 86.7%, 86.7% and 85.0% using 10 selected features, and 86.7% and 75.0% using 5 selected features for Random Forest and Extreme Gradient Boosting respectively.
The Auto Sprinkler Rover: An Innovative Fertilizer Applicator for Sustainable Agriculture Original Paper
Wan khaima azira Wan mat khalir, ISMAIL AZIMAH, Nur Maizatul Akma Abdul Aziz (email: nurmaizatulakma011@ gmail.com)
Fertilizers are essential to agriculture since they provide crucial nutrients that stimulate crop growth, increase yield and improve yield quality. However, this practice requires the use of manual labour, which consumes energy and time as well as limits the effectiveness of fertilization methods and is environmentally harmful. These factors have led to the development of an innovative product based on the Internet of Things (IoT) called the Auto Sprinkler Rover, which is a remote controlled machine that operates automatically. It contains a 12 L water barrel that serves as a storage tank for liquid fertilizer. This study had designed a liquid sensor and light emitting diode to alert the user to the amount of liquid in the tank. The water pump facilitates the smooth spraying of liquid fertilizer from the pipe’s nozzle. Auto Sprinkler Rover differs from conventional methods because it integrates real-time monitoring via Wi-Fi, an automatic liquid level sensor as well as an automatic spraying system and ergonomic design that is manually operated. The Auto Sprinkler Rover was designed to be a cutting-edge technology that benefits sustainable crop cultivation for households, communities and the global agriculture industry.
Effect of maturity and colour parameters on cocoa opening force using the existing mechanical systemOriginal Paper
Amuaku Randy, Eric Amoah Asante, Emmanuel Y.H Bobobee, Jerry Oppong Adutwum, Godwin Amanor
The study assessed the mechanical and colourimetric properties of cocoa (Theobroma cacao L.) pods at various maturity stages to improve mechanised pod-opening efficiency. Thirty pods, each replicated three times, underwent compressive loading and colour analysis using the CIE Lab* colour space to correlate colour attributes with mechanical resistance. Pod maturity significantly (p < 0.05) influenced the opening force and compressive strength: unripe pods required the highest mean force (1,222 N) and strength (0.316 N.mm-2), while aged pods needed only 346 N and 0.094 N.mm-2. Transverse orientation yielded higher and more consistent force responses than longitudinal orientation, with ANOVA confirming significant differences (p < 0.05). Colour parameters, especially b* (yellowness), chroma (C), and hue angle (H°), were strongly and negatively correlated with mechanical properties (r ≥ –0.99, p ≤ 0.05), making them reliable non-destructive indicators of maturity. Partial Least Squares Regression (PLSR) models validated the predictive power of combined colour and mechanical data, with longitudinal orientation producing the highest model accuracy across all maturity levels. Combining colour-based maturity assessment with mechanical testing provides a robust framework for designing automated, maturity-sensitive cocoa pod-opening systems that optimise efficiency and reduce bean damage during postharvest handling. The research approach provided an excellent outstanding quantitative assessment.
Vetiver Grass for Road Slope Protection through Soil Erosion and Runoff Reduction in a Haor Area of Sunamganj DistrictOriginal Paper
Tahmina Akter, Saumik Acharya, Sayed Jaber Al-Hossain, Golam Kibria Robbani
Erosion control on slopes and embankments is a global concern, with vetiver planting emerging as an effective solution. This study investigated vetiver grass as a potential alternative for reducing runoff and soil erosion in protecting road embankments. In a small-scale model on a 1V:2H slope, simulated rainfall was applied through a perforated sheet. Runoff was collected, and eroded soil samples were dried using the sand bath method. Runoff and soil loss were compared with and without vetiver grass. Results showed that vetiver grass significantly reduced soil erosion, preventing about 87% more soil loss than bare soil. During 60 minutes of simulated rainfall, the bare soil plot lost per unit area is 27.196 kg.m-2, whereas the vetiver-covered plot lost only 3.606 kg.m-2. This soil retention is due to vetiver’s strong, deep root system and hedge-like structure that effectively holds the soil. Additionally, the runoff was reduced by 14% in plots covered with vetiver. For the 60 minutes of cumulative rainfall, the total runoff in the bare soil micro-plot was 81.45 cm, compared to 70.35 cm in the vetiver micro-plot. Therefore, vetivers are expected to be a sustainable and eco-friendly solution for runoff and erosion control within the lowland haor context.
Comparative study of Portable Vis-NIR Spectrometers for Corn Moisture Content Prediction Using Machine LearningOriginal Paper
Harki Himawan, Muhammad Dzakky Alghifari, Rut Juniar Nainggolan, Mochamad Bagus Hermanto, Nazmi Mat Nawi, Ken Abamba Omwange, Dimas Firmanda Al Riza
Non-destructive estimation of corn kernel moisture content is essential for determining the optimal harvest period. Although various spectrometer sensors are currently available, their predictive performance differs due to variations in spectral resolution and wavelength coverage. This study compared the performance of several portable spectrometer sensors with different wavelength ranges for predicting corn moisture content. Spectral data and reference moisture content were used to develop prediction models using Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN). Based on PLSR modelling, the AS7265X and C12880MA sensors produced the best performance, with coefficients of determination (R²) for training and testing reaching up to 0.90. Furthermore, ANN modelling yielded improved predictive accuracy, with the highest R² value of 0.95 obtained using the same sensor combination. These results demonstrate that portable spectrometers show strong potential for non-destructive field-based prediction of corn moisture content and can serve as a reliable indicator for determining optimal harvest timing.