Template-Type: ReDIF-Article 1.0 Author-Name: Muhammad Hashaam Author-Workplace-Name: Faculty of Agricultural Engineering and Technology, University of Agriculture Faisalabad, Pakistan Author-Name: Muhammad Waqar Akram Author-Workplace-Name: Faculty of Agricultural Engineering and Technology, University of Agriculture Faisalabad, Pakistan Author-Name: Moaz Ahmad Author-Workplace-Name: Department of North Surgery, King Edward Medical University Lahore, Pakistan Author-Name: Muhammad Zuhaib Akram Author-Workplace-Name: State Key Laboratory of Automobile Safety and Energy, School of Vehicle and Mobility, Tsinghua University, China Author-Name: Muhammad Faheem Author-Workplace-Name: Faculty of Agricultural Engineering and Technology, University of Agriculture Faisalabad, Pakistan Author-Name: Muhammad Maqsood Author-Workplace-Name: National Institute of Food science and Technology, University of Agriculture Faisalabad, Pakistan Author-Name: Muhammad Aleem Author-Workplace-Name: College of Environment, Hohai University, China Title: 3D finite element analysis of tine cultivator and soil deformation Abstract: For effective tillage, design and selection of tillage tool according to soil type and condition is very important. The present study is carried out for in-depth investigation of different types of shovels of tine cultivator and behavior of soil in response to loads subjected during tillage using finite element analysis. Different types of shovels like reversible, duck foot, seed drill and cultivator shovel are simulated with different types of soil like sand, clay and loam. The origination, level and distribution of stresses and deformations in shovels experienced in different types of soils are probed. Furthermore, high stressed and crack sensitive regions are identified. The stresses of 18, 53, 64 MPa are generated in reversible shovel of tine cultivator during ploughing in sandy, clay and loamy soil respectively. In addition, results of different shovels are compared, and it is found that the duck foot type shovel experiences highest stress and deformation. The duck foot shovel experiences about 20 and 71% higher stresses in loam compared to that in clay and sand respectively. Moreover, the study of soil mechanical behavior shows that the soil block (clay soil) experiences maximum stress of 34 MPa while tilling with reversible shovel. The statistical analysis is also conducted that shows high significance of simulation results. Keywords: finite element method, soil deformation, stresses, tillage implement, tine cultivator shovel Journal: Research in Agricultural Engineering Pages: 107-117 Volume: 69 Issue: 3 Year: 2023 DOI: 10.17221/58/2022-RAE File-URL: http://rae.agriculturejournals.cz/doi/10.17221/58/2022-RAE.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/rae-202303-0001.txt Handle: RePEc:caa:jnlrae:v:69:y:2023:i:3:id:58-2022-RAE Template-Type: ReDIF-Article 1.0 Author-Name: Andriani Lubis Author-Workplace-Name: Department of Agricultural Engineering, Faculty of Agriculture, Syiah Kuala University, Banda Aceh, Aceh Province, Indonesia Author-Name: Syafriandi Syafriandi Author-Workplace-Name: Department of Agricultural Engineering, Faculty of Agriculture, Syiah Kuala University, Banda Aceh, Aceh Province, Indonesia Author-Name: Muhammad Idkham Author-Workplace-Name: Department of Agricultural Engineering, Faculty of Agriculture, Syiah Kuala University, Banda Aceh, Aceh Province, Indonesia Author-Name: Ari Maulana Author-Workplace-Name: Department of Agricultural Engineering, Faculty of Agriculture, Syiah Kuala University, Banda Aceh, Aceh Province, Indonesia Title: Design and construction of coffee roasting machine with rounding cylinder tube using electric heat source Abstract: The purpose of this research is  to design a rounding cylinder tube on a coffee roaster using an electric heating element that will be used to roast coffee. The roasting process also uses an electric motor to rotate the cylindrical drum so that the stirring process becomes even. The research was conducted using engineering methods including identification of problems, roasting machine design formulation, prototyping, functional testing, and performance testing. The data analysed are roasting capacity, roasting temperature and the need for electrical energy used. The results showed that the roasting capacity was 2.3 kg.h-1. The serving of coffee for dark roast maturity levels can be ended when the temperature has reached a temperature of 201 °C. The need for electrical energy in the heater for roasting arabica coffee beans with a maturity level of  a dark roast for 1 hour 54 minutes obtained an average value of 3.4 kWh, with the need for electrical energy for roasting arabica coffee beans which is 1.35 kWh. Keywords: coffee beans, dark roast, electrical energy, electric motor, roasting temperature Journal: Research in Agricultural Engineering Pages: 118-123 Volume: 69 Issue: 3 Year: 2023 DOI: 10.17221/69/2022-RAE File-URL: http://rae.agriculturejournals.cz/doi/10.17221/69/2022-RAE.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/rae-202303-0002.txt Handle: RePEc:caa:jnlrae:v:69:y:2023:i:3:id:69-2022-RAE Template-Type: ReDIF-Article 1.0 Author-Name: Darwin Darwin Author-Workplace-Name: Department of Agricultural Engineering, Faculty of Agriculture, Universitas Syiah Kuala, Banda Aceh, Indonesia Author-Name: Rini Ayu Marisa Harahap Author-Workplace-Name: Department of Agricultural Engineering, Faculty of Agriculture, Universitas Syiah Kuala, Banda Aceh, Indonesia Author-Name: Atmadian Pratama Author-Workplace-Name: Department of Agricultural Engineering, Faculty of Agriculture, Universitas Syiah Kuala, Banda Aceh, Indonesia Author-Name: Muhammad Thifa Author-Workplace-Name: Department of Agricultural Engineering, Faculty of Agriculture, Universitas Syiah Kuala, Banda Aceh, Indonesia Author-Name: Muhammad A Alwi Fayed Author-Workplace-Name: Department of Agricultural Engineering, Faculty of Agriculture, Universitas Syiah Kuala, Banda Aceh, Indonesia Title: Enhanced biodiesel production from waste cooking oils catalyzed by sodium hydroxide supported on heterogeneous co-catalyst of bentonite clay Abstract: 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. Keywords: biofuel, heterogeneous catalyst, biodiesel, used frying oil Journal: Research in Agricultural Engineering Pages: 124-131 Volume: 69 Issue: 3 Year: 2023 DOI: 10.17221/70/2022-RAE File-URL: http://rae.agriculturejournals.cz/doi/10.17221/70/2022-RAE.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/rae-202303-0003.txt Handle: RePEc:caa:jnlrae:v:69:y:2023:i:3:id:70-2022-RAE Template-Type: ReDIF-Article 1.0 Author-Name: Sheeraz Arif Arif Author-Workplace-Name: Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan Author-Name: Rashid Hussain Author-Workplace-Name: Department of Information and Communication Engineering, Beijing Institute of Technology, Beijing, China Author-Name: Nadia Mustaqim Ansari Author-Workplace-Name: Department of Electronic Engineering, Faculty of Engineering Science and Technology, Hamdard University, Karachi, Pakistan Author-Name: Waseem Rauf Author-Workplace-Name: Department of Electronic Engineering, Dawood University of Engineering and Technology, Karachi, Pakistan Title: A novel hybrid feature method for weeds identification in the agriculture sector Abstract: Weed identification and controlling systems are gaining great attention and are very effective for large productivity in the agriculture sector. Currently, farmers are facing a weed control and management problem, and to tackle this challenge precision agriculture in the form of selective spraying is much-needed practice. In this article, we introduce a novel framework for a weed identification system that leverages (hybrid) the robust and relevant features of deep learning models, such as convolutional neural network (CNN) and handcrafted features. First, we apply the image pre-processing and augmentation techniques for image quality and dataset size enhancement. Then, we apply handcrafted feature extraction techniques, such as local binary pattern (LBP) and histogram of oriented gradients (HOG) to extract texture and shape features from the input. We also apply the deep learning model, such as CNN, to capture the relevant semantic features. Lastly, we concatenate the features extracted from a different domain and explore the performance using different classifiers. We achieved better performance and classification accuracy in the presence of the extreme gradient boosting (XGBoost) classifier. The achieved results witnessed the effectiveness and applicability of the given method and the importance of concatenated features. Keywords: convolutional neural network, deep learning, handcrafted features, weed detection, XGBoost classifier Journal: Research in Agricultural Engineering Pages: 132-142 Volume: 69 Issue: 3 Year: 2023 DOI: 10.17221/77/2022-RAE File-URL: http://rae.agriculturejournals.cz/doi/10.17221/77/2022-RAE.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/rae-202303-0004.txt Handle: RePEc:caa:jnlrae:v:69:y:2023:i:3:id:77-2022-RAE Template-Type: ReDIF-Article 1.0 Author-Name: Dinh Anh Tuan Tran Author-Workplace-Name: Faculty of Heat and Refrigeration engineering, Industrial University of Ho Chi Minh city, Ho Chi Minh city, Viet Nam Author-Name: Tuan Nguyen Van Author-Workplace-Name: Faculty of Heat and Refrigeration engineering, Industrial University of Ho Chi Minh city, Ho Chi Minh city, Viet Nam Author-Name: Dinh Nhat Hoai Le Author-Workplace-Name: Faculty of Heat and Refrigeration engineering, Industrial University of Ho Chi Minh city, Ho Chi Minh city, Viet Nam Author-Name: Thi Khanh Phuong Ho Author-Workplace-Name: Faculty of Heat and Refrigeration engineering, Industrial University of Ho Chi Minh city, Ho Chi Minh city, Viet Nam Title: Study on drying of bitter gourd slices based on halogen dryer Abstract: In this study, the drying of bitter gourd slices with a halogen dryer was done at different thicknesses of bitter gourd (3, 5, and 7 mm) and temperatures (60, 65, and 70 °C). The effect of varying drying characteristics in the experiment was explored. Experimental results were evaluated based on the drying time and moisture content. The results indicate that the material drying thickness and drying temperature significantly impact the drying time and the equilibrium moisture content. Furthermore, the Multivariate Adaptive Regression Splines (MARS) model is also used to train and predict the moisture content of bitter gourd in this research. The temperature, thickness of the bitter gourd, and drying time were used as input parameters for the model. Two measures R2 and Root Mean Ssquare Error (RMSE) were used to determine the accuracy of the trained MARS model. During training, the values of R2 and RMSE obtained were 0.9846 and 3.7324, respectively. The test of trained MARS was successful, with a satisfactory correlation between experimental data points and predicted points. The results show that MARS can accurately predict the moisture content of bitter gourd in a halogen dryer. Keywords: ANN, drying temperature, machine learning, MARS model, moisture content Journal: Research in Agricultural Engineering Pages: 143-150 Volume: 69 Issue: 3 Year: 2023 DOI: 10.17221/97/2022-RAE File-URL: http://rae.agriculturejournals.cz/doi/10.17221/97/2022-RAE.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/rae-202303-0005.txt Handle: RePEc:caa:jnlrae:v:69:y:2023:i:3:id:97-2022-RAE Template-Type: ReDIF-Article 1.0 Author-Name: Shitophyta Lukhi Mulia Author-Workplace-Name: Department of Chemical Engineering, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta, Indonesia Author-Name: Arnita Arnita Author-Workplace-Name: Department of Chemical Engineering, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta, Indonesia Author-Name: Wulansari Hilda Dyah Ana Author-Workplace-Name: Department of Chemical Engineering, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta, Indonesia Title: Evaluation and modelling of biogas production from batch anaerobic digestion of corn stover with oxalic acid Abstract: Corn stover is one of the potential lignocellulosic biomasses as the raw material of biogas production. Pretreatment of lignocellulose substrates can enhance biodegradability and biogas yield. This study investigates the effect of oxalic acid pretreatment on biogas production during batch anaerobic digestion of corn stover. First-order, logistic, modified Gompertz and transference models predicted kinetic parameters during biogas production from pretreated corn stover. Results showed that oxalic acid pretreatment significantly affected biogas production (P < 0.05). The highest cumulative biogas yields of pretreated and untreated corn stover were 95.14 mL/gVS and 57.55 mL/gVS, respectively. Pretreated substrates improved biodegradability by 165%. Four kinetic models provided the determination coefficients R2 higher than 0.9. The logistic model and modified Gompertz provided the best deviation of 1.57 and 3.75%, respectively. The logistic model proved the best fitting in predicting cumulative yields and simulating the kinetic model of anaerobic digestion of pretreated corn stover among the three models. Keywords: First-Order model, Logistic model, Kinetic model, modified Gompertz, Transference model Journal: Research in Agricultural Engineering Pages: 151-157 Volume: 69 Issue: 3 Year: 2023 DOI: 10.17221/98/2022-RAE File-URL: http://rae.agriculturejournals.cz/doi/10.17221/98/2022-RAE.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/rae-202303-0006.txt Handle: RePEc:caa:jnlrae:v:69:y:2023:i:3:id:98-2022-RAE