Article
Agronomy
Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Amin Taheri-Garavand, Amin Nasiri, Asghar Mahmoudi
Summary: This study proposed a one-dimensional CNN model for sorting pistachio nuts using acoustic emissions signals, which outperformed other comparative methods on a sample of 1600 pistachio nuts. The method provides a smart, non-invasive, and reliable technique for online pistachio nuts sorting systems.
POSTHARVEST BIOLOGY AND TECHNOLOGY
(2022)
Article
Materials Science, Paper & Wood
Mohammad Arabi, Mohammad Dahmardeh Ghalehno
Summary: The drying process of bagasse particles was studied using an artificial neural network (ANN) and 18 thin-layer drying (TLD) models. The results showed that an increase in temperature decreased the drying time of bagasse and increased the constant drying. Additionally, the ANN model was found to accurately predict changes in bagasse moisture content compared to other models.
Article
Engineering, Chemical
Xabier Sukunza, Maider Bolanos, Mikel Tellabide, Idoia Estiati, Roberto Aguado, Martin Olazar
Summary: Pistachio nuts splitting for commercial purposes is necessary and a new method based on conical spouted beds technology is being developed. Experimental results show that low temperature batch drying and splitting can achieve a splitting percentage of up to 35%.
CHEMICAL ENGINEERING SCIENCE
(2023)
Review
Agriculture, Multidisciplinary
Ran Yang, Jiajia Chen
Summary: Microwave-assisted thermal process is a high-efficiency drying method with potential applications in the food industry. Artificial neural network (ANN) models have been extensively studied for prediction, optimization, monitoring, and control of microwave drying processes. Future research can focus on testing the developed ANN models in industrial-scale processes and applying them to optimize dynamic drying processes.
JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
(2022)
Article
Engineering, Chemical
C. P. Batuwatta-Gamage, C. M. Rathnayaka, H. C. P. Karunasena, W. D. C. C. Wijerathne, H. Jeong, Z. G. Welsh, M. A. Karim, Y. T. Gu
Summary: This paper presents a Physics-Informed Neural Network-based surrogate framework for coupling moisture concentration and shrinkage of a plant cell during drying. The results show that the PINN-based model outperforms regular deep neural networks in terms of accuracy and stability when predicting moisture concentration and shrinkage, making it a powerful tool for investigating complex drying mechanisms.
JOURNAL OF FOOD ENGINEERING
(2022)
Article
Environmental Sciences
Ehssan Torabi, Khalil Talebi Jahromi, Mohammad Homayoonzadeh, Ali Olyaie Torshiz, Ebrahim Tavakoli
Summary: Pistachio is an economically valuable crop in Iran, but pesticides residues in nuts have raised health concerns. Research on the uptake and dissipation kinetics of insecticides in pistachio nuts found variations in uptake/dissipation rates depending on pesticide properties. Multiple sprayings with THI and THX may lead to pesticide residues exceeding the maximum residue limit. However, hazard quotients for all pesticides were below 1, indicating no risk to human health from consuming pistachio nuts.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Energy & Fuels
Saban Pusat, Ali Volkan Akkaya
Summary: Accurately predicting the instant moisture content of low-rank coals during the drying process is crucial for system design and operation. This study introduces a nonlinear and explicit model equation developed using a GMDH-type neural network, which provides accurate predictions under different drying conditions.
INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION
(2022)
Article
Thermodynamics
Mahesh Kumar, Ravinder Kumar Shimpy, Ravinder Kumar Sahdev, Sunil Kumar Sansaniwal, Vijay Bhutani, Himanshu Manchanda
Summary: This article compares the drying performance of two simple and common solar dryers for date fruits. The convective heat transfer coefficients and evaporative heat transfer coefficients were analyzed for both dryers. The results showed that the forced convection cabinet solar dryer (FCCSD) had higher drying efficiency and lower energy consumption than the forced convection greenhouse dryer (FCGHD). The Midilli-Kucuk model provided the best fit for the experimental drying data. The economic feasibility of both drying arrangements was also discussed.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2023)
Article
Plant Sciences
Rosane Patricia Ferreira Chaves, Adriano Lucena de Araujo, Alessandra Santos Lopes, Rosinelson da Silva Pena
Summary: This study investigated the effect of convective drying on the color degradation and phenolic compound loss of purple basil leaves, as well as the moisture absorption behavior of dried leaves. Drying was performed at different temperatures, and the degradation of chlorophyll, flavonoids, and phenolic compounds was evaluated. Mathematical modeling and moisture sorption data analysis were carried out. The results showed that the effective diffusivity for drying increased with temperature, and the leaves dried at 40 degrees C had less degradation but slower drying process. Different models were applied and the Oswin model was found to be the most suitable for estimating moisture sorption isotherms of the dried leaves.
Article
Agronomy
Yang Hao, Zhang Tong, Yang Weili, Xiang Hua, Liu Xiaoli, Zhang Hongqi, Liu Lei, Yang Xingyou, Liu Yajie, Guo Shiping, Zeng Shuhua
Summary: In this study, a Convolution Neural Network method consisting of digital images was proposed to monitor the moisture content of cigar leaves during the drying process. The trained Convolution Neural Network model demonstrated better results compared to traditional machine learning algorithms, with an R2 value of 0.9044 and an average accuracy of 87.34%. The generalization test showed that the Convolution Neural Network is a viable method for accurately estimating the moisture content, with an R2 value of 0.8673 and an average accuracy of 86.81%. The Convolution Neural Network established using features extracted from digital images was shown to be an effective monitoring tool.
INTERNATIONAL AGROPHYSICS
(2023)
Article
Agronomy
Huaiyu Liu, Zhijun Meng, Anqi Zhang, Yue Cong, Xiaofei An, Weiqiang Fu, Guangwei Wu, Yanxin Yin, Chengqian Jin
Summary: An online detection device based on the capacitance method was developed to measure the moisture content of straw bales in a square baler. The device integrates a capacitance sensor, pressure sensor, and temperature sensor, and exhibits good accuracy and stability.
Article
Forestry
Sohrab Rahimi, Stavros Avramidis, Ciprian Lazarescu
Summary: This study predicts and characterizes moisture variation in kiln-dried wood using polynomial models based on initial and target moisture values. Three models successfully characterize final moisture variation, with the best one having an R-2 > 96%. The linear model is the most resilient and recommended for estimating final moisture variation.
Article
Engineering, Chemical
Jialiang Sun, Xueyu Zhang, Xinjing Qiu, Xinyu Zhu, Tao Zhang, Jixin Yang, Xu Zhang, Yan Lv, Huihui Wang
Summary: This study aimed to investigate the feasibility of using hyperspectral data to predict moisture content and visualize moisture distribution during drying processes. By establishing regression coefficients and two-dimensional correlation spectroscopy models, the optimal model for moisture content prediction was determined. The visualization maps generated by the model revealed the optimal drying process.
Article
Thermodynamics
Sammy Sadaka
Summary: This study aimed to explore the drying kinetics parameters of rough rice under isothermal conditions and layer thicknesses. The results showed that increasing drying temperature, duration, and decreasing grain layer thickness led to a decrease in rough rice moisture content and moisture ratio. Among the tested models, the Page model showed the best fit. Layer drying was found to expedite the drying process.
CASE STUDIES IN THERMAL ENGINEERING
(2022)
Article
Chemistry, Analytical
Noraini Azmi, Latifah Munirah Kamarudin, Ammar Zakaria, David Lorater Ndzi, Mohd Hafiz Fazalul Rahiman, Syed Muhammad Mamduh Syed Zakaria, Latifah Mohamed
Summary: Seasonal crops require reliable storage conditions to protect the yield once harvested. Moisture content level in grains for long term storage is challenging. This study focuses on utilizing wireless technology and Artificial Neural Network models for moisture content classification and prediction in rice. Random Forest method provides the highest accuracy for moisture content prediction.
Article
Green & Sustainable Science & Technology
Manijeh Talebi, Baris Majnounian, Majid Makhdoum, Ehsan Abdi, Mahmoud Omid
Summary: This study evaluated the ecological capability of an area for ecotourism using a common systematic approach in Iran and a multilayer perceptron neural network. The performance of artificial neural network and linear discriminant analysis methods were compared, with the ANN achieving higher overall accuracy.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2021)
Article
Food Science & Technology
Amin Taheri-Garavand, Hasan Mumivand, Soodabeh Fatahi, Amin Nasiri, Mahmoud Omid
Summary: This study investigated changes in essential oil content and main components of mint during thin-layer drying process using response surface methodology. Results showed that drying time and temperature optimization can scale-up essential oil content and main constituents of mint. The efficiency of the models for predicting essential oil content and components during drying process was high, demonstrating the potential of response surface methodology as a rapid and accurate method.
JOURNAL OF FOOD PROCESSING AND PRESERVATION
(2021)
Article
Food Science & Technology
Mahmoud Soltani Firouz, Mahdi Rashvand, Mahmoud Omid
Summary: A new method was developed for detecting adulteration in sesame oil, using PCA model for authentic sesame oil detection and predicting adulteration amounts through artificial neural network and support vector regression. The radial basis function kernel SVR model had the best performance, followed by the ANN model.
LWT-FOOD SCIENCE AND TECHNOLOGY
(2021)
Article
Energy & Fuels
Mostafa Jafarian, Monica Delgado, Mahmoud Omid, Majid Khanali, Mozaffar Mokhtari, Ana Lazaro
Summary: The addition of nanoparticles to paraffin has been proven to be an effective method for enhancing thermophysical properties, with higher mass fractions and smaller nanoparticle sizes resulting in higher densities and thermal energy storage densities in NePCMs. Furthermore, using smaller nanoparticle sizes with mass fractions of 3% and 6% can significantly improve thermal conductivity.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Food Science & Technology
Khaled Mohi-Alden, Mahmoud Omid, Mahmoud Soltani Firouz, Amin Nasiri
Summary: A bell pepper sorting system based on deep convolutional neural network was developed, achieving efficient and fast classification capabilities suitable for industrial applications.
JOURNAL OF FOOD SCIENCE
(2022)
Article
Agronomy
Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Amin Taheri-Garavand, Amin Nasiri, Asghar Mahmoudi
Summary: This study proposed a one-dimensional CNN model for sorting pistachio nuts using acoustic emissions signals, which outperformed other comparative methods on a sample of 1600 pistachio nuts. The method provides a smart, non-invasive, and reliable technique for online pistachio nuts sorting systems.
POSTHARVEST BIOLOGY AND TECHNOLOGY
(2022)
Article
Green & Sustainable Science & Technology
Abotaleb Salehnasab, Mahmoud Bayat, Manouchehr Namiranian, Bagher Khaleghi, Mahmoud Omid, Hafiz Umair Masood Awan, Nadir Al-Ansari, Abolfazl Jaafari
Summary: Estimating the diameter increment of forests is crucial in forest management, and this study explored the application of MLP and ANFIS in developing diameter increment models for Hyrcanian forests. Results showed that ANFIS performed better for beech and chestnut-leaved oak groups, suggesting a strong relationship between modeling techniques and tree species characteristics.
Article
Engineering, Multidisciplinary
Seyed Roohollah Mousavi, Fereydoon Sarmadian, Mahmoud Omid, Patrick Bogaert
Summary: This study focuses on modeling and mapping soil organic carbon in an arid and semi-arid region of Iran at high spatial resolution. The results show that considering soil variables can improve the accuracy of SOC predictions.
Article
Computer Science, Hardware & Architecture
Amin Nasiri, Mahmoud Omid, Amin Taheri-Garavand, Abdolabbas Jafari
Summary: This study utilizes deep learning techniques to achieve pixel-wise semantic segmentation of sugar beet, weed, and soil using the U-Net architecture. By training the model with a properly distributed image dataset and employing a custom loss function, segmentation accuracy is improved.
SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
(2022)
Article
Agriculture, Multidisciplinary
Rahim Azadnia, Ali Rajabipour, Bahareh Jamshidi, Mahmoud Omid
Summary: Timely monitoring of apple trees' nutrition status is crucial for accurate nutrient management, and this study aims to establish a cost-effective and non-destructive method for estimating NPK status using Vis/NIR spectroscopy and machine learning. Ground-based sensors provide efficient information on nutritional status, while tissue analysis is laborious and destructive. The results indicate that the developed models using non-linear methods show superior performance in estimating NPK contents in apple tree leaves.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Thermodynamics
Sara Borhani, Alibakhsh Kasaeian, Peyman Pourmoghadam, Mahmoud Omid
Summary: The goal of this research is to study a trigeneration solar system that can meet household energy demands. A reliable network is trained to forecast the system's functionality under different weather conditions, replacing the time-consuming simulation process. The research shows that warm regions like Ahwaz province have the most suitable weather conditions to effectively utilize the system.
APPLIED THERMAL ENGINEERING
(2023)
Article
Geosciences, Multidisciplinary
Seyed Roohollah Mousavi, Fereydoon Sarmadian, Marcos Esteban Angelini, Patrick Bogaert, Mahmoud Omid
Summary: This study investigates the use of a structural equation modeling (SEM) approach to assess the effects of soil forming factors on key soil properties in an arid and semi-arid region of Iran. The results show that several environmental factors are impacting these soil properties. Although the SEM approach is useful for identifying cause-effect relationships, it is not efficient for digital soil mapping of these soil properties.
Article
Environmental Sciences
Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker
Summary: This research developed a novel deep learning-based model for predicting soil properties using visible and near-infrared spectroscopic data. The study demonstrated that the CNN model outperformed the PLSR model in predicting various soil properties. The use of deep learning-based models and VIS-NIR spectral data was shown to be a feasible method for rapidly assessing important soil properties.
ENVIRONMENTAL EARTH SCIENCES
(2023)
Article
Agriculture, Multidisciplinary
Saman Alvandi, Seyed Saeid Mohtasebi, Mahmoud Omid, Mohammad Hosseinpour-Zarnaq
Summary: An intelligent system combining machine vision and artificial neural networks was developed to classify and clean N. sativa seeds and its impurities. The results demonstrated the feasibility of this approach in real-time cleaning and suggested its potential use in detecting, classifying, and automatically cleaning other similar seeds.
SPANISH JOURNAL OF AGRICULTURAL RESEARCH
(2023)
Article
Agriculture, Multidisciplinary
Khaled Mohi-Alden, Mahmoud Omid, Mahmoud Soltani Firouz, Amin Nasiri
Summary: The uniformity of appearance attributes is crucial for bell peppers in order to meet the standards and needs of consumers and the food industry. This research aimed to develop a machine vision-based system for sorting bell peppers based on maturity levels and size, using multilayer perceptron (MLP) artificial neural networks as the nonlinear models. The results showed that the MLP outperformed other models, and the system achieved high accuracy and speed in the in-line sorting process.
INFORMATION PROCESSING IN AGRICULTURE
(2023)