Shahid Bahonar University of KermanBiomechanism and Bioenergy Research2821-18555120260301Non-Destructive Authentication of Rice Varieties Using Hyperspectral Imaging and Machine Learning119537810.22103/bbr.2026.26384.1142ENEsmatKishani FarahaniInformation Technology and Intelligent Systems Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran.SeyedehsamanehShojaielangariBiomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran.Journal Article20251125This study develops a non-destructive method for authenticating four commercial rice varieties: Sargol, Domsiyah, AliKazemi, and Aria-Paria, by integrating hyperspectral imaging (HSI) with machine learning. Hyperspectral data in the visible–near infrared range (400–950 nm) were acquired from 4,305 individual rice grains. Effective preprocessing mitigated initial anisotropic spatial sampling by resizing images to achieve isotropic resolution, preventing grain loss during segmentation. Two analytical strategies were investigated: one relying on handcrafted features and another directly exploiting reduced spectral profiles as sequential data. Comparative evaluation showed that models trained on sequential spectral information consistently outperformed feature-based methods. Among the evaluated classifiers, a Support Vector Machine (SVM) achieved the highest classification accuracy of 92.62%, exceeding both classical machine learning models and deep learning approaches, including Long Short-Term Memory (LSTM) networks, one-dimensional Convolutional Neural Networks (1D-CNNs), and a hybrid CNN-LSTM architecture. The proposed HSI–SVM framework demonstrates strong potential for accurate rice variety authentication and offers practical applicability in quality control and supply chain monitoring.https://bbr.uk.ac.ir/article_5378_66c7c9cdb10b2a1c9447d11839fa1543.pdfShahid Bahonar University of KermanBiomechanism and Bioenergy Research2821-18555120260301Mild Heat and Microwave Inactivation of Escherichia coli and Saccharomyces cerevisiae in Pomegranate Juice2030537910.22103/bbr.2026.26448.1144ENHamid-RezaAkhavanDepartment of Food Science and Technology, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.MohsenBarzegarDepartment of Food Science and Technology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.Mohammad AliSahariDepartment of Food Science and Technology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.Journal Article20251205Pomegranate juice is a rich source of functional ingredients which thermal processing significantly affects its functional compounds. In this study, the effect of mild heat and microwave processing on the inactivation of inoculated microorganisms in pomegranate juice was studied. The juices were processed with mild heat treatment (52, 54, 56, 58 and 60°C for 0 to 120 s) and also with a domestic microwave at 600 W (for 0, 45, 60, 67.5, 75, 80, and 90 s) and 900 W (for 0, 30, 40, 45, 50, and 60 s). Mild heat (at 60°C for 120 s) and microwave (600 and 900 W for 90 and 60 s, respectively) treatments led to a reduction of approximately 6.65 log cycles in E. coli and 5.06 log cycles in S. cerevisiae. The mild temperature of 58°C for 120 s reduced S. cerevisiae to below the detection limit, while the population of E. coli was inactivated at 60°C for 120 s. Also, the S. cerevisiae load at 600 W/80 s and 900 W/50 s was less than the detection limit. But, the E. coli load at 600 W/90 s and 900 W/60 s were less than detection limit. The type of pomegranate juice did not have a significant effect on microbial inactivation in both mild heat and microwave processes. Similar to mild heat treatment, S. cerevisiae was more sensitive than E. coli at both microwave powers.https://bbr.uk.ac.ir/article_5379_2d488d6366a680c38ad131d68638ebd2.pdfShahid Bahonar University of KermanBiomechanism and Bioenergy Research2821-18555120260301Studies on Flow Rates of Wheat through Rectangular Orifices3140537610.22103/bbr.2026.26243.1140ENFarhadKhoshnamDepartment of Mechanical Engineering of Biosystems, Faculty of Agriculture, University of Jiroft, Jiroft, Iran.MoslemNamjooDepartment of Mechanical Engineering of Biosystems, Faculty of Agriculture, University of Jiroft, Jiroft, Iran.Mohammad RezaKamandarDepartment of Mechanical Engineering of Biosystems, Faculty of Agriculture, University of Jiroft, Jiroft, Iran.Journal Article20251105This experiment was conducted to examine how factors, including orifice area, aspect ratio, grain moisture content, and grain variety, influence the flow rate. Flow rates were measured for samples passing through rectangular horizontal orifices. An adjustable rectangular orifice with a maximum width of 50 mm and a length of 76 mm was used, with the shorter side varied in increments of 10 mm, ranging from 10 mm upward. The flow rate of wheat was significantly influenced by the orifice area and the moisture content. The decrease in flow rate with an increasing moisture content can be well described by a second-degree polynomial equation. The coefficients for several derived equations, which predict wheat flow rate through orifices based on measured parameters and their combinations, were determined. A linear relationship is found between the flow rate (m³/h) and the product of the effective orifice area (Aₑ, cm²) and the square root of (g·Dₑ), i.e., . The flow rate of wheat varieties for a given dimension of orifice decreased as the aspect ratio increased. Flow rate increased as the area of the orifice increased from 1.15 to 23.15 cm2; however, a higher flow rate was obtained at a lower aspect ratio. The variation in flow rates among wheat varieties was minimal across the different orifice sizes. This study establishes a universal correlation based on the Beverloo and ASABE models that accurately () predicts the flow of wheat through rectangular orifices in a hopper.https://bbr.uk.ac.ir/article_5376_bb4053c13184e8f65b30b87b76884ed7.pdfShahid Bahonar University of KermanBiomechanism and Bioenergy Research2821-18555120260301A Review of Solar-Powered, Robotic, and AI-Driven Agricultural Machinery for Smart and Sustainable Farming4161538010.22103/bbr.2026.26353.1141ENMohammadMohammadiDepartment of Mechanical Engineering, Shi.C., Islamic Azad University, Shiraz, IranSeyed Mohammad RezaNazemosadatDepartment of Mechanical Engineering, Shi.C., Islamic Azad University, Shiraz, IranYousefBazargan LariDepartment of Mechanical Engineering, Shi.C., Islamic Azad University, Shiraz, IranAhmadAfsariDepartment of Mechanical Engineering, Shi.C., Islamic Azad University, Shiraz, IranAlirezaBahramkiaDepartment of Mechanical Engineering, Sarv. C., Islamic Azad University, Sarvestan, IranJournal Article20251209The transition from conventional mechanization to intelligent and sustainable farming is increasingly driven by clean energy and automation. This review synthesizes recent advances in solar-powered agricultural machinery, robotics, and artificial intelligence (AI) within the broader context of biosystems engineering. Solar-powered tractors, autonomous ground vehicles, and unmanned aerial systems reduce reliance on fossil fuels, lower labor demands, and enhance precision in seeding, irrigation, and harvesting. At the same time, AI, machine vision, IoT, and big data enable real-time monitoring and decision-making, contributing to resource-efficient and climate-resilient farming systems. Despite progress, challenges such as high initial costs, limited battery capacity, and insufficient charging infrastructure hinder large-scale adoption. Promising solutions include next-generation batteries, modular energy storage, hybrid renewable energy platforms, and advances in robotic perception and deep learning. This review highlights the synergistic role of bioenergy integration, digital automation, and mechanical innovation in shaping future agricultural machinery. By outlining research priorities in energy storage, robotics, and data-driven farm management, the article provides a roadmap for accelerating smart agriculture toward financially viable, climate-smart, and digitally integrated biosystems.https://bbr.uk.ac.ir/article_5380_48a323e354b35e39ebdc8e9312635e87.pdfShahid Bahonar University of KermanBiomechanism and Bioenergy Research2821-18555120260301Efficient Tomato Drying Using Refractance Window-UV Equipped With a Heat Pump: Performance Optimization and Kinetic Modeling6269538110.22103/bbr.2026.26958.1148ENZeinabRezvaniDepartment of Biosystems Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.AhmadGhazanfari-MoghaddamDepartment of Biosystems Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.Journal Article20260304This study investigates the efficiency of a combined Refractance Window-Ultraviolet drying system with a heat pump for tomato processing. Experiments were conducted at three different temperatures: 60, 70, and 80°C, with and without the heat pump. The objective of this research was to evaluate drying time, determine relative humidity, and develop a model under varying conditions. The results showed that at 80°C, with the use of the heat pump, drying time was reduced to 91 minutes, and the final moisture content ratio reached 0.13. The Midilli model was used to analyze moisture reduction, which described the moisture changes well, with a coefficient of determination (R²) higher than 0.99. The highest R² was observed at 70°C without the pump (R² = 0.99834), and the lowest was at 80°C without the pump (R² = 0.98832). These results indicate that the use of a heat pump can optimize the drying process and reduce time. This study demonstrates that the combined Refractance Window-Ultraviolet system with a heat pump can be an efficient method for processing heat-sensitive products such as tomatoes.https://bbr.uk.ac.ir/article_5381_9df5c4a096afc9f476fa0c407f0e262e.pdfShahid Bahonar University of KermanBiomechanism and Bioenergy Research2821-18555120260301Prediction of Enzymatic Activity of Aspergillus Species Using Visible-Near Infrared Machine Vision System7081538210.22103/bbr.2026.26809.1146ENMohammad HosseinNargesiMechanical Engineering of Biosystems Department, Ilam University, Ilam, IranKhadijehAbbasiPlant Protection Department, Ilam University, Ilam, IranZeinabBastamiPlant Protection Department, Ilam University, Ilam, IranKamranKheiralipourMechanical Engineering of Biosystems Department, Ilam University, Ilam, IranJournal Article20260210Various species of the filamentous fungus Aspergillus are among the most common fungi found in air, soil, plants, and indoor environments. Many species of this genus are of significant importance in economic, biotechnological, and medical contexts, particularly in the production of enzymes, organic acids, antibiotics, and other bioactive metabolites. However, some species are opportunistic pathogens capable of causing diseases in both plants and humans. Hyperspectral imaging is a useful tool for identifying fungal traits, and when combined with machine learning, it enables more accurate and automated detection. In this study, the effect of time on the fungal growth and production of chitinase enzyme in Aspergillus endophytic isolates was investigated and a comparison was made between the isolates. The results showed that with time, the enzymatic activity of the fungal isolates increased. Additionally, significant differences were observed between the fungal isolates. The fungal growth increased with increasing enzyme activity duration too.https://bbr.uk.ac.ir/article_5382_ae9742ef78c081ff3c3c3b5575a76e44.pdf