Research in Agricultural Engineering - Online first
Architecture of a cyber-physical system for washing agricultural machineryOriginal Paper
Anatoliy Tryhuba, Orest Filkin, Inna Тryhuba, Andriy Tatomyr, Oksana Malanchuk
Res. Agr. Eng., X:X 
This paper presents the architecture of a cyber-physical system for the automated washing of agricultural machinery, designed to enhance efficiency and intelligent control. The system includes four layers – physical, sensor, computational, and interface and integrates actuators, sensors, decision-making modules, and analytics. A Python-based simulation using Control and SimPy showed an average washing time of 10.4 minutes and 97.5% cycle initiation accuracy under critical contamination. The Control was achieved via gated recurrent unit (GRU) prediction and proportional–integral–derivative (PID) regulation. Despite assumptions like...
Study on the drying kinetics of Rosa flower buds using different drying methodsOriginal Paper
Hamid Reza Gazor, Omid Reza Roustapour
Res. Agr. Eng., X:X | DOI: 10.17221/43/2025-RAE
This study investigated the drying kinetics of Rosa flower buds (Rosa damascene Mill) under environmental conditions (shade), as well as in direct and indirect solar dryers. The effects of 40 °C, 50 °C, and 60 °C temperatures on the drying of the buds were also examined using a laboratory cabinet dryer. The drying rate of the Rosa flower buds was compared with various mathematical models, and the parameters of these models were evaluated. The results illustrated that drying Rosa flower buds under shade required a lengthy period time of approximately 13 days. In contrast, utilising solar dryers significantly reduced the...
Detection of heat-stressed chickens in poultry house based on deep network and optical flow vectors in the Fourier domainOriginal Paper
Ngo Quoc Viet, Thai Yen
Res. Agr. Eng., X:X | DOI: 10.17221/46/2025-RAE
The productivity and quality of the entire flock are negatively impacted by heat stress in chickens, which can have major repercussions, particularly in crowded farming settings where diseases are easy to spread and hard to control. This study uses deep networks and optical flow to identify heat stress in chickens. The technique focuses on identifying obvious signs of heat stress, such as panting and open-mouth breathing in chickens. There are two phases to the suggested approach: (1) using a deep network to detect open-mouth breathing in chickens; (2) using the Gunnar Farnebäck algorithm to compute the optical flow vectors of the wattle, the breathing...