Research in Agricultural Engineering, 2025 (vol. 71), issue 2
Perception of bimodal warning cues during remote supervision of autonomous agricultural machinesOriginal Paper
Anita Chidera Ezeagba, Cheryl Mary Glazebrook, Daniel Delmar Mann
Res. Agr. Eng., 2025, 71(2):69-79 | DOI: 10.17221/73/2024-RAE 
Agricultural machines that are fully autonomous will still need human supervisors to monitor and troubleshoot system failures. Recognising the emergency as soon as possible is crucial to reduce adverse effects. The ability of humans to detect visual, auditory, or tactile cues is usually enabled by warning systems. The effectiveness of different warning cues varies in terms of prompting a quick response. The study’s objective was to compare the effectiveness of two bimodal warnings (i.e., visual-auditory and visual-tactile) at eliciting supervisor perception (which equates to level one situation awareness). Twenty-five participants engaged in...
Spoilage detection of tomatoes using a convolutional neural networkOriginal Paper
Ninja Begum, Manuj Kumar Hazarika
Res. Agr. Eng., 2025, 71(2):80-87 | DOI: 10.17221/31/2024-RAE
With the increasing productivity in agriculture, it has become extremely essential to look for an advanced technique that will help to minimise losses. Recently, deep learning has outperformed the task of recognition and classification of fruits and vegetables automatically from images, finding applicability in this study. This work, thus, attempts to develop an automatic spoilage detection CNN model for tomatoes. In this work, a deep learning-based CNN model is trained and validated on a self-prepared dataset for classifying tomatoes as edible and spoilt is proposed. The dataset consisted of 810 images, out of which 572 images were considered...
Stability of soil moisture sensors for agricultural crop cultivationShort Communication
Sitti Nur Faridah, Muhammad Tahir Sapsal, Tisha Aditya A. Jamaluddin, Andini Dani Achmad, Muhammad Adi Surya
Res. Agr. Eng., 2025, 71(2):88-94 | DOI: 10.17221/33/2024-RAE
Soil water content is critical in plants’ morphological and physiological processes; therefore, water must always be available in appropriate quantities to meet plant growth needs. Soil moisture can be easily detected using sensors, which offer a practical solution for monitoring water content in the soil. However, using sensors for a long time, especially on agricultural land, will reduce sensor accuracy. This research aims to investigate the accuracy of soil moisture sensors during their use for cultivating crops. Using sensors in sandy clay soil can detect soil moisture levels with an accuracy of 93.80% and a precision of 90.81%. A reading...
Enhancing the destructive egg quality assessment using the machine vision and feature extraction techniqueOriginal Paper
Ehsan Sheidaee, Pourya Bazyar
Res. Agr. Eng., 2025, 71(2):95-104 | DOI: 10.17221/86/2024-RAE
The rapid growth of the food industry necessitates rigorous quality control, particularly in egg production. This study explores advanced methodologies for egg quality assessment by integrating the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbour (KNN) with machine vision techniques. While traditional destructive methods like measuring the Haugh unit (HU) offer direct insights, but render eggs unusable, non-destructive techniques, such as imaging and spectroscopy, allow continuous quality monitoring. Over a 20-day period, egg samples were evaluated using a digital camera to capture key parameters like...
Camera systems and their user recognition reliability when entering an agri-food complexOriginal Paper
Jaroslav Mrázek, Jakub Vošáhlík, Eva Olmrová, Martin Pexa, Zdeněk Aleš, Jakub Čedík
Res. Agr. Eng., 2025, 71(2):105-112 | DOI: 10.17221/35/2025-RAE
This study evaluates the efficiency of various facial recognition camera systems used to control access in agri-food production environments, focusing on their ability to identify individuals based on biometric facial traits. It is also important to prevent the movement of unwanted persons into the production premises in the agri-food complex. The main goal was to assess how these factors influence the recognition performance and to determine the most reliable system for preventing unauthorised entry. The results show notable performance disparities between the devices tested. It can be concluded in this research that there are statistically significant...
Portable analogue-based electronic moisture meter for root-crop chipsShort Communication
James Roy Lesidan, Mencius Lesidan, Melvin Hagonob, Charlie Andan, Ma. Grace Sumaria, Ronaldo Almencion, Kebin Ysrael Martinez
Res. Agr. Eng., 2025, 71(2):113-120 | DOI: 10.17221/15/2025-RAE
Moisture content regulation of root crops is crucial in post-harvest processing operations, not only in the price stipulation but also to avoid aflatoxin contamination. To prolong their storage life, they are processed into dried chips to extend their usability in feed formulations and starches. In this study, we use the capacitance-based method to evaluate the performance of an analogue-based electronic meter for the cassava, sweet potato, and taro chips. The meter was calibrated against the oven-drying method, yielding high R2 values of the different root crops. The established calibration models were validated and revealed...