AgriEngineering, Volume 7, Issue 9 (September 2025) – 5 articles

Accurate postharvest quality evaluation of soybeans is essential for preserving product value and meeting industry standards. Traditional inspection methods are often inconsistent, labor-intensive, and unsuitable for high-throughput operations. This study presents a non-destructive soybean classification approach using a simplified reflectance-mode hyperspectral imaging system equipped with a single light source, eliminating the complexity and maintenance demands of dual-light configurations used in prior studies. A spectral–spatial data fusion strategy was developed to classify harvested soybeans into four categories: normal, split, diseased, and foreign materials such as stems and pods. The dataset consisted of 1140 soybean samples distributed across these four categories, with spectral reflectance features and spatial texture attributes extracted from each sample. These features were combined to form a unified feature representation for use in classification. Among multiple machine learning classifiers evaluated, Linear Discriminant Analysis (LDA) achieved the highest performance, with approximately 99% accuracy, 99.05% precision, 99.03% recall and 99.03% F1-score. When evaluated independently, spectral features alone resulted in 98.93% accuracy, while spatial features achieved 78.81%, highlighting the benefit of the fusion strategy. Overall, this study demonstrates that a single-illumination HSI system, combined with spectral–spatial fusion and machine learning, offers a practical and potentially scalable approach for non-destructive soybean quality evaluation, with applicability in automated industrial processing environments. Full article

Soybean (Glycine max L.) is one of the world’s most important sources of plant-based protein, with a protein content exceeding 35–40% (dry basis), along with other essential nutritional benefits. Ideally, soybeans are field-dried to approximately 13% moisture content (wet basis, wb); however, adverse weather conditions can necessitate harvesting at elevated moisture levels sometimes exceeding 20% (wb). In such cases, mechanized drying systems, particularly in northern U.S. regions, become essential for safe storage and quality preservation. This study investigated the effects of drying temperature, airflow rate, and initial moisture content on drying kinetics and kernel integrity using mathematical modeling. Drying behavior was modeled using fractional calculus and compared to the empirical Page model, while kernel cracking and breakage were analyzed using logistic regression. Both fractional and Page models exhibited strong agreement with experimental data (R² = 0.903–0.993). The fractional model achieved superior predictive accuracy, improving RMSE and MAE by 83.7% and 81.2%, respectively, compared to the Page model. Cracking and breakage were more strongly influenced by drying temperature than by initial moisture content, with the greatest quality degradation occurring at high temperatures. Optimal drying conditions were identified as temperatures below 27 °C and initial moisture contents between 19 and 20% (wb), which best preserved kernel quality. Logistic models more accurately predicted breakage than cracking, confirming their effectiveness in assessing mechanical damage during drying. The results affirm the suitability of fractional order models for accurately capturing drying kinetics, while logistic models offer robust performance for evaluating physical quality degradation. These modeling approaches provide a framework for efficient and quality-preserving soybean drying strategies in regions reliant on off-field drying systems. Full article

Monitoring the development of greenhouse crops is essential for optimising yield and ensuring the efficient use of resources. A system for monitoring hemp (Cannabis sativa L.) cultivation under greenhouse conditions using computer vision has been developed. This system is based on open-source automation software installed on a single-board computer. It integrates various temperature and humidity sensors and surveillance cameras, automating image capture. Hemp seeds of the Tiborszallasi variety were sown. After germination, plants were transplanted into pots. Five specimens were selected for growth monitoring by image analysis. A surveillance camera was placed in front of each plant. Different approaches were applied to analyse growth during the early stages: two traditional computer vision techniques and a deep learning algorithm. An average growth rate of 2.9 cm/day was determined, corresponding to 1.43 mm/°C day. A mean MAE value of 1.36 cm was obtained, and the results of the three approaches were very similar. After the first growth stage, the plants were subjected to water stress. An algorithm successfully identified healthy and stressed plants and also detected different stress levels, with an accuracy of 97%. These results demonstrate the system’s potential to provide objective and quantitative information on plant growth and physiological status. Full article

The precise identification of plant diseases is essential for improving agricultural productivity and reducing reliance on human expertise. Deep learning frameworks, belonging to the YOLO series, have demonstrated significant potential in the real-time detection of plant diseases. There are various factors influencing model performance; activation functions play an important role in improving both accuracy and efficiency. This study proposes αSiLU, a modified activation function developed to optimize the performance of YOLOv11n for plant disease-detection tasks. By integrating a scaling factor α into the standard SiLU function, αSiLU improved the effectiveness of feature extraction. Experiments are conducted on two different plant disease datasets—tomato and cucumber—to demonstrate that YOLOv11n models equipped with αSiLU outperform their counterparts using the conventional SiLU function. Specifically, with α = 1.05, mAP@50 increased by 1.1% for tomato and 0.2% for cucumber, while mAP@50–95 improved by 0.7% and 0.2% each. Additional evaluations across various YOLO versions confirmed consistently superior performance. Furthermore, notable enhancements in precision, recall, and F1-score were observed across multiple configurations. Crucially, αSiLU achieves these performance improvements with minimal effect on inference speed, thereby enhancing its appropriateness for application in practical agricultural contexts, particularly as hardware advancements progress. This study highlights the efficiency of αSiLU in the plant disease-detection task, showing the potential in applying deep learning models in intelligent agriculture. Full article

Two-way channel irrigation pumping stations are widely used along rivers for irrigation and drainage. Due to fluctuating internal and external water levels, these stations often operate under ultra-low or near-zero head conditions, leading to poor hydraulic performance. This study employs computational fluid dynamics (CFD) to investigate such systems’ pressure fluctuation and chaotic dynamic characteristics. A validated 3D model was developed, and the wavelet transform was used to perform time–frequency analysis of pressure signals. Phase space reconstruction and the Grassberger–Procaccia (G–P) algorithm were applied to evaluate chaotic behavior using the maximum Lyapunov exponent and correlation dimension. Results show that low frequencies dominate pressure fluctuations at the impeller inlet and guide vane outlet, while high-frequency components increase significantly at the intake bell mouth and outlet channel. The maximum Lyapunov exponent in the impeller and guide vane regions reaches 0.0078, indicating strong chaotic behavior, while negative values in the intake and outlet regions suggest weak or no chaos. This integrated method provides quantitative insights into the unsteady flow mechanisms, supporting improved stability and efficiency in ultra-low-head pumping systems. Full article