AgriEngineering

Agricultural tractors possess front loaders that are employed for the handling and transportation of materials, but are exposed to mechanical vibrations and shocks from ground undulations and sudden variations in the load. These vibrations are harmful to the durability of the parts, the comfort of the driver, and the longevity of the machine. In this current study, the performance of the hydraulic accumulator to mitigate such vibrations for a Foton 904 wheeled tractor equipped with a TZ10C-824 front loader is studied. Vibration measurements were taken by an experimental Brüel & Kjær 3050-A040 analyzer under various loading configurations (no loading, 180 kg, and 312 kg), with or without a 1.4 L, 50-bar nitrogen gas-charged Fox Opera Mi Italy hydraulic accumulator. Results reveal that maximum accelerations were as much as 6.24 m·s⁻² without an accumulator during testing of a 312 kg load, whereas they were extremely low at 2.66 m·s⁻² when the accumulator was activated. Frequency-domain analysis verified that the main vibrations were within the range of 3–4 Hz, with FFT peak amplitudes dropping from 5.6 m·s⁻² to 2.4 m·s⁻² upon the accumulator’s operation. The observations verify the effectiveness of the accumulator in vibration intensity reduction, absence of high-frequency shock loads, and ride comfort, along with structural safety improvement. The study provides a solid platform for further enhancement in vibration control techniques for agricultural machines and loader system design. Full article

The environmental burden of widely used protein sources in animal feeds, such as soybean and fishmeal, has raised concerns about the sustainability of current livestock production systems. In response, alternative protein sources are being explored, with insect meal emerging as a promising candidate. This study conducted a comparative Life Cycle Assessment (LCA) of four compound pig feed formulations differing in protein composition, incorporating soybean meal, fishmeal, and Tenebrio molitor (insect) meal. The LCA followed ISO 14040/44 standards and applied both mass-based and protein-based functional units (FUs) to examine how FU choice influences environmental outcomes. Results showed that crop-derived ingredients, particularly soybean meal, drove most environmental burdens due to land use change and fertilizer inputs. Replacing soybean with insect meal led to impact reductions in key environmental categories. Insect meal’s scalability, efficient land use, and potential waste valorisation supported its role as a sustainable alternative. The study also highlighted key sustainability issues not assessed by LCA, such as overfishing and ecosystem disruption, associated with fishmeal. Overall, insect meal appeared to be a strong replacement for soybean and fishmeal, with soy substitution proving key to reducing environmental burdens. Finally, the protein-based FU was more relevant given the study’s nutritional focus. Full article

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

Blueberries are in high demand worldwide because of their taste and functional components. However, the shelf life of blueberries is short owing to their perishability and rapid quality deterioration. Therefore, a sterilization technology must be developed that can extend the shelf life of blueberries while maintaining their appearance and taste. As such, we verified the effectiveness of three pre-storage sterilization treatments (UV-C, ozone gas, and ozone water) using mercury-free excimer UV lamps that did not adversely affect the environment. We then created a device that continuously treated blueberries with approximately 2.57 ppm of ozone gas to ensure sterilization during the storage period, and we verified the effectiveness of the device. We found that the pre-storage ozone treatment reduced the number of fungi on the blueberry surface without adversely affecting fruit quality. The continuous ozone treatment suppressed the decrease in anthocyanin content, further reduced the number of fungi on the fruit surface and maintained fruit appearance for a longer period compared with the control. This suggests that continuous low-concentration ozone treatment suppresses the decay and extends the storage period of blueberries intended for raw consumption. Full article

Rice, wheat, and maize are important food grains consumed by most of the population in Asian countries (like India, Japan, Singapore, Malaysia, China, and Thailand). These crops’ production is affected by biotic and abiotic factors that cause diseases in several parts of the crops (including leaves, stems, roots, nodes, and panicles). A severe infection affects the growth of the plant, thereby undermining the economy of a country, if not detected at an early stage. This may cause extensive damage to crops, resulting in decreased yield and productivity. Early safeguarding methods are overlooked because of farmers’ lack of awareness and the variety of crop diseases. This causes significant crop damage and can consequently lower productivity. In this manuscript, a lightweight vision transformer (MaxViT) with 814.7 K learnable parameters and 85 layers is designed for classifying crop diseases in paddy and wheat. The MaxViT DNN architecture consists of a convolutional block attention module (CBAM), squeeze and excitation (SE), and depth-wise (DW) convolution, followed by a ConvNeXt module. This network architecture enhances feature representation by eliminating redundant information (using CBAM) and aggregating spatial information (using SE), and spatial filtering by the DW layer cumulatively enhances the overall classification performance. The proposed model was tested using a paddy dataset (with 7857 images and eight classes, obtained from local paddy farms in Lalgudi district, Tiruchirappalli) and a wheat dataset (with 5000 images and five classes, downloaded from the Kaggle platform). The model’s classification performance for various diseases has been evaluated based on accuracy, sensitivity, specificity, mean accuracy, precision, F1-score, and MCC. During training and testing, the model’s overall accuracy on the paddy dataset was 99.43% and 98.47%, respectively. Training and testing accuracies were 94% and 92.8%, respectively, for the wheat dataset. Ablation analysis was carried out to study the significant contribution of each module to improving the performance. It was found that the model’s performance was immune to the presence of noise. Additionally, there are a minimal number of parameters involved in the proposed model as compared to pre-trained networks, which ensures that the model trains faster. Full article

It is critical to provide proper environmental conditions in poultry houses to maintain birds’ health, boost productivity, and improve the overall economic viability of the poultry industry. Among the myriad of environmental elements, indoor air quality has been a determining factor that directly affects poultry well-being. Elevated concentrations of harmful gases—in particular Carbon Dioxide ($C{O}_2$), Methane ($C{H}_4$), and Ammonia ($N{H}_3$)—decomposition products of poultry litter, feed wastage, and biological processes have draconian effects on bird health, feed efficiency, the growth rate, reproduction efficiency, and mortality rate. Despite their importance, traditional air quality monitoring systems are often operated manually, labor intensive, and cannot detect sudden environmental changes due to the lack of real-time sensing. To overcome these limitations, this paper presents an interdisciplinary approach combining cloud computing, Artificial Intelligence (AI), and Internet of Things (IoT) technologies to measure real-time poultry gas concentrations. Real-time sensor feeds are transmitted to a cloud-based platform, which stores, displays, and processes the data. Furthermore, a machine learning (ML) model was trained using historical sensory data to predict the next-day gas emission levels. A web-based platform has been developed to enable convenient user interaction and display the gas sensory readings on an interactive dashboard. Also, the developed system triggers automatic alerts when gas levels cross safe environmental thresholds. Experimental results of $C{O}_2$ concentrations showed a significant diurnal trend, peaking in the afternoon, followed by the evening, and reaching their lowest levels in the morning. In particular, $C{O}_2$ concentrations peaked at approximately 570 ppm during the afternoon, a value that was significantly elevated (p < 0.001) compared to those recorded in the evening (~560 ppm) and morning (~555 ppm). This finding indicates a distinct diurnal pattern in $C{O}_2$ accumulation, with peak concentrations occurring during the warmer afternoon hours. Full article

This study applied geostatistics to analyze thermal images of the back surface of pigs’ ears (TSO) to understand how spatial temperature variability influences thermoregulation. The objective was to assess TSO variability in pigs housed under two climate control systems, namely, pens without cooling (BTEST) and with an evaporative cooling system (BECS), using infrared thermography and geostatistical tools. A total of 432 thermal images were obtained from 18 finishing pigs at 08:00, 12:00, and 16:00. Semivariograms were modeled and validated, and kriging maps were developed to visualize the spatial temperature distribution. The pens were thermally characterized using reclassified Temperature and Humidity Index (THI) values. The Gaussian model (R² > 0.9) showed strong spatial dependence in temperature data. Pigs in the BECS system exhibited lower average TSO temperatures (28.2–38.6 °C) than those in the BTEST system, where temperatures exceeded 34 °C, highlighting the role of cooling in mitigating heat stress. In both systems, higher THI values were associated with increased TSO, indicating thermal discomfort under elevated environmental temperatures. Geostatistical analysis effectively revealed spatial patterns and variability in surface temperatures, providing key insights into how environmental conditions impact pigs’ thermal responses. Full article

Spectral reflectance of plants can be readily associated with physiological and biochemical parameters. Thus, relating spectral data to amino acid contents in different genetic materials provides an innovative and efficient approach for understanding and managing genetic diversity. Therefore, this study had two objectives: (I) to differentiate genetic materials according to amino acid contents and spectral reflectance; (II) to establish the relationship between amino acids and spectral bands derived from hyperspectral data. The research was conducted with 32 soybean genetic materials grown in the field during the 2023–2024 crop year. The experimental design involved randomized blocks with four replicates. Leaf spectral data were collected 60 days after plant emergence, when the plants were in full bloom. Three leaf samples were collected from the third fully developed trifoliate leaf, counted from top to bottom, from each plot. The samples were taken to the laboratory, where reflectance readings were obtained using a spectroradiometer, which can measure the 350–2500 nm spectrum. Wavelengths were grouped as means of representative intervals and then organized into 28 bands. Subsequently, the leaf samples from each plot were subjected to quantification analyses for 17 amino acids. Then, the soybean genotypes were subjected to a PCA–K-means analysis to separate the genotypes according to their amino acid content and spectral behavior. A correlation network was constructed to investigate the relationships between the spectral variables and between the amino acids within each group. The groups formed by the different genetic materials exhibited distinct profiles in both amino acid composition and spectral behavior. Leaf reflectance data proved to be efficient in identifying differences between soybean genotypes regarding the amino acid content in the leaves. Leaf reflectance was effective in distinguishing soybean genotypes according to leaf amino acid content. Specific and high-magnitude associations were found between spectral bands and amino acids. Our findings reveal that spectral reflectance can serve as a reliable, non-destructive indicator of amino acid composition in soybean leaves, supporting advanced phenotyping and selection in breeding programs. Full article

Evaluating the response of maize crops to different nitrogen fertilization rates is essential to ensure their agronomic, environmental, and economic efficiency. In this study, the spectral information of maize plants subjected to five distinct nitrogen fertilization strategies was analyzed. The fertilization strategies were based on the practices commonly used in maize fields in the study area, with the aim of ensuring the research findings’ applicability. The spectral reflectance was measured using a spectroradiometer covering the 350–2500 nm range, and the results enabled the identification of optimal spectral regions for monitoring plants’ nitrogen status, particularly in the visible and infrared ranges. A Principal Component Analysis (PCA) of the reflectance data revealed the key wavelengths most sensitive to the nitrogen availability: 555 nm and 720 nm during the vegetative stage and 680 nm during the reproductive stage. This information will support the development of drone-mounted multispectral sensor systems for large-scale monitoring, as well as the design of low-cost sensors for early nitrogen deficiency detection. Furthermore, the study demonstrated the feasibility of estimating the cornstalk nitrate content based on direct reflectance measurements of maize stems. The prediction model showed satisfactory performance, with a coefficient of determination (R²) of 0.845 and a root mean square error of prediction (RMSECV) of 2035.3 ppm, indicating its strong potential for predicting the NO₃-N concentrations in maize stems. Full article

Heavy machinery degrades agricultural soils, with severity influenced by wheel type, contact area, and soil moisture. Tropical agriculture is characterized by the constant maintenance of straw on the ground. This permanent cover, among other benefits, can mitigate the stress imposed by wheels on the physical structure of the soil. This study aimed to evaluate the effect of tire types and straw amounts on soil stresses. Static studies were carried out under controlled conditions in a static tire test unit (STTU), equipped with standardized sensors and systems that simulated real farming conditions. Three tire models were tested: road truck double wheelset—2 × 275/80R22.5 (p1); agricultural radial tire—600/50R22.5 (p2); and bias-ply tire—600/50-22.5 (p3) on four contact surfaces (rigid surface; bare soil; soil with 15 and 30 Mg ha⁻¹ straw cover). We performed comparative statistical tests and subsurface stress simulations for each tire and surface condition. On the hard surface, the contact areas were 4.7 to 6.8 times smaller than on bare soil. Straw increased the tire’s contact area, reducing compaction and subsoil stresses. Highest pressure was imposed by the road tire (p1) and lowest by the radial tire (p2). Adding 15 Mg ha⁻¹ of straw reduced soil SPR by 18%, while increasing it to 30 Mg ha⁻¹ led to an additional 8% reduction. Tire selection and effective straw management improve soil conservation and agriculture sustainability. Full article

Existing residual film recovery machines for the cultivated layer struggle to achieve an efficient separation of plastic film from impurities during operation. To address this challenge, a wind-separation-type residual film recovery machine equipped with a cross-flow fan was designed. Through theoretical analysis and field tests, the optimal operational parameters were determined. In this paper, with the recovery rate and impurity content of the residual film were taken as the test objectives, with the air inlet angle, fan speed, and the proportion of residual film quality as the test factors. The response surface diagram was drawn with design-expert software, and the optimal parameters of the fan device obtained by orthogonal test range analysis and variance analysis were that when the air inlet angle was 31°, the fan speed was 1277 r/min, and the residual film mass accounted for 62%. Under these parameters, the residual film recovery rate was 88.1%, and the impurity content was 3.7%, which met the operational requirements of residual film recovery, and the research results could provide a basis for the recovery of residual film in the tillage layer. Full article

Understanding the combined gravi-phototropic behavior of plants is essential for space agriculture. Existing single-axis clinostats and gel-based grow media provide limited simulation fidelity. This study developed a Cloud-enabled triple-axis clinostat with built-in automated aeroponic and artificial photosynthetic lighting systems for Earth-based simulation under Martian gravity ranging from 0.35 to 0.4 g. Finite element analysis validated the stability and reliability of the acrylic and stainless steel rotating platform based on stress, strain, and thermal simulation tests. Arduino UNO microcontrollers were used to acquire and process sensor data to activate clinorotation and controlled environment systems. An Arduino ESP32 transmits grow chamber temperature, humidity, moisture, light intensity, and gravity sensor data to ThingSpeak and the Create IoT online platform for seamless monitoring and storage of enviro-physical data. The developed system can generate 0.252–0.460 g that suits the target Martian gravity. The combined gravi-phototropic tests confirmed that maize seedlings exposed to partial gravity and grown using the aeroponic approach have a shoot system growth driven by light availability (395–400 μmol/m²/s) across the partial gravity extremes. Root elongation is more responsive to gravity increase under higher partial gravity (0.375–0.4 g) even with low light availability. The developed soilless clinostat technology offers a scalable tool for simulating other high-value crops aside from maize. Full article

Soil moisture prediction is a key parameter for effective irrigation scheduling and water use efficiency. However, accurate long-term prediction remains challenging, as most existing models excel in short- to medium-term prediction but struggle to capture the complex temporal dependencies and non-linear interactions of soil moisture variables over extended horizons. This study proposes a hybrid soil moisture prediction method, integrating a long short-term memory (LSTM) network and extreme gradient boosting (XGBoost) model for multistep soil moisture prediction at 24 h, 72 h, and 168 h horizons. The LSTM captures temporal dependencies and extracts high-level features from the dataset, which are then used by XGBoost for final predictions. The study uses real-world data from the D.A.T.A (Demonstrating Applied Technology in Agriculture) research farm at ABAC (Abraham Baldwin Agricultural College) Tifton, GA, USA, utilizing watermark soil moisture sensors and weather station’s data installed on the farm. Results show that the proposed method outperforms other hybrid models, achieving R² values of 98.67%, 98.54%, and 98.56% for 24, 72, and 168 h predictions, respectively. The study findings highlight that LSTM-XGBoost offers a precise long-term soil moisture prediction, making it a practical tool for real-time irrigation scheduling, enhancing water use efficiency in precision agriculture. Full article

Crop rotation is a well-established practice that helps reduce nutrient depletion and pressure from pests and weeds. At the same time, the use of artificial intelligence tools to recognize crops from satellite multispectral imagery is gaining momentum as a first step toward automated agricultural monitoring. However, the recognition process is limited by inherent errors and the scarcity of available data. In this paper, we build upon Monte Carlo simulation methods to investigate whether incorporating crop rotation information—encoded as a Markov chain—can improve identification accuracy. To broaden the simulation across diverse datasets, we also synthesize multispectral pixels for underrepresented crop types. Crop rotation is used not only in post-processing, but also integrated into the classifier, where a Gradient Boosting Machine is adapted to penalize learners that predict the same crop as in the previous year. Our evaluation uses Sentinel satellite imagery of agricultural crops, combined with the DACIA5 database from the Brașov region of Romania. We conclude that incorporating accurate prior information and crop rotation models noticeably improves crop identification performance. Synthesized data further enhances recognition rates and enables broader applicability, beyond the original region. Full article

Different crop production scenarios and crop rotation systems should be investigated with lower greenhouse gas (GHG) intensity levels, with it being possible to reach net-zero GHG emissions from grain production farms. This study was divided into three stages—the development of spreadsheets for data acquisition for each crop rotation, calculations of GHG emissions based on IPCC methodologies and specific regional emission factors, and an analysis of the main emissions and sinks sources we evaluated, including the potential for soil and biomass carbon (C) sequestration to offset agricultural emissions. The system C footprints were 2413, 2209, and 2096 kg CO₂eq ha⁻¹ for farms K, M, and G, respectively, demanding estimated C sequestration (soil or biomass) rates of 657, 602, and 571 kg C ha⁻¹ year⁻¹ to offset all emissions of agricultural phases. Mitigating practices can reduce GHG emissions, but compensation via sequestration (soil or biomass C) shall be required to achieve zero GHG emissions. Reserving approximately 10–15% of the farm’s total agricultural production area to plant native trees or eucalyptus in marginal areas or even introducing crop–livestock–forest integration or crop–forest integration systems can offset the GHG emissions of the entire agricultural production phase, considering the potential for soil and biomass C sequestration, showing that it is a feasible option for producing C credit from the agricultural sector. Full article