Applied Sciences

Research

12 pages, 4625 KB

Open AccessArticle

Autonomous Intelligent Irrigation Systems in Hop Plantations (Republic of Chuvashia, Russia)

by Sergey A. Vasiliev, Vladimir P. Filippov, Victor V. Alekseev, Evgeny A. Maksimov and Evgeny V. Abakumov

Appl. Sci. 2026, 16(15), 7425; https://doi.org/10.3390/app16157425 - 24 Jul 2026

Viewed by 267

Abstract

The possibility of implementing intelligent irrigation has a number of undeniable advantages, mainly including the fact that the time can be determined and the volume of irrigation water can be adapted to specific plant types on a specific soil. A neural network has [...] Read more.

The possibility of implementing intelligent irrigation has a number of undeniable advantages, mainly including the fact that the time can be determined and the volume of irrigation water can be adapted to specific plant types on a specific soil. A neural network has been trained to describe the dynamics of soil moisture based on the basic soil water retention curve (SWRC). It is able to take into account a wide range of input data, such as the specific surface area of the solid phase of soils, porosity, humidity, etc., for a given initial soil moisture profile. Preference is given to a recurrent neural network, since this type works well with sequential data and is able to take into account time dependence and solve the problem of decaying gradients of soil hydrophysical properties. The neural network processes the vector of incoming signs—humidity, temperature, volume of incoming/outgoing water, etc.—and connects them with the dynamics of humidity from sensors located at different depths. When modeling mass–salt transfer with different boundary and initial conditions, the dependence of moisture retention on the moisture conductivity function is used, which allows us to calculate how moisture with dissolved nutrients moves through the soil under the influence of pressure and concentration gradients. Since the SWRC is constructed as a function of directly measured data, it is easy to set it for each point of interest in the field and at each depth. During modeling, the soil is divided into elementary volumes (from 2–3 mm to 1 cm), and an array with data sets is compiled at each point. The research was conducted in a real hop plantation (the village of Opytny, Tsivilsky district, Republic of Chuvashia). The values of the soil moisture sensors at different depths, together with the data from the portable weather station, are sent to the input of the neural network. According to the minimum allowable humidity for hops, the model predicts situations when humidity reaches critical values and initiates watering. Thus, the implemented approach makes it possible to automate irrigation management, increase water use efficiency and ensure optimal conditions for plants. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

31 pages, 4570 KB

Open AccessArticle

An IWMA-Optimized LightGBM Model for Early Ketosis Risk Screening in Dairy Cows Using DHI Data

by Yang Yang, Yongqiang Dai, Huan Liu and Rui Guo

Appl. Sci. 2026, 16(10), 5050; https://doi.org/10.3390/app16105050 - 19 May 2026

Viewed by 303

Abstract

Ketosis is a prevalent metabolic disorder in early-lactation dairy cows, significantly affecting animal health, milk production, and farm profitability. Developing accurate and non-invasive methods for early risk detection is therefore of critical importance. In this study, a hybrid optimization framework integrating an Improved [...] Read more.

Ketosis is a prevalent metabolic disorder in early-lactation dairy cows, significantly affecting animal health, milk production, and farm profitability. Developing accurate and non-invasive methods for early risk detection is therefore of critical importance. In this study, a hybrid optimization framework integrating an Improved Whale Migration Algorithm (IWMA) with a Light Gradient Boosting Machine (LightGBM) is proposed to predict ketosis risk based on the milk fat-to-protein ratio (F/P) using Dairy Herd Improvement (DHI) records. The proposed IWMA enhances optimization performance through cubic chaotic initialization, elite opposition-based learning, and a Cauchy–Gaussian hybrid mutation strategy, enabling improved global exploration and convergence stability. A dataset comprising 25,155 DHI records collected from multiple commercial dairy farms over seven months was used for model development and evaluation. Experimental results demonstrate that the IWMA–LightGBM model achieves a classification accuracy of 0.8997 and a mean squared error of 0.289, consistently outperforming six benchmark optimization methods. Feature analysis identifies Herd Within Index (WHI), Energy Corrected Milk (ECM), Days in Milk (DIM), Milk Urea Nitrogen, and Foremilk as key predictors associated with metabolic risk. Overall, the proposed approach provides a robust and effective non-invasive solution for early-stage metabolic risk screening at the herd level, offering practical value for precision dairy management. It should be noted that the model is intended for risk assessment rather than clinical diagnosis of ketosis. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

23 pages, 3094 KB

Open AccessArticle

Improved Unsupervised Domain Generalization by Leaf-Bridge for Soybean Leaf Disease Identification

by Jiaqi Cui, Jinpeng Gu and Qiufeng Wu

Appl. Sci. 2026, 16(7), 3520; https://doi.org/10.3390/app16073520 - 3 Apr 2026

Viewed by 449

Abstract

Soybean diseases are the main factors causing serious yield reduction in soybeans. Therefore, an automatic identification method for soybean leaf diseases is an urgent need to develop. However, most soybean leaf disease recognition models have problems such as weak generalization ability and insufficient [...] Read more.

Soybean diseases are the main factors causing serious yield reduction in soybeans. Therefore, an automatic identification method for soybean leaf diseases is an urgent need to develop. However, most soybean leaf disease recognition models have problems such as weak generalization ability and insufficient labeled soybean images. To overcome these problems, this paper proposes an improved method for unsupervised domain generalization (UDG) named Leaf-Bridge Across Domains (LBADs), which is dedicated to cross-domain soybean leaf disease image recognition. Our method builds a semantic-preserving image-to-image mapping between each training domain and an auxiliary leaf-bridge domain to achieve cross-domain semantic alignment of soybean disease images in the shared feature space through fully unsupervised self-supervised contrastive learning. Firstly, LBAD is performed on both labeled source and target domain data. To bridge the gap between these two domains, it constructs a cross-domain aided leaf-bridge domain within a contrastive self-supervised model, mapping images from different domains into a shared space to achieve semantic alignment. After the fully unsupervised pre-training of the feature backbone, we use 1%, 5%, and 10% of the labeled source domain images, respectively, to fine-tune the linear classifier only (the pre-trained backbone weights are completely frozen) and complete the classification of unlabeled target domain images. The highest accuracy of the three-label fraction can reach 83.56%, 89.22%, and 87.2%, respectively, higher than that of the model without LBAD. The final identification result is also higher than the accuracy of the model trained without LBAD. Experimental results showed that LBAD can improve the generalization ability of the model and obtain a more accurate soybean leaf disease recognition model. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

29 pages, 7114 KB

Open AccessArticle

Modeling and Experimental Study of Fuzzy Control System for Operating Parameters of Grain Combine Harvester Cleaning Device

by Jing Pang, Yahao Tian, Zhanchao Dai, Zhe Du, Fengkui Dang, Xinqi Chen and Xinping Li

Appl. Sci. 2026, 16(7), 3137; https://doi.org/10.3390/app16073137 - 24 Mar 2026

Cited by 1 | Viewed by 449

Abstract

The cleaning unit is a key functional component of grain combine harvesters, yet its operating parameters are still predominantly adjusted according to operator experience, resulting in limited adaptability to fluctuating working conditions. To enhance the intelligence and stability of the cleaning process, this [...] Read more.

The cleaning unit is a key functional component of grain combine harvesters, yet its operating parameters are still predominantly adjusted according to operator experience, resulting in limited adaptability to fluctuating working conditions. To enhance the intelligence and stability of the cleaning process, this study develops a fuzzy control approach supported by data-driven performance modeling. Based on multi-condition bench experiments, feeding rate, fan speed, cleaning sieve vibration frequency, and sieve opening were selected as input variables. Gaussian Process Regression (GPR) models were established to describe the nonlinear relationships between operating parameters and cleaning loss rate and impurity rate, and impurity rate was inferred online to compensate for the absence of a reliable sensor. Taking feeding rate variation as the primary disturbance, a dual-input fuzzy control strategy was designed using loss rate monitoring and model-predicted impurity rate as feedback signals. Simulation and bench test results show that, under small and moderate load disturbances (±20% and ±35%), the proposed method reduces either impurity rate or cleaning loss rate through coordinated parameter adjustment. Under large disturbances (±50%), performance deterioration cannot be fully eliminated, but its extent is alleviated compared with open-loop conditions. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

31 pages, 5841 KB

Open AccessArticle

An Improved Real-Time Lightweight YOLOv8 Algorithm for Bloom Maturity Recognition in Torreya grandis

by Qipeng Li, Kang Li and Songwei Zeng

Appl. Sci. 2026, 16(5), 2177; https://doi.org/10.3390/app16052177 - 24 Feb 2026

Viewed by 636

Abstract

To address the challenge of rapidly and accurately detecting male cones of Torreya at various stages of maturity in natural environments, this study presents a target detection algorithm, GFM-YOLOv8s, which is based on an improved YOLOv8s model. Using the YOLOv8s network model as [...] Read more.

To address the challenge of rapidly and accurately detecting male cones of Torreya at various stages of maturity in natural environments, this study presents a target detection algorithm, GFM-YOLOv8s, which is based on an improved YOLOv8s model. Using the YOLOv8s network model as the foundation, this study replaces the backbone feature extraction network with c2f-faster-ema to lighten the model and simultaneously enhance its ability to capture and express important image features. Additionally, the PAN-FPN feature extraction structure in the neck is substituted with a BiFPN structure. By removing less contributive nodes and adding cross-layer connections, the algorithm achieves better fusion and utilization of features at different scales. The WIoU loss function is introduced to mitigate the mismatch in orientation between the predicted and ground truth bounding boxes. Furthermore, a structured pruning strategy was applied to the optimized network, significantly reducing redundant parameters while preserving accuracy. Results: The improved GFM-YOLOv8 has a detection accuracy of 88.2% for Torreya male cones, the detection time of a single image is 8.3 ms, and the model size is 4.44 M, while the FPS rate is 120 frames, and the parameter size is 2.20 × 10⁶. Compared with the original YOLOv8s algorithm, map50 and recall are increased by 2.0% and 2.0%, and the model size and parameters are reduced by 79.2% and 80.1%. On the Jetson Orin Nano, it runs at 24 FPS. The refined lightweight model can swiftly and accurately detect male cones of Torreya at different stages of maturity in natural settings, providing technical support for the visual recognition system used in growth monitoring at Torreya bases. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

20 pages, 4349 KB

Open AccessArticle

Agricultural Carbon Flux Estimation Using Multi-Source Remote Sensing and Ensemble Models

by Jiang Qiu, Qinrong Li, Weiyu Yu and Jinping Chen

Appl. Sci. 2026, 16(4), 2118; https://doi.org/10.3390/app16042118 - 22 Feb 2026

Viewed by 619

Abstract

To accurately understand and investigate carbon fluxes in cropland ecosystems, this study adopted a machine learning ensemble model for estimation. Focusing on the Jinzhou station of the ChinaFLUX, we integrated eddy covariance carbon flux observations with multi-source satellite remote sensing data to construct [...] Read more.

To accurately understand and investigate carbon fluxes in cropland ecosystems, this study adopted a machine learning ensemble model for estimation. Focusing on the Jinzhou station of the ChinaFLUX, we integrated eddy covariance carbon flux observations with multi-source satellite remote sensing data to construct a machine learning-based cropland carbon flux estimation model. For environmental driver selection, a strategy combining correlation analysis with ecological mechanism understanding was employed to screen LST, NDVI, and NDMI as model input variables, effectively avoiding multicollinearity issues. Using footprint-weighted integrated data from 2005 to 2014 for model training and validation, a Stacking ensemble model was constructed with the RF model serving as the meta-learner to stack the predictions of RF, CART, and GBM. The ensemble model further reduced the prediction error (RMSE = 39.82), maintaining an R² > 0.9 in most years and effectively improving predictive performance during anomalous years where single models underperformed. Based on these findings, the model was applied to analyze the spatiotemporal evolution of NEE in Jinzhou croplands from 2005 to 2014. The analysis revealed that while the region functioned overall as a carbon sink, it exhibited significant spatiotemporal heterogeneity. Spatially, the distribution followed a pattern of “strong intensity in the northeast and center, and weak intensity in the northwest and southwest.” Temporally, the sink intensity underwent significant interannual oscillations characterized by a “strengthening–weakening–re-strengthening–declining” trajectory. The high-precision prediction method proposed in this study is of great significance for revealing spatiotemporal variations in carbon sources/sinks, guiding green agricultural development, and supporting relevant policy formulation. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

29 pages, 4138 KB

Open AccessArticle

Predictive Modeling of Massey Ferguson Tractor Performance Parameters Using Artificial Neural Network Methodology

by Saleh M. Al-Sager, Saad S. Almady, Waleed A. Almasoud, Saad A. Al-Hamed, Abdulrahman A. Al-Janobi and Abdulwahed M. Aboukarima

Appl. Sci. 2026, 16(4), 1818; https://doi.org/10.3390/app16041818 - 12 Feb 2026

Cited by 1 | Viewed by 583

Abstract

Predicting tractor performance factors accurately is crucial for enhancing energy efficiency and assisting with the choice of machinery in agricultural operations. Using the Nebraska Tractor Test Laboratory (NTTL) identical data, this study uses artificial neural network (ANN) modeling to forecast important performance metrics [...] Read more.

Predicting tractor performance factors accurately is crucial for enhancing energy efficiency and assisting with the choice of machinery in agricultural operations. Using the Nebraska Tractor Test Laboratory (NTTL) identical data, this study uses artificial neural network (ANN) modeling to forecast important performance metrics of a front wheel assist (FWA) Massey Ferguson tractor. A feed-forward ANN model was developed and validated using reported data from official tractor tests. Performance indicators, such as drawbar pull (kN), drawbar power (kW), hourly fuel consumption rate (kg/h), drawbar specific fuel consumption (kg/kW·h), and drawbar specific volumetric fuel efficiency (kW/kg·h), were utilized as outputs and certain operational factors, the tractor characteristics variables as well as other variables were used as inputs. Statistical measures, including the coefficient of determination and error metrics from training and testing datasets, were used to assess the model’s performance. The results showed that the ANN model produced excellent generalization capabilities and good prediction performance by correctly capturing the nonlinear correlations between inputs and tractor performance indicators. The suggested strategy performed better than traditional regression-based techniques documented in the literature, especially when operation variables and tractor characteristics varied. The results show that combining NTTL data with ANN techniques offers a dependable and affordable method for predicting tractor performance indicators and evaluating energy efficiency. This eliminates the need for extensive experimental procedures and promotes data-driven decision-making in agricultural machinery management. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

12 pages, 1180 KB

Open AccessArticle

Optimal Color Space Selection for Vermicompost Nitrogen Classification: A Comparative Study Using the KNN Model

by Panida Lorwongtragool and Suthisa Leasen

Appl. Sci. 2025, 15(21), 11578; https://doi.org/10.3390/app152111578 - 29 Oct 2025

Cited by 1 | Viewed by 868

Abstract

This study presents a cost-effective and accurate method for assessing nitrogen concentration in vermicompost fertilizer using a low-cost TCS3200 color sensor and a K-Nearest Neighbors (KNN) machine learning model. The objective was to evaluate the performance of four different color spaces—RGB, Lab, LCh, [...] Read more.

This study presents a cost-effective and accurate method for assessing nitrogen concentration in vermicompost fertilizer using a low-cost TCS3200 color sensor and a K-Nearest Neighbors (KNN) machine learning model. The objective was to evaluate the performance of four different color spaces—RGB, Lab, LCh, and CMYK—identify the most effective feature representation for a multi-class classification task based on accuracy and theoretical robustness to ambient light variations. A total of 2400 data points were collected from a standard chemical test kit and processed. A rigorous 60-fold cross-validation approach was used to determine the optimal model hyperparameters and to ensure the robustness of the findings. The results demonstrate that the model trained on the LCh color space achieved the highest classification accuracy of 0.9708 with an optimal K-value of 6, significantly outperforming Lab (0.9688), RGB (0.9625), and CMYK (0.9583). A detailed analysis of the confusion matrix revealed that the model successfully classified the ‘High’ and ‘Medium’ nitrogen levels with near-perfect accuracy, while minor misclassifications occurred between the ‘Low’ and ‘Trace’ categories (5 Low ⟶ Trace, 6 Trace ⟶ Low). The proposed system offers a practical, robust, and accessible tool for precision agriculture, enabling farmers to make informed decisions regarding fertilization, and directly supporting sustainable agriculture and responsible resource management. The findings indicate that the LCh color space is highly effective for this application, providing a viable solution for the rapid and reliable assessment of vermicompost quality. Most importantly, this inexpensive, on-site system removes the need for costly, time-consuming laboratory analyses, giving farmers and compost users the instantaneous, accurate nitrogen data they need to maximize crop yield, optimize nutrient application, and significantly reduce input costs from overfertilization. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

24 pages, 12281 KB

Open AccessArticle

Prediction Method of Available Nitrogen in Red Soil Based on BWO-CNN-LSTM

by Yun Deng, Yuchen Cao and Chang Liu

Appl. Sci. 2025, 15(20), 11077; https://doi.org/10.3390/app152011077 - 16 Oct 2025

Viewed by 812

Abstract

Accurate assessment of forest soil nitrogen from hyperspectral spectra is critical for precision fertilization, yet conventional preprocessing and baseline CNNs constrain predictive accuracy. We introduce streamlined spectral preprocessing and an optimized CNN–LSTM framework and evaluate it on Guangxi forest soils against competitive models [...] Read more.

Accurate assessment of forest soil nitrogen from hyperspectral spectra is critical for precision fertilization, yet conventional preprocessing and baseline CNNs constrain predictive accuracy. We introduce streamlined spectral preprocessing and an optimized CNN–LSTM framework and evaluate it on Guangxi forest soils against competitive models using standard validation metrics. Results: The proposed approach outperformed comparative models (CNN, LSTM, and BiLSTM), achieving a validation set R² of 0.889 and RMSE of 16.5722, representing improvements of 6.79–10.37% in R² and 18.60–24.44% in RMSE over baseline methods. The method delivers accurate, scalable nitrogen estimation from spectra, supporting timely fertilization decisions and sustainable soil management. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

22 pages, 4583 KB

Open AccessArticle

MemGanomaly: Memory-Augmented Ganomaly for Frost- and Heat-Damaged Crop Detection

by Jun Park, Sung-Wook Park, Yong-Seok Kim, Se-Hoon Jung and Chun-Bo Sim

Appl. Sci. 2025, 15(19), 10503; https://doi.org/10.3390/app151910503 - 28 Sep 2025

Viewed by 795

Abstract

Climate change poses significant challenges to agriculture, leading to increased crop damage owing to extreme weather conditions. Detecting and analyzing such damage is crucial for mitigating its effects on crop yield. This study proposes a novel autoencoder (AE)-based model, termed “Memory Ganomaly,” designed [...] Read more.

Climate change poses significant challenges to agriculture, leading to increased crop damage owing to extreme weather conditions. Detecting and analyzing such damage is crucial for mitigating its effects on crop yield. This study proposes a novel autoencoder (AE)-based model, termed “Memory Ganomaly,” designed to detect and analyze weather-induced crop damage under conditions of significant class imbalance. The model integrates memory modules into the Ganomaly architecture, thereby enhancing its ability to identify anomalies by focusing on normal (undamaged) states. The proposed model was evaluated using apple and peach datasets, which included both damaged and undamaged images, and was compared with existing robust Convolutional neural network (CNN) models (ResNet-50, EfficientNet-B3, and ResNeXt-50) and AE models (Ganomaly and MemAE). Although these CNN models are not the latest technologies, they are still highly effective for image classification tasks and are deemed suitable for comparative analyses. The results showed that CNN and Transformer baselines achieved very high overall accuracy (94–98%) but completely failed to identify damaged samples, with precision and recall equal to zero under severe class imbalance. Few-shot learning partially alleviated this issue (up to 75.1% recall in the 20-shot setting for the apple dataset) but still lagged behind AE-based approaches in terms of accuracy and precision. In contrast, the proposed Memory Ganomaly delivered a more balanced performance across accuracy, precision, and recall (Apple: 80.32% accuracy, 79.4% precision, 79.1% recall; Peach: 81.06% accuracy, 83.23% precision, 80.3% recall), outperforming AE baselines in precision and recall while maintaining comparable accuracy. This study concludes that the Memory Ganomaly model offers a robust solution for detecting anomalies in agricultural datasets, where data imbalance is prevalent, and suggests its potential for broader applications in agricultural monitoring and beyond. While both Ganomaly and MemAE have shown promise in anomaly detection, they suffer from limitations—Ganomaly often lacks long-term pattern recall, and MemAE may miss contextual cues. Our proposed Memory Ganomaly integrates the strengths of both, leveraging contextual reconstruction with pattern recall to enhance detection of subtle weather-related anomalies under class imbalance. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

19 pages, 3081 KB

Open AccessArticle

Temporal and Statistical Insights into Multivariate Time Series Forecasting of Corn Outlet Moisture in Industrial Continuous-Flow Drying Systems

by Marko Simonič and Simon Klančnik

Appl. Sci. 2025, 15(16), 9187; https://doi.org/10.3390/app15169187 - 21 Aug 2025

Cited by 5 | Viewed by 1416

Abstract

Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning [...] Read more.

Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a dataset of 3826 observations from an operational dryer. The effects of inlet moisture, target air temperature, and material discharge interval on thermal behavior of the system were evaluated through linear regression and t-test, which provided interpretable insights into process dependencies. Three neural network architectures (LSTM, GRU, and TCN) were benchmarked for multivariate time-series forecasting of outlet corn moisture, with hyperparameters optimized using grid search to ensure fair performance comparison. Results demonstrated GRU’s superior performance in the context of absolute deviations, achieving the lowest mean absolute error (MAE = 0.304%) and competitive mean squared error (MSE = 0.304%), compared to LSTM (MAE = 0.368%, MSE = 0.291%) and TCN (MAE = 0.397%, MSE = 0.315%). While GRU excelled in average prediction accuracy, LSTM’s lower MSE highlighted its robustness against extreme deviations. The hybrid methodology bridges statistical insights for interpretability with deep learning’s dynamic predictive capabilities, offering a scalable framework for real-time process optimization. By combining traditional analytical methods (e.g., regression and t-test) with deep learning-driven forecasting, this work advances intelligent monitoring and control of industrial drying systems, enhancing process stability, ensuring compliance with moisture standards, and indirectly supporting energy efficiency by reducing over drying and enabling more consistent operation. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

27 pages, 3922 KB

Open AccessArticle

Discrete Element Simulation Parameter Calibration of Wheat Straw Feed Using Response Surface Methodology and Particle Swarm Optimization–Backpropagation Hybrid Algorithm

by Zhigao Hu, Hao Li, Xuming Shi, Lingzhuo Kong, Xiang Tian, Shiguan An, Bin Feng and Juan Ma

Appl. Sci. 2025, 15(14), 7668; https://doi.org/10.3390/app15147668 - 8 Jul 2025

Cited by 4 | Viewed by 1535

Abstract

To establish a fundamental property database for discrete elements targeting long-fiber materials and address the issue of response surface methodology (RSM) being prone to local optima in high-dimensional nonlinear optimization, this study conducted parameter calibration experiments and validated the calibrated parameters through a [...] Read more.

To establish a fundamental property database for discrete elements targeting long-fiber materials and address the issue of response surface methodology (RSM) being prone to local optima in high-dimensional nonlinear optimization, this study conducted parameter calibration experiments and validated the calibrated parameters through a combined approach of simulation and physical testing. The Plackett–Burman design and steepest ascent test were employed to screen significant factors. Using the angle of repose (42.3°) obtained from physical experiments as the response value, response surface methodology (RSM) and a particle swarm optimization–back propagation (PSO-BP) neural network model were independently applied to optimize and compare the critical parameters. The results demonstrated that the dynamic friction coefficient between wheat straw particles, the static friction coefficient between wheat straw and steel plate, and the JKR surface energy were the most influential factors on the simulated angle of repose. The PSO-BP model exhibited superior optimization performance compared to RSM, yielding an optimal parameter combination of 0.17, 0.46, and 0.03. The simulated repose angle under these conditions was 41.67°, exhibiting a relative error of only 1.5% compared to the physical experiment. These findings provide a robust theoretical foundation for discrete element simulations of wheat straw feedstock. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

20 pages, 1935 KB

Open AccessArticle

Residual Attention Network with Atrous Spatial Pyramid Pooling for Soil Element Estimation in LUCAS Hyperspectral Data

by Yun Deng, Yuchen Cao, Shouxue Chen and Xiaohui Cheng

Appl. Sci. 2025, 15(13), 7457; https://doi.org/10.3390/app15137457 - 3 Jul 2025

Cited by 5 | Viewed by 1737

Abstract

Visible and near-infrared (Vis–NIR) spectroscopy enables the rapid prediction of soil properties but faces three limitations with conventional machine learning: information loss and overfitting from high-dimensional spectral features; inadequate modeling of nonlinear soil–spectra relationships; and failure to integrate multi-scale spatial features. To address [...] Read more.

Visible and near-infrared (Vis–NIR) spectroscopy enables the rapid prediction of soil properties but faces three limitations with conventional machine learning: information loss and overfitting from high-dimensional spectral features; inadequate modeling of nonlinear soil–spectra relationships; and failure to integrate multi-scale spatial features. To address these challenges, we propose ReSE-AP Net, a multi-scale attention residual network with spatial pyramid pooling. Built on convolutional residual blocks, the model incorporates a squeeze-and-excitation channel attention mechanism to recalibrate feature weights and an atrous spatial pyramid pooling (ASPP) module to extract multi-resolution spectral features. This architecture synergistically represents weak absorption peaks (400–1000 nm) and broad spectral bands (1000–2500 nm), overcoming single-scale modeling limitations. Validation on the LUCAS2009 dataset demonstrated that ReSE-AP Net outperformed conventional machine learning by improving the R² by 2.8–36.5% and reducing the RMSE by 14.2–69.2%. Compared with existing deep learning methods, it increased the R² by 0.4–25.5% for clay, silt, sand, organic carbon, calcium carbonate, and phosphorus predictions, and decreased the RMSE by 0.7–39.0%. Our contributions include statistical analysis of LUCAS2009 spectra, identification of conventional method limitations, development of the ReSE-AP Net model, ablation studies, and comprehensive comparisons with alternative approaches. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

14 pages, 2017 KB

Open AccessArticle

Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology

by Mingxiong Ou, Yong Zhang, Zhiyong Yu, Jiayao Zhang, Weidong Jia and Xiang Dong

Appl. Sci. 2025, 15(13), 7411; https://doi.org/10.3390/app15137411 - 1 Jul 2025

Cited by 3 | Viewed by 1428

Abstract

Precise detection of canopy parameters is vital as it offers essential information for pest management in orchards. Among these parameters, leaf area density stands out as a key indicator of orchard canopies. A detection algorithm for leaf area density was proposed, and a [...] Read more.

Precise detection of canopy parameters is vital as it offers essential information for pest management in orchards. Among these parameters, leaf area density stands out as a key indicator of orchard canopies. A detection algorithm for leaf area density was proposed, and a leaf area density detection system for orchard canopies was designed based on the algorithm. By processing the point cloud data acquired by using LiDAR together with the algorithm, the total leaf area of the fitted leaves was calculated. Through an orthogonal regression experiment conducted on a laboratory-simulated canopy, this research established a mathematical calculation model (

R^{2}

= 0.96) for determining the leaf area density of an orchard canopy. The leaf area density of an orchard canopy can be calculated using the total leaf area of the fitted leaves and an established mathematical model. To assess the accuracy of the detection system, both laboratory-simulated canopy experiments and real orchard canopy experiments were conducted. The results revealed that the absolute value of the mean relative error in the laboratory-simulated canopy experiments was 11.58%, and the absolute value of the mean relative error in the orchard canopy experiments was 16.75%. The research results have confirmed the feasibility of the LiDAR point cloud data processing algorithm. Furthermore, this algorithm can provide theoretical support for the subsequent development of intelligent plant protection equipment in orchards. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

25 pages, 4959 KB

Open AccessArticle

Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network

by Yilong Pan, Yaxin Yu, Junwei Zhou, Wenbing Qin, Qiang Wang and Yinghao Wang

Appl. Sci. 2025, 15(7), 3682; https://doi.org/10.3390/app15073682 - 27 Mar 2025

Cited by 1 | Viewed by 945

Abstract

This innovative method improves the inefficient optimization of the parameters of a pneumatic drum seed metering device. The method applies a backpropagation neural network (BPNN) to establish a predictive model and multi-objective particle swarm optimization (MOPSO) to search for the optimal solution. Six [...] Read more.

This innovative method improves the inefficient optimization of the parameters of a pneumatic drum seed metering device. The method applies a backpropagation neural network (BPNN) to establish a predictive model and multi-objective particle swarm optimization (MOPSO) to search for the optimal solution. Six types of small vegetable seeds were selected to conduct orthogonal experiments of seeding performance. The results were used to build a dataset for building a BPNN predictive model according to the inputs of the physical properties of the seed (thousand-grain weight, kernel density, sphericity, and geometric mean diameter) and the parameters of the device (vacuum pressure, drum rotational speed, and suction hole diameter). From this, the model output the seeding performance indices (the missing and reseeding indexes). The MOPSO algorithm uses the BPNN predictive model as a fitness function to search for the optimal solution for three types of seeds, and the optimized results were verified through bench experiments. The results show that the predicted qualified indices for tomato, pepper, and bok choi seeds are 85.50%, 85.52%, and 84.87%, respectively. All the absolute errors between the predicted and experimental results are less than 3%, indicating that the results are reliable and meet the requirements for efficient parameter optimization of a seed metering device. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

17 pages, 8502 KB

Open AccessArticle

A Lightweight Deep Learning Model for Forecasting the Fishing Ground of Purpleback Flying Squid (Sthenoteuthis oualaniensis) in the Northwest Indian Ocean

by Shengmao Zhang, Junlin Chen, Haibin Han, Fenghua Tang, Xuesen Cui and Yongchuang Shi

Appl. Sci. 2025, 15(3), 1219; https://doi.org/10.3390/app15031219 - 24 Jan 2025

Cited by 1 | Viewed by 1828

Abstract

The purpleback flying squid (Sthenoteuthis oualaniensis) is an economically significant cephalopod species in the Northwest Indian Ocean. Predicting its fishing grounds can provide a crucial foundation for fishery management and production. In this research, we collected data from China’s light-purse seine [...] Read more.

The purpleback flying squid (Sthenoteuthis oualaniensis) is an economically significant cephalopod species in the Northwest Indian Ocean. Predicting its fishing grounds can provide a crucial foundation for fishery management and production. In this research, we collected data from China’s light-purse seine fishery in the Northwest Indian Ocean from 2016 to 2020 to train and validate the AlexNet and VGG11 models. We designed a data partitioning method (DPM) to divide the training set into three scenarios, namely DPM-S1, DPM-S2, and DPM-S3. Firstly, DPM-S1 was employed to select the base model (BM). Subsequently, the optimal BM was lightweighted to obtain the optimal model (OM). The OM, known as the AlexNetMini model, has a model size that is one-third of that of the BM-AlexNet model. Our results also showed the following: (1) the F1-scores for AlexNet and AlexNetMini across the datasets DPM-S1, -S2, and -S3 were 0.6957, 0.7505, and 0.7430 for AlexNet and 0.6992, 0.7495, and 0.7486 for AlexNetMini, suggesting that both models exhibited comparable predictive performance; (2) the optimal dropout values for the AlexNetMini model were 0 and 0.2, and the optimal training set proportion was 0.8; (3) AlexNetMini utilized both DPM-S2 and DPM-S3, yielding comparable outcomes. However, given that the training duration for DPM-S3 was relatively shorter, DPM-S3 was selected as the preferred method for data partitioning. The findings of our study indicated that the lightweight model for the purpleback flying squid fishing ground prediction, specifically AlexNetMini, demonstrated superior performance compared to the original AlexNet model, particularly in terms of efficiency. Our study on the lightweight method for deep learning models provided a reference for enhancing the usability of deep learning in fisheries. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

16 pages, 3168 KB

Open AccessArticle

Impact of Subsurface Drainage System Design on Nitrate Loss and Crop Production

by Soonho Hwang, Shailendra Singh, Rabin Bhattarai, Hanseok Jeong and Richard A. Cooke

Appl. Sci. 2024, 14(22), 10180; https://doi.org/10.3390/app142210180 - 6 Nov 2024

Cited by 2 | Viewed by 2570

Abstract

Subsurface (or tile) drainage offers a valuable solution for enhancing crop productivity in poorly drained soils. However, this practice is also associated with significant nutrient leaching, which can contribute to water quality problems at the regional scale. This research presents the findings from [...] Read more.

Subsurface (or tile) drainage offers a valuable solution for enhancing crop productivity in poorly drained soils. However, this practice is also associated with significant nutrient leaching, which can contribute to water quality problems at the regional scale. This research presents the findings from a 4-year tile depth and spacing study in central Illinois that included three drain spacings (12.2, 18.3, and 24.4 m) and two drain depths (0.8 and 1.1 m) implemented in six plots under the corn and soybean rotation system (plots CS-1 and CS-3: 12.2 m spacing and 1.1 m depth, plots CS-2 and CS-4: 24.4 m spacing and 1.1 m depth, and plots CS-5 and CS-6 18.3 m spacing and 0.8 m depth). Our observations indicate that drain flow and NO₃-N losses were higher in plots with narrower drain spacings, while plots with wider drain spacing showed reduced drain flow and NO₃-N losses. Specifically, plots set up with drain spacings of 18.3 m and 24.4 m showed significant reductions in drain flow compared to plots featuring a 12.2 m drain spacing. Likewise, plots characterized by 18.3 m and 24.4 m drain spacings (except CS-4) showed better NO₃-N retention and lower leaching losses than those with 12.2 m spacing (CS-1 and CS-3). Crop yield results over a 3-year period indicated that CS-2 (wider spacing plot) showed the highest productivity, with up to 13.6% higher yield compared to other plots. Furthermore, when comparing plots with the same drainage designs, CS-2 and CS-4 showed 5.1% to 2.6% higher corn yield (3-year average) compared to CS-1 and CS-3, and CS-5 and CS-6, respectively. Overall, a wider drainage system showed the capacity to export lower nutrient levels while concurrently enhancing productivity. These findings represent that optimizing tile drainage systems can effectively reduce nitrate losses while increasing crop productivity. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

Review

Jump to: Research, Other

34 pages, 1410 KB

Open AccessReview

Digital Transformation Drivers, Technologies, and Pathways in Agricultural Product Supply Chains: A Comprehensive Literature Review

by Wenhui Wang, Zhen Li and Qingfeng Meng

Appl. Sci. 2025, 15(19), 10487; https://doi.org/10.3390/app151910487 - 28 Sep 2025

Cited by 15 | Viewed by 7611

Abstract

The digital transformation of agricultural product supply chains has emerged as a strategic direction that cannot be overlooked in the global modernization of agriculture. This paper adopts a narrative review framework based on the “Technology–Collaboration–Sustainability” perspective in the digital transformation of agricultural product [...] Read more.

The digital transformation of agricultural product supply chains has emerged as a strategic direction that cannot be overlooked in the global modernization of agriculture. This paper adopts a narrative review framework based on the “Technology–Collaboration–Sustainability” perspective in the digital transformation of agricultural product supply chains, summarizing the drivers of digital transformation, the application of digital technologies, multi-stakeholder collaborative mechanisms, and pathways for sustainable development within these supply chains. The study finds that the core drivers promoting the digital transformation of agricultural product supply chains include external environmental factors (such as population growth, dietary shifts, and food waste) and internal demand drivers (such as industrial upgrading and increased corporate competition). The application of digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) has significantly improved the efficiency, transparency, and resilience of the supply chains. Furthermore, various models of multi-stakeholder collaborative mechanisms have optimized resource allocation and enhanced supply chain stability. Finally, the paper proposes a pathway for the sustainable development of agricultural product supply chains based on digital transformation, providing directions for future research and practice. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

Other

Jump to: Research, Review

24 pages, 2221 KB

Open AccessPerspective

Digital Twins in Poultry Farming: Deconstructing the Evidence Gap Between Promise and Performance

by Suresh Raja Neethirajan

Appl. Sci. 2026, 16(3), 1317; https://doi.org/10.3390/app16031317 - 28 Jan 2026

Cited by 2 | Viewed by 1078

Abstract

Digital twins, understood as computational replicas of poultry production systems updated in real time by sensor data, are increasingly invoked as transformative tools for precision livestock farming and sustainable agriculture. They are credited with enhancing feed efficiency, reducing greenhouse gas emissions, enabling disease [...] Read more.

Digital twins, understood as computational replicas of poultry production systems updated in real time by sensor data, are increasingly invoked as transformative tools for precision livestock farming and sustainable agriculture. They are credited with enhancing feed efficiency, reducing greenhouse gas emissions, enabling disease detection earlier and improving animal welfare. Yet close examination of the published evidence reveals that these promises rest on a surprisingly narrow empirical foundation. Across the available literature, no peer reviewed study has quantified the full lifecycle carbon footprint of digital twin infrastructure in poultry production. Only one field validated investigation reports a measurable improvement in feed conversion ratio attributable to digital optimization, and that study’s design constrains its general applicability. A standardized performance assessment framework specific to poultry has not been established. Quantitative evaluations of reliability are scarce, limited to a small number of studies reporting data loss, sensor degradation and cloud system downtime, and no work has documented abandonment timelines or reasons for discontinuation. The result is a pronounced gap between technological aspiration and verified performance. Progress in this domain will depend on small-scale, deeply instrumented deployments capable of generating the longitudinal, multidimensional evidence required to substantiate the environmental and operational benefits attributed to digital twins. Full article

(This article belongs to the Special Issue Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (19 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI