Search Results (492)

Biofuels represent a sustainable alternative that supports global energy development without compromising environmental balance. This work introduces a novel hardware–software platform for the experimental characterization of biomass solid yield during the slow pyrolysis process, integrating physical experimentation with advanced computational modeling. The hardware consists of a custom-designed pyrolizer equipped with temperature and weight sensors, a dedicated control unit, and a user-friendly interface. On the software side, a two-step kinetic model was implemented and coupled with three optimization algorithms, i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Nelder–Mead (N-M), to estimate the Arrhenius kinetic parameters governing biomass degradation. Slow pyrolysis experiments were performed on wheat straw (WS), pruning waste (PW), and biosolids (BS) at a heating rate of 20 °C/min within 250–500 °C, with a 120 min residence time favoring biochar production. The comparative analysis shows that the N-M method achieved the highest accuracy (100% fit in estimating solid yield), with a convergence time of 4.282 min, while GA converged faster (1.675 min), with a fit of 99.972%, and PSO had the slowest convergence time at 6.409 min and a fit of 99.943%. These results highlight both the versatility of the system and the potential of optimization techniques to provide accurate predictive models of biomass decomposition as a function of time and temperature. Overall, the main contributions of this work are the development of a low-cost, custom MATLAB-based experimental platform and the tailored implementation of optimization algorithms for kinetic parameter estimation across different biomasses, together providing a robust framework for biomass pyrolysis characterization. Full article

Selection decisions in plant breeding programs are complex, and breeders aim to integrate phenotypic impressions, genotypic data, and agronomic performance across multiple selection stages to develop successful varieties. This study investigates whether such decisions can be predicted in a commercial winter wheat (Triticum aestivum L.) breeding program using elastic net models trained on genome-wide distributed markers and genomic estimated breeding values. For this purpose, a dataset of several thousand lines tested between 2015 and 2019 in preliminary, advanced, and elite multi-environment yield trials was analyzed across three decision-making scenarios. The predictive models achieved a higher precision than random selection in all scenarios, with an increased performance when genomic estimated breeding values were included as predictors. Comparisons of breeder selections and model recommendations in terms of selection differentials for key agronomic traits showed a substantial overlap in breeding objectives, while both the breeder’s decisions and the model’s suggestions maintained similar levels of genetic diversity. Although the precision of the elastic net model was of moderate magnitude, divergent model recommendations often identified promising alternative lines, highlighting the potential of artificial intelligence to support decision-making in plant breeding. Full article

Accurate prediction of cultivar performance across diverse environments is crucial for breeding and recommendation systems, helping to reduce the yield gap, the difference between potential and actual yields, which is often widened by poor cultivar selection. This study assessed the adaptability of winter wheat (Triticum aestivum L.) cultivars using a linear mixed-model framework combined with environmental mean regression. The model was trained on yield data from 19 locations over nine years (2015–2023) and validated independently using 2024 data. To ensure robustness, outliers were removed and cultivars with fewer than 30 observations excluded. The model accounted for genotype-by-environment (G×E) interactions and produced adjusted means for each location–year–management combination. These were used in cultivar-specific regressions to estimate yield response across environments. The approach showed strong predictive performance, with a Pearson correlation of 0.958 between predicted and observed yields in the validation year. Results highlight the model’s potential to inform cultivar recommendations, including for less-tested cultivars. This framework offers a practical tool for data-driven decision-making in plant breeding and agronomy, especially under variable growing conditions. Full article

Background: Facing global wheat production pressures such as environmental degradation and reduced cultivated land, breeding innovation is urgent to boost yields. Genomic selection (GS) is a useful wheat breeding technology to make the breeding process more efficient, increasing the genetic gain per unit time and cost. Precise genomic estimated breeding value (GEBV) via genome-wide markers is usually hampered by high-dimensional genomic data. Methods: To address this, we propose GRE, a framework combining genome-wide association study (GWAS)’s biological significance and random forest (RF)’s prediction efficiency for an explainable machine learning GS model. First, GRE identifies significant SNPs affecting wheat yield traits by comparison of the constructed 24 SNP subsets (intersection/union) selected by leveraging GWAS and RF, to analyze the marker scale’s impact. Furthermore, GRE compares six GS algorithms (GBLUP and five machine learning models), evaluating performance via prediction accuracy (Pearson correlation coefficient, PCC) and error. Additionally, GRE leverages Shapley additive explanations (SHAP) explainable techniques to overcome traditional GS models’ “black box” limitation, enabling cross-scale quantitative analysis and revealing how significant SNPs affect yield traits. Results: Results show that XGBoost and ElasticNet perform best in the union (383 SNPs) of GWAS and RF’s TOP 200 SNPs, with high accuracy (PCC > 0.864) and stability (standard deviation, SD < 0.005), and the significant SNPs identified by XGBoost are precisely explained by their main and interaction effects on wheat yield by SHAP. Conclusions: This study provides tool support for intelligent breeding chip design, important trait gene mining, and GS technology field transformation, aiding global agricultural sustainable productivity. Full article

Monitoring wheat traits across diverse environments requires reliable sensing tools that balance accuracy, cost, and scalability. This study compares the performance of proximal and UAV-derived NDVI sensing for predicting the key agronomic traits in winter wheat. The research was conducted at a long-term NPK field experiment on Haplic Chernozem soils in Rimski Šančevi, Serbia, using UAV multispectral imagery and a handheld proximal sensor to collect NDVI data across 400 micro-plots and six phenological stages. The UAV-derived NDVI achieved a higher mean value (0.71 vs. 0.60), lower coefficient of variation (29.2% vs. 33.0%), and stronger correlation with the POM readings (R² = 0.92). For trait prediction, the UAV-based NDVI reached R² values up to 0.95 for grain yield and 0.84 for plant height, outperforming the POM (maximum R² = 0.94 and 0.83, respectively), and it showed superior temporal consistency (average R² = 0.74 vs. 0.64). Although the POM performed comparably during mid-season under controlled conditions, its sensitivity to operator handling and limited spatial resolution reduced robustness in more variable field scenarios. A cost–benefit analysis revealed that the POM offers advantages in affordability, ease of use, and deployment in small-scale settings, while UAV systems are better suited for large-scale monitoring due to their higher spatial resolution and data richness. The findings highlight the importance of selecting sensing technologies based on biological context, operational goals, and resource constraints, and suggest that combining methods through stratified sampling may improve the efficiency and accuracy of crop monitoring in precision agriculture. Full article

Sustainable nitrogen (N) management in arable crops requires the real-time assessment of crop growth and N uptake, particularly in water-limited environments. In the present study, we conducted two large-scale field experiments with rainfed and irrigated wheat in South-East Turkey to evaluate the effectiveness of drone- and satellite-based spectral indices, in combination with neural network models, for estimating biomass and nitrogen uptake. Four N fertilizer rates in the irrigated fields (N₀: 0, N₆: 60, N₁₂: 120, and N₁₆: 160 kg N ha⁻¹) and five N rates in the rainfed fields (N₀: 0, N₂: 20, N₄: 40, N₅: 50, and N₆: 60 kg N ha⁻¹) were tested. Highest fresh biomass was 57.7 ± 1.1 and 15.9 ± 1.0 t/ha⁻¹ for irrigated and rainfed treatments, respectively, with 2.5-fold higher grain yield in irrigated (8.2 ± 1.2 t/ha⁻¹) compared to rainfed (2.9 ± 0.9 t/ha⁻¹) wheat. Drone-based spectral indices, especially those based on the red-edge region (CL_{Red_edge}), correlated strongly with biomass (R² > 0.9 in irrigated wheat) but failed to explain crop N concentration throughout the vegetation period. This limitation was attributed to the nitrogen dilution effect, where increasing biomass during crop growth leads to a decline in the concentration of nitrogen, complicating its accurate estimation via remote sensing. To address this, we employed a two-layer feed-forward neural network model and used SPAD and plant height values as supplementary input parameters to enhance estimations based on vegetation indices. This approach substantially enhanced the predictions of N uptake (R² up to 0.95), while even simplified model version using only NDVI and plant height parameters achieved significant performance (R² = 0.84). Overall, our results showed that spectral indices are reliable predictors of biomass but insufficient for estimating nitrogen concentration or uptake. Integrating indices with complementary crop traits in nonlinear models provides acceptable estimates of N uptake, supporting more precise fertilizer management and sustainable wheat production under water-limited conditions. Full article

Traditional NDVI-based biomass estimation methods often suffer from saturation at high vegetation density and limited sensitivity during early crop growth, which reduces their effectiveness for precise monitoring. This study addresses these limitations by introducing the use of the second derivative of NDVI with respect to near-infrared (NIR) reflectance as a novel indicator of inflection points and dynamic changes in crop development. The proposed method is mathematically derived, and a corresponding index is calculated. Field trials were conducted on five winter wheat varieties over two growing seasons (2021–2023). The results demonstrated a strong correlation between the derived index and actual biomass measurements. To validate the findings, linear regression analysis between the second derivative of NDVI and biomass scores yielded R and R² values equal to 1. These findings confirm the high predictive power and reliability of the method for non-destructive UAV-based biomass monitoring in precision agriculture. Full article

This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat (Triticum aestivum L.) during 2022–2023 using a DJI M300 RTK equipped with multispectral sensors. The dataset encompasses diverse field scenarios under five fertilization treatments (organic-only, organic–inorganic 7:3 and 3:7 ratios, inorganic-only, and no fertilizer) and two irrigation regimes (full and deficit irrigation), ensuring representativeness and generalizability. For model development, we replaced conventional VGG16 with ResNet-50 as the backbone network, incorporating residual connections and channel attention mechanisms to achieve 92.1% mean average precision (mAP) while reducing parameters from 135 M to 77 M (43% decrease). The GFLOPS of the improved model has been reduced from 1.9 to 1.7, an decrease of 10.53%, and the computational efficiency of the model has been improved. Performance tests demonstrated a 15% reduction in missed detection rate compared to YOLOv8 in dense canopies, with spike count regression analysis yielding R² = 0.88 (p < 0.05) against manual measurements and yield prediction errors below 10% for optimal treatments. To validate robustness, we established a dedicated 500-image test set (25% of total data) spanning density gradients (30–80 spikes/m²) and varying illumination conditions, maintaining >85% accuracy even under cloudy weather. Furthermore, by integrating spike recognition with agronomic parameters (e.g., grain weight), we developed a comprehensive yield estimation model achieving 93.5% accuracy under optimal water–fertilizer management (70% ETc irrigation with 3:7 organic–inorganic ratio). This work systematically addresses key technical challenges in automated spike detection through standardized data acquisition, lightweight model design, and field validation, offering significant practical value for smart agriculture development. Full article

Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms. Full article

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the ${\mathrm{AP}}_{50}$ of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article

Wheat powdery mildew is a major fungal disease threatening global wheat production. To develop an effective and environmentally friendly control strategy, this study systematically evaluated the disease-suppressive efficacy of strong oxidative free radicals across a series of treatment parameters, including radical concentrations (3.0–8.0 mg/L), spraying durations (20–60 s), solution pH levels (5–8), spraying heights (0–20 cm), and treatment timings corresponding to different infection stages (0–120 h post-inoculation). Response surface methodology (RSM) was used to optimize these variables with the objective of maximizing disease control efficacy. The results showed that control efficacy increased with radical concentration up to 5.0 mg/L, beyond which a saturation effect was observed. The most effective conditions included a spraying duration of 50 s and a height of 6.5 cm. Maximum suppression was achieved when the treatment was applied within 0–12 h post-infection. Moreover, adjusting the solution pH to a range of 5–7 further enhanced the efficacy. The RSM-based predictive model demonstrated high accuracy (R² = 0.9942), and the optimized parameters—6.65 mg/L radical concentration, 50.84 s spraying duration, and treatment at 15.67 h post-infection—yielded a predicted control efficacy of 97.64%, with a validation error below 0.5%. This study provides a quantitative basis for the precise and sustainable deployment of free radical-based treatments in wheat disease management. Full article

Accurate estimation aboveground biomass (AGB) in winter wheat is crucial for yield assessment but remains challenging to achieve non-destructively. Unmanned aerial vehicle (UAV)-based remote sensing offers a promising solution at the plot level. Traditional field sampling methods, such as random plant selection or full-quadrat harvesting, are labor intensive and may introduce substantial errors compared to the canopy-level estimates obtained from UAV imagery. This study proposes a novel method using Fractional Vegetation Coverage (FVC) to adjust field-sampled AGB to per-plant biomass, enhancing the accuracy of AGB estimation using UAV imagery. Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. The aim was to evaluate the performance of multimodal data in estimating winter wheat leaves, spikes, stems, and total AGB. Results demonstrated that (1) FVC-adjusted per-plant biomass significantly improved correlations with most indicators, particularly during the filling stage, when the correlation between leaf biomass and NDVI increased by 56.1%; (2) RF and NN models outperformed SVM, with the optimal accuracies being R² = 0.709, RMSE = 0.114 g for RF, R² = 0.66, RMSE = 0.08 g for NN, and R² = 0.557, RMSE = 0.117 g for SVM. Notably, the RF model achieved the highest prediction accuracy for leaf biomass during the flowering stage (R² = 0.709, RMSE = 0.114); (3) among different water treatments, the R² values of water and drought treatments were higher 0.723 and 0.742, respectively, indicating strong adaptability. This study provides an economically effective method for monitoring winter wheat growth in the field, contributing to improved agricultural productivity and fertilization management. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

To solve the issue that existing yield prediction methods do not fully capture the interaction between multiple factors, we propose a winter wheat yield prediction framework with triple cross-attention for multi-source data fusion. This framework consists of three modules: a multi-source data processing module, a multi-source feature fusion module, and a yield prediction module. The multi-source data processing module collects satellite, climate, and soil data based on the winter wheat planting range, and constructs a multi-source feature sequence set by combining statistical data. The multi-source feature fusion module first extracts deeper-level feature information based on the characteristics of different data, and then performs multi-source feature fusion through a triple cross-attention fusion mechanism. The encoder part in the production prediction module adds a graph attention mechanism, forming a dual branch with the original multi-head self-attention mechanism to ensure the capture of global dependencies while enhancing the preservation of local feature information. The decoder section generates the final predicted output. The results show that: (1) Using 2021 and 2022 as test sets, the mean absolute error of our method is 385.99 kg/hm², and the root mean squared error is 501.94 kg/hm², which is lower than other methods. (2) It can be concluded that the jointing-heading stage (March to April) is the most crucial period affecting winter wheat production. (3) It is evident that our model has the ability to predict the final winter wheat yield nearly a month in advance. Full article

Accurate and timely wheat yield prediction is valuable globally for enhancing agricultural planning, optimizing resource use, and supporting trade strategies. Study addresses the need for precision in yield estimation by applying machine-learning (ML) regression models to high-resolution Unmanned Aerial Vehicle (UAV) multispectral (MS) and Red-Green-Blue (RGB) imagery. Research analyzes five European wheat cultivars across 400 experimental plots created by combining 20 nitrogen, phosphorus, and potassium (NPK) fertilizer treatments. Yield variations from 1.41 to 6.42 t/ha strengthen model robustness with diverse data. The ML approach is automated using PyCaret, which optimized and evaluated 25 regression models based on 65 vegetation indices and yield data, resulting in 66 feature variables across 400 observations. The dataset, split into training (70%) and testing sets (30%), was used to predict yields at three growth stages: 9 May, 20 May, and 6 June 2022. Key models achieved high accuracy, with the Support Vector Regression (SVR) model reaching R² = 0.95 on 9 May and R² = 0.91 on 6 June, and the Multi-Layer Perceptron (MLP) Regressor attaining R² = 0.94 on 20 May. The findings underscore the effectiveness of precisely measured MS indices and a rigorous experimental approach in achieving high-accuracy yield predictions. This study demonstrates how a precise experimental setup, large-scale field data, and AutoML can harness UAV and machine learning’s potential to enhance wheat yield predictions. The main limitations of this study lie in its focus on experimental fields under specific conditions; future research could explore adaptability to diverse environments and wheat varieties for broader applicability. Full article