Search Results (143)

Seed priming, traditionally viewed as a method for enhancing crop resilience to abiotic stress, has evolved into a multifaceted agronomic strategy. This review synthesizes the current findings demonstrating that priming influences plant development, metabolic regulation, and yield enhancement even under optimal conditions. By covering a wide range of crops, including cereals (e.g., wheat, maize, rice, and barley) as well as vegetables and horticultural species (e.g., tomato, carrot, spinach, and lettuce), we highlight the broad applicability of priming across agricultural systems. The underlying mechanisms include hormonal modulation, altered source–sink dynamics, accelerated phenology, and epigenetic memory. Various priming techniques are discussed, including hydropriming, osmopriming, biopriming, chemopriming, and nanopriming, with attention to their physiological and molecular effects. Special focus is given to the role of seed priming in advancing climate-smart and precision agriculture. By shifting the narrative from stress mitigation to holistic crop performance optimization, seed priming emerges as a key tool for sustainable agriculture in the face of global challenges. Full article

Unlike traditional yield mapping, which is conducted using costly yield sensors mounted on combine harvesters to collect post-harvest data, yield potential prediction using remote sensing data is considered a low-cost alternative. In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. Combinations of seven vegetation indices from Sentinel-2 imagery and seven phenology metrics were evaluated for the prediction of maize and soybean yield potential. Ground truth yield data were provided by the Quantile Loss Domain Adversarial Neural Network (QDANN) database, with 1000 samples randomly selected per year from 2019 to 2022 for Iowa and Illinois. Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). Across all evaluations, RF was found to outperform the other models in both cross-validation and final model accuracy metrics. Vegetation index values at peak of season (POS) and phenological timing, expressed as the day of year (DOY) of phenological events, were identified as the most influential covariates for predicting yield potential in particular years for both maize and soybean. Full article

This study presents the MSPAT (Multispectral Soil Plant Analysis Tool), a device designed for assessing leaf nitrogen concentrations in maize crops under field conditions. The MSPAT includes the AS7265x sensor, which has 18 bands and covers the spectrum from 410 to 940 nm. This device was designed to be portable, using the ESP32 microcontroller and incorporating such functionalities as data storage on a MicroSD card, communication with a smartphone via Wi-Fi, and geolocation of acquired data. The MSPAT was evaluated in an experiment conducted at the Federal University of Ceará (UFC), where maize was subjected to different doses of nitrogen fertiliser (0, 60, 90, 120, 150, and 180 kg·ha⁻¹ N). Spectral readings were taken at three phenological stages (V5, V10, and R2) using the MSPAT, an SPAD-502 chlorophyll meter, and a FieldSpec PRO FR3 spectroradiometer. After the optical measurements were taken, the nitrogen concentrations in the leaves were determined in a laboratory by using the Kjeldahl method. The data analysis included the calculation of normalised ratio indices (NRIs) using linear regression and the application of multivariate statistical methods (PLSR and PCR) for predicting leaf nitrogen concentrations (LNCs). The best performance for the MSPAT index (NRI) was obtained using the 900 nm and the 560 nm bands (R² = 0.64) at stage V10. In the validation analysis, the MSPAT presented an R² of 0.79, showing performance superior to that of SPAD-502, which achieved an R² of 0.70. This confirms the greater potential of the MSPAT compared to commercial equipment and makes it possible to obtain results similar to those obtained using the reference spectroradiometer. The PLSR model with data from the FieldSpec 3 provided important validation metrics when using reflectance data with first-derivative transformation (R² = 0.88, RMSE = 1.94 and MAE = 1.28). When using the MSPAT, PLSR (R² = 0.75, RMSE = 2.77 and MAE = 2.26) exhibited values of metrics similar to those for PCR (R² = 0.75, RMSE = 2.78 and MAE = 2.26). This study validates the use of MSPAT as an effective tool for monitoring the nutritional status of maize to optimize the use of nitrogen fertilisers. Full article

Multispectral images provide valuable indicators of crop nutritional status, playing a key role in strategies to reduce fertilizer use and enable supplementary applications in cases of nitrogen deficiency, thereby ensuring productivity and profitability for farmers. However, the diversity of remote sensing platforms (RSPs) makes the choice challenging, as there are few comparative studies. This study compares the remote sensing platforms Sentinel-2, CBERS-04A, and unmanned aerial vehicle (UAV), assessing their accuracy in detecting different nitrogen doses (NDs) throughout the maize crop cycle in Botucatu-SP, using 10 vegetation indices (VIs). Six NDs were tested (0, 36, 84, 132, 180, and 228 kg ha⁻¹ of nitrogen) in nine assessments during the crop cycle. The results showed that, at the V7 stage, the RSPs were effective in detecting the NDs in eight VIs. However, at the VT stage, only the Sentinel-2 and CBERS-04A satellites demonstrated effectiveness in six VIs. Despite the high correlation among the RSPs, the ability to distinguish the NDs varied depending on the vegetation index (VI) and phenological stage. These findings highlight the importance of selecting the appropriate VI and optimal timing, regardless of the chosen platform. Full article

Phenological parameters extracted from time series (TS) of spectral indices are essential to characterizing crops. However, the lack of data in the TS can affect their accuracy. The Copernicus Land Monitoring Service (CLMS) provides these parameters and their temporal quality. This paper evaluates the impact of missing vegetation index data on phenological parameters, namely, SOS, EOS, and MAX, for extensive arable crop between 2018 and 2023. The TSGenerator package was developed to download, process, and analyze the data. We used 252 images from the BIOPAR-VI module, 6 phenology parameters, and 2025 plots of barley and maize in Monegros and Zaidín, Spain. In barley, SOS and MAX showed 42.9% and 40.9% of missing data, while in maize, SOS and EOS showed 36.6% and 41.0%. The correlation between the Copernicus VPP quality parameter and the proposed one was r = 0.89 for barley and r = 0.74 for maize. This study advances the understanding of the effect of missing data on SOS, EOS, and MAX. Full article

In response to the limited research on silage maize classification in China and the lack of data support for refined agricultural and livestock management, this study proposes a Temporal Difference-based Silage Maize Identification Model (TempDiff-SMID) using the Google Earth Engine (GEE) platform. By analyzing the phenological phases of silage maize and grain maize, we identified their critical harvest periods and established decision rules for classifying silage maize, grain maize, and other land cover types. Preprocessed Sentinel-2 imagery was smoothed using the Whittaker filter to construct the TempDiff-SMID model. After iterative threshold optimization, the decision tree model achieved an overall accuracy of 0.9291 and a Kappa coefficient of 0.8923, indicating robust classification performance. The user’s accuracies for silage maize, grain maize, and other land cover types were 0.9216, 0.9219, and 0.9404, respectively, while the producer’s accuracies reached 0.94, 0.9008, and 0.9467, demonstrating minimal omission and commission errors across all categories. Furthermore, the F1 scores for silage maize, grain maize, and other land cover types were 0.9307, 0.9112, and 0.9435, respectively, confirming the effectiveness of the TempDiff-SMID framework in leveraging harvest time differences for accurate silage maize identification. To evaluate performance, we compared the TempDiff-SMID with the RF Model for Silage Maize Classification (SMRF). The TempDiff-SMID outperformed the SMRF in both overall accuracy (0.9043 vs. 0.9291) and Kappa coefficient (0.8511 vs. 0.8923), while also providing an intuitive representation of spectral and phenological differences between silage maize and grain maize. When applied to multi-year data, TempDiff-SMID demonstrated strong temporal transferability, achieving overall accuracies of 0.8621 (2022) and 0.8816 (2021), thereby confirming its robustness across growing seasons. The proposed model offers simplicity in methodology, clear interpretability, and efficient deployment, making it a practical tool for agricultural and livestock management systems. Its ability to rapidly adapt to new regions or years underscores its significance in supporting precision agriculture and sustainable farming practices. Full article

Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R² values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R² values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R² values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. Full article

To authors’ knowledge, no previous studies thoroughly focused on determining the single optimal combination of vegetation index and phenology metric for maize yield assessment based on ground truth yield map from combine harvester. Therefore, the main objective of this study was to evaluate correlation between all combinations of eight vegetation indices and seven phenology metrics with maize yield. A specific focus was put on evaluating saturation-resistant vegetation indices and utilizing Sentinel-2 images, including novel vegetation indices such as Inverted Difference Vegetation Index (IDVI), Three Red-Edge Vegetation Index (NDVI_3RE) and Plant Phenology Index (PPI). Twelve parcels located in Eastern Croatia were observed during 2022 and 2023, with a total area of ground truth data of 67.61 ha. The analysis of vegetation indices and phenology metrics indicated varying strengths of correlation with maize yield, with the combination of NDVI_3RE and Senescence producing the highest Pearson correlation coefficient (0.506). However, the relationship of optimal combination of vegetation index and phenology metric with maize yield based on combined dataset which included parcels 1–12 on individual parcels varied notably and is likely indicative of interannual weather variations. Overall, the reduced saturation effect in red-edge-based index suggests that it may be more suitable for maize yield prediction. Full article

In precision agriculture research, it is important to monitor maize growth conditions in real time for effective field diagnosis, management, and accurate yield prediction. This study aimed to develop an inverse model of vegetation index and maize yield using Unmanned Aerial Vehicle (UAV) multispectral imagery. The objective was to investigate how the vegetation index, maize growth stages, and growth parameters respond to plastic film mulching on the Loess Plateau. Annual field trials (2019–2020) employed a factorial design to evaluate mulch and nitrogen regimes. The results show that vegetation index long-time series curves, combined with maize phenological growth stages, can be used to monitor maize growth and yield estimation (R² > 0.9). The 13 vegetation indices (VIs) obtained by UAVs had a good regression relationship with the leaf area index, with the enhanced vegetation index 2 (EVI2) performing the best. The VIs obtained by UAVs at different stages of growth and development predicted yields, finding that EVI2 performed best with an R² of 0.92 and an RMSE of 0.52 t ha^-1 when maize entered the heading stage in 2019. The regression effect of VIs and yield based on maize without plastic film mulching management entering the heading stage was the best in 2020, with an R2 of 0.94 and an RMSE of 0.44 t ha⁻¹. When maize enters the heading stage, the best simulation results can be obtained by using the VIs to establish a yield prediction model. Spectral signatures during reproductive transition (VT-R1) proved most indicative of the final yield. Convergence of UAV-based spectral phenotyping with crop developmental physiology enables high-resolution growth diagnostics, providing empirical support for precision farming adaptations. Full article

Accurately extracting the phenology of maize, one of the three major staple crops, is crucial for assessing regional suitability under climate change, optimizing field management, predicting yield fluctuations, and ensuring food security. This study compares and validates the accuracy of various vegetation indices, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), solar-induced chlorophyll fluorescence (SIF), and kernel NDVI (kNDVI), in extracting the phenological phases of summer maize at the sixth leaf (V6), tasseling (VT), and maturity (R6). Additionally, explainable machine learning methods were employed to elucidate how climate and stress factors influence the phenological sequences of summer maize. The results show that compared to NDVI and EVI, SIF and kNDVI are more suitable for extracting the summer maize phenological phase. SIF achieved the highest phenological extraction precision at the V6 and R6 phases, with root mean square errors (RMSEs) of 7.86 and 8.22 days, respectively. kNDVI provided the highest extraction accuracy for the VT phase, with an RMSE of 5 days. SHapley Additive exPlanations (SHAP) analysis revealed that temperature and radiation are the primary meteorological factors influencing maize phenology in the study area. Regarding stress factors, drought and heat stress delayed phenology at the V6 and VT phases, while heat stress prior to maturity accelerated summer maize maturation. In conclusion, this study reveals the potential of emerging vegetation indices for extracting maize phenology, offering both data and theoretical support for regional crop adaptability assessments. Full article

Cropland suitability calculations quantify natural suitability according to abiotic conditions, thus making them crucial for sustainable land management. However, since ground-truth yield data are extremely scarce, there is a need to improve knowledge on the optimal proxy metric from satellite imagery, which represents cropland suitability and enables global applicability. This study evaluated four frequently used vegetation indices from Sentinel-2 image time-series (normalized difference vegetation index, enhanced vegetation index, enhanced vegetation index 2, and wide dynamic range vegetation index) with three phenology metrics for correlation analysis with maize and soybean yield. Four years (2019–2022) in two study areas (Iowa and Illinois) were utilized in this research, and 1000 ground-truth crop yield samples were created for each combination of study year and area. The combination of wide dynamic range vegetation index (WDRVI) and maximum vegetation index phenology metric (MAX) was an optimal proxy for maize yield prediction, while enhanced vegetation index 2 (EVI2) and MAX produced the highest correlation for soybean, producing Pearson’s correlation coefficient means of 0.506 and 0.519, respectively. This study improved our knowledge of the optimal proxy metric for cropland suitability by combining multiple large ground-truth crop yield datasets with 30 m spatial resolution satellite imagery, which can be further improved with the use of novel vegetation indices with improved resistance to a saturation effect. Full article

Plant moisture content (PMC) serves as a crucial indicator of crop water status, directly affecting agricultural productivity, product quality, and the effectiveness of precision irrigation. Conventional methods for PMC assessment predominantly rely on destructive sampling techniques, which are labor-intensive and impede real-time monitoring. This study investigates silage maize cultivated in the Hexi region of China, leveraging multispectral data acquired via an unmanned aerial vehicle (UAV) to estimate PMC across different phenological stages. A stacked ensemble learning framework was developed, integrating Back Propagation Neural Network (BPNN), Random Forest Regression (RFR), and Support Vector Regression (SVR), with Partial Least Squares Regression (PLSR) employed for feature fusion. The findings indicate that incorporating vegetation indices into spectral variables significantly improved prediction performance. The standalone models demonstrated coefficient of determination (R²) values ranging from 0.43 to 0.69, with root mean square error (RMSE) spanning 0.61% to 1.43%. In contrast, the ensemble model exhibited superior accuracy, achieving R² values between 0.61 and 0.87 and RMSE values from 0.54% to 1.38%. This methodology offers a scalable, non-invasive alternative for PMC estimation, facilitating data-driven irrigation optimization in regions facing water scarcity. Full article

Enhancing chlorophyll retention in maize leaves and prolonging the grain-filling duration constitute critical strategies for yield improvement in agricultural production systems. This study investigated the mechanistic relationship between yield enhancement pathways and the leaf senescence process induced by high-input straw pellets amendment. We analyzed the impact mechanisms of green leaf area dynamics and dry matter redistribution on yield during late reproductive stages, establishing theoretical foundations for yield optimization through intensive straw pellets incorporation. The study used the maize variety Jingnongke 728 as the experimental material. Based on previous research, four treatments were set up, including no straw returning (CK), chopped straw (15 t/ha) returning to the field (FS1), a large amount of chopped straw (75 t/ha) returning to the field (FS5), and a large amount of pelletized straw (75 t/ha) returning to the field (KL5), with four replicates. A two-year experimental design systematically assessed green leaf area index (GLAI), dry matter accumulation, distribution, translocation, yield components, and grain yield to explore the differences among various treatments under different straw returning amounts and returning forms. The study detected no significant differences between FS1 and CK. Although KL5 and FS5 delayed leaf senescence, FS5 significantly depressed green leaf area index (GLAI) at the R1 stage (silking), which results in it not having more effective photosynthetic area during late phenological phases. In dry matter dynamics, KL5 exhibited 5.52–25.71% greater pre-anthesis accumulation, 2.73–60.74% higher post-anthesis accumulation, and 9.48–25.76% elevated ear dry matter allocation relative to other treatments. KL5’s post-anthesis assimilates contributed 2.43–17.02% more to grain development, concurrently increasing ear-to-total biomass ratio. Yield analysis ranked KL5 as the superior treatment with 0.68–25.15% yield advantage, driven by significantly enhanced kernel number per ear and 100-kernel mass, whereas FS5 displayed the lowest kernel count among all treatments. Returning 75 t/ha of straw pellets to the black soil area in Northeast China can significantly delay the senescence of maize leaves and increase the accumulation of dry matter after anthesis by maintaining the effective photosynthetic area of leaves in the later stage of growth, thereby achieving the goal of increasing yield. The research can offer a practical and novel approach for straw return in the black soil region of Northeast China and provide a new technological pathway for enhancing crop productivity. Full article

The temperature-based crop water stress index (CWSI) is the most robust metric among precise techniques that assess the severity of crop water stress, particularly in susceptible crops like maize. This study used a unmanned aerial vehicle (UAV) to remotely collect data, to use in combination with the random forest regression algorithm to detect the maize CWSI in smallholder croplands. This study sought to predict a foliar temperature-derived maize CWSI as a proxy for crop water stress using UAV-acquired spectral variables together with random forest regression throughout the vegetative and reproductive growth stages. The CWSI was derived after computing the non-water-stress baseline (NWSB) and non-transpiration baseline (NTB) using the field-measured canopy temperature, air temperature, and humidity data during the vegetative growth stages (V5, V10, and V14) and the reproductive growth stage (R1 stage). The results showed that the CWSI (CWSI < 0.3) could be estimated to an R² of 0.86, RMSE of 0.12, and MAE of 0.10 for the 5th vegetative stage; an R² of 0.85, RMSE of 0.03, and MAE of 0.02 for the 10th vegetative stage; an R² of 0.85, RMSE of 0.05, and MAE of 0.04 for the 14th vegetative stage; and an R² of 0.82, RMSE of 0.09, and MAE of 0.08 for the 1st reproductive stage. The Red, RedEdge, NIR, and TIR UAV-bands and their associated indices (CCCI, MTCI, GNDVI, NDRE, Red, TIR) were the most influential variables across all the growth stages. The vegetative V10 stage exhibited the most optimal prediction accuracies (RMSE = 0.03, MAE = 0.02), with the Red band being the most influential predictor variable. Unmanned aerial vehicles are essential for collecting data on the small and fragmented croplands predominant in southern Africa. The procedure facilitates determining crop water stress at different phenological stages to develop timeous response interventions, acting as an early warning system for crops. Full article

Agricultural drought is a condition that threatens natural ecosystems, water security, and food security. The timely identification of an agricultural drought event is essential to mitigating its effects. However, achieving a reliable and accurate assessment is challenging due to the interannual variability of precipitation in a region. Therefore, the objective of this study was to identify the months with drought during the agricultural cycle of the maize crop (Zea mays L.) in the Atlacomulco Rural Development District (ARDD) as a study area using the SPI and SPEI indices and their impact on each phenological stage. The results show that when analyzing the historical period (1985–2017), the ARDD is a region prone to agricultural droughts with a duration of one month. The stages of grain filling and ripening were the most vulnerable, since SPI and SPEI-1 quantify that 25% and 31% of the total months with drought occur during those stages, respectively. Towards the 2041–2080 horizon, the MCG ACCESS-ESM1-5 with the SSP2-4.5 scenario identified an occurrence of dry periods with 17% and 20% by SPI and SPEI, respectively, while for SSP5-8.5, 17% and 22% of the total number of periods corresponded to dry months with SPI and SPEI, respectively. Greater recurrence will be observed in the future, specifically after the year 2061, meaning an increase in the frequency of agricultural drought events in the region, causing difficult and erratic productive conditions for each agricultural cycle and threatening sustainable development. Therefore, it is necessary to take action to mitigate the effects of climate change in this sector. Full article