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Keywords = soybean phenology

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19 pages, 7604 KiB  
Article
Phenology-Based Maize and Soybean Yield Potential Prediction Using Machine Learning and Sentinel-2 Imagery Time-Series
by Dorijan Radočaj, Ivan Plaščak and Mladen Jurišić
Appl. Sci. 2025, 15(13), 7216; https://doi.org/10.3390/app15137216 - 26 Jun 2025
Viewed by 303
Abstract
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 [...] Read more.
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
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17 pages, 3699 KiB  
Article
Soybean Cultivar Breeding Has Increased Yields Through Extended Reproductive Growth Periods and Elevated Photosynthesis
by Hongbao Sun, Shuaijie Shen, Jingya Yang, Jun Zou, Matthew Tom Harrison, Zechen Wang, Jiaqi Hu, Haiyu Guo, Renan Caldas Umburanas, Yunlong Zhai, Xinya Wen, Fu Chen and Xiaogang Yin
Plants 2025, 14(11), 1675; https://doi.org/10.3390/plants14111675 - 30 May 2025
Viewed by 499
Abstract
Despite being one of China’s largest soybean (Glycine max L. Merr.) production areas, the Huanghuaihai Farming Region (HFR) has long been plagued by suboptimal yields. While cultivar development has contributed to yield gains in the past, whether such breeding will afford resilience [...] Read more.
Despite being one of China’s largest soybean (Glycine max L. Merr.) production areas, the Huanghuaihai Farming Region (HFR) has long been plagued by suboptimal yields. While cultivar development has contributed to yield gains in the past, whether such breeding will afford resilience under more adverse climatic conditions expected in future remains an open question. Here, we conducted two-year field experiments to contrast the growth and development of soybean cultivars released between 1960 and 2010 in the HFR. We found that cultivar breeding significantly influenced phenology, with contemporary cultivars having shorter and longer vegetative and reproductive growth phases, respectively. Grain filling duration of modern cultivars (LD11, HD14, JD21, and QH34) was 10 days longer than that of older cultivars (JX23 and WF7). Maturity height of modern cultivars decreased over time to a current value of ~80 cm, despite having higher leaf area index (LAI) and SPAD values compared with older cultivars during reproductive development. The quantum yield of electron transport in photosystem I, quantum yield of electron transport chain, photosynthetic performance index, stomatal conductance, net photosynthetic rate, and Rubisco activity of contemporary cultivars was stronger than those of older cultivars during grain filling. Prolonged grain filling duration, higher LAI, greater light interception, and stronger photosynthetic capacity evoked greater rates of grain filling, leading to higher grain weight, seed number, and yield. Genetic evolution of the cultivars over time, warmer conditions, and more precipitation together afforded longer reproductive stages. Our results indicate that yield gains have been realized primarily by cultivar breeding, and to a lesser extent, beneficial climate change. We highlight dynamic source/sink relationships underpinning the co-evolution of photosynthetic traits through soybean breeding, and provide practical advice to guide future breeding efforts. Full article
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26 pages, 4037 KiB  
Article
Cascade Learning Early Classification: A Novel Cascade Learning Classification Framework for Early-Season Crop Classification
by Weilang Kong, Xiaoqi Huang, Jialin Liu, Min Liu, Luo Liu and Yubin Guo
Remote Sens. 2025, 17(10), 1783; https://doi.org/10.3390/rs17101783 - 20 May 2025
Viewed by 348
Abstract
Accurate early-season crop classification is critical for food security, agricultural applications and policymaking. However, when classification is performed earlier, the available time-series data gradually become scarce. Existing methods mainly focus on enhancing the model’s ability to extract features from limited data to address [...] Read more.
Accurate early-season crop classification is critical for food security, agricultural applications and policymaking. However, when classification is performed earlier, the available time-series data gradually become scarce. Existing methods mainly focus on enhancing the model’s ability to extract features from limited data to address this challenge, but the extracted critical phenological information remains insufficient. This study proposes a Cascade Learning Early Classification (CLEC) framework, which consists of two components: data preprocessing and a cascade learning model. Data preprocessing generates high-quality time-series data from the optical, radar and thermodynamic data in the early stages of crop growth. The cascade learning model integrates a prediction task and a classification task, which are interconnected through the cascade learning mechanism. First, the prediction task is performed to supplement more time-series data of the growing stage. Then, crop classification is carried out. Meanwhile, the cascade learning mechanism is used to iteratively optimize the prediction and classification results. To validate the effectiveness of CLEC, we conducted early-season classification experiments on soybean, corn and rice in Northeast China. The experimental results show that CLEC significantly improves crop classification accuracy compared to the five state-of-the-art models in the early stages of crop growth. Furthermore, under the premise of obtaining reliable results, CLEC advances the earliest identifiable timing, moving from the flowing to the third true leaf stage for soybean and from the flooding to the sowing stage for rice. Although the earliest identifiable timing for corn remains unchanged, its classification accuracy improved. Overall, CLEC offers new ideas for solving early-season classification challenges. Full article
(This article belongs to the Section AI Remote Sensing)
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14 pages, 9320 KiB  
Article
A Phenology-Based Evaluation of the Optimal Proxy for Cropland Suitability Based on Crop Yield Correlations from Sentinel-2 Image Time-Series
by Dorijan Radočaj and Mladen Jurišić
Agriculture 2025, 15(8), 859; https://doi.org/10.3390/agriculture15080859 - 15 Apr 2025
Cited by 2 | Viewed by 439
Abstract
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 [...] Read more.
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
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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14 pages, 3449 KiB  
Article
Enhancing Soybean Physiology and Productivity Through Foliar Application of Soluble Monoammonium Phosphate
by Vitor Alves Rodrigues, Luiz Gustavo Moretti, Israel Alves Filho, Marcela Pacola, Josiane Viveiros, Lucas Moraes Jacomassi, Sirlene Lopes Oliveira, Amine Jamal, Tatiani Mayara Galeriani, Murilo de Campos, José Roberto Portugal, João William Bossolani and Carlos Alexandre Costa Crusciol
Agronomy 2025, 15(4), 818; https://doi.org/10.3390/agronomy15040818 - 26 Mar 2025
Cited by 1 | Viewed by 778
Abstract
Phosphorus (P) is essential for crop growth, but its complex behavior in tropical soils necessitates alternative management strategies, such as foliar supplementation. Foliar-applied nutrients act as biostimulants, enhancing stress tolerance and plant productivity. This study assessed the physiological responses of soybean to foliar [...] Read more.
Phosphorus (P) is essential for crop growth, but its complex behavior in tropical soils necessitates alternative management strategies, such as foliar supplementation. Foliar-applied nutrients act as biostimulants, enhancing stress tolerance and plant productivity. This study assessed the physiological responses of soybean to foliar application of soluble monoammonium phosphate (MAP; at a rate of 5 kg ha−1 each application) at different phenological stages (two during vegetative stages V4 and V6 and two during reproductive stages R1 and R3 or all four stages) across two growing seasons in tropical field conditions. Key parameters analyzed included leaf nutrient content, photosynthetic pigments, Rubisco activity, carbohydrate content, gas exchange (photosynthetic rate, stomatal conductance, transpiration, water use efficiency, and carboxylation efficiency), oxidative stress markers, and productivity indicators (100-grain weight and grain yield). MAP application improved all parameters, particularly at R1 and R3. Total chlorophyll increased by 29.2% at R1 and 30.0% when applied at all four stages, while the net photosynthetic rate rose by 15.8% and 18.4%, respectively. Water use efficiency improved by 20.0% at R1 and all four stages, while oxidative stress indicators, such as H2O2 levels, decreased. Rubisco activity increased most at R3 (46.0%) and all four stages (59.9%). Grain yield was highest with MAP spread at all four stages (12.3% increase), though a single application at R1 still boosted yield by 7.4%, compared to the control treatment. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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19 pages, 42632 KiB  
Article
Correlation Between the Growth Index and Vegetation Indices for Irrigated Soybeans Using Free Orbital Images
by Gildriano Soares de Oliveira, Jackson Paulo Silva Souza, Érica Pereira Cardozo, Dhiego Gonçalves Pacheco, Marinaldo Loures Ferreira, Marcelo Coutinho Picanço, João Rafael Silva Soares, Ana Maria Oliveira Souza Alves, André Medeiros de Andrade and Ricardo Siqueira da Silva
AgriEngineering 2025, 7(3), 67; https://doi.org/10.3390/agriengineering7030067 - 5 Mar 2025
Viewed by 1196
Abstract
Soybeans are key in generating foreign currency for the world economy. Geotechnologies, through vegetation indices (VIs) generated by orbital images or remotely piloted aircraft, are essential tools for assessing the impact of climate on productivity and the ecoclimatic suitability of crops. This study [...] Read more.
Soybeans are key in generating foreign currency for the world economy. Geotechnologies, through vegetation indices (VIs) generated by orbital images or remotely piloted aircraft, are essential tools for assessing the impact of climate on productivity and the ecoclimatic suitability of crops. This study aimed to correlate the growth indices from the CLIMEX model, previously validated, with VIs derived from orbital remote sensing and ecological niche modeling for soybean cultivation in six irrigated pivots located in the northwest of Minas Gerais, Brazil. The maximum normalized difference vegetation index (NDVImax) and the maximum soil-adjusted vegetation index (SAVImax) were extracted from Landsat-8 OLI/TIRS sensor images for the 2016 to 2019 harvests during the R1 to R3 phenological stages. The maximum NDVI values varied across the study regions and crops, ranging from 0.27 to 0.95. Similarly, SAVI values exhibited variability, with the maximum SAVI ranging from 0.13 to 0.85. The growth index (GIw), derived from the CLIMEX model, ranged from 0.88 to 1. The statistical analysis confirmed a significant correlation (p < 0.05) between NDVImax and GIw only for the 2018/19 harvest, with a Pearson correlation coefficient of r = 0.86, classified as very strong. Across all harvests, NDVI consistently outperformed SAVI in correlation strength with GIw. Using geotechnologies through remote sensing shows promise for correlating spectral indices and climate suitability models. However, when using a valid model, all crops did not correlate. Still, our study has the potential to be improved by investigating new hypotheses, such as using drone images with better resolution (spatial, spectral, temporal, and radiometric) and adjusting the response of soybean vegetation indices and the phenological stage. Our results correlating the CLIMEX model of growth indices with vegetation indices have the potential for monitoring soybean cultivation and analyzing the performance of varieties but require a more in-depth view to adapt the methodology. Full article
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18 pages, 4807 KiB  
Article
Accumulated Photosynthetically Active Radiation and Its Heterogeneity Collectively Decrease Soybean Yield in Apple-Based Intercropping Systems
by Ruidong Peng, Huasen Xu, Huaxing Bi and Ning Wang
Agronomy 2025, 15(3), 581; https://doi.org/10.3390/agronomy15030581 - 26 Feb 2025
Viewed by 554
Abstract
The under-canopy light environment in agroforestry systems is a key limiting factor for the growth of intercropped crops. However, the impact of under-canopy light heterogeneity on crop yield remains unclear. This study focused on 4 (Y4)-, 6 (Y6)-, and 8 (Y8)-year-old apple tree/soybean [...] Read more.
The under-canopy light environment in agroforestry systems is a key limiting factor for the growth of intercropped crops. However, the impact of under-canopy light heterogeneity on crop yield remains unclear. This study focused on 4 (Y4)-, 6 (Y6)-, and 8 (Y8)-year-old apple tree/soybean intercropping systems with root barriers, measuring under-canopy photosynthetically active radiation, yield, and yield components at different phenological stages of soybean, and establishing a quantitative relationship between light heterogeneity and soybean yield. In the apple/soybean intercropping system, the spatial heterogeneity of accumulated photosynthetically active radiation (APAR) is greatest in Y6, with the APAR divided into five categories parallelized along the tree rows. Y4, which had the least spatial APAR heterogeneity, was divided into three categories. The APAR was split into two classes in Y8. The seed number per plant and 100-seed weight of soybean decreased with the increase in tree age. Compared to Y4, yields of Y6 and Y8 treatments decreased by 22.6% and 46.2%, respectively. The reduction in APAR showed a negative effect on yield and its components of soybean, especially in Y4. The shading intensity and under-canopy light heterogeneity jointly constrained the intercropped soybean yield; this effect was gradually strengthened with increasing tree age. Different measures should be taken according to different tree ages and soybean growth stages in intercropping systems to reduce the adverse effects of under-canopy light on soybean yield. Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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28 pages, 3329 KiB  
Article
PhenoCam Guidelines for Phenological Measurement and Analysis in an Agricultural Cropping Environment: A Case Study of Soybean
by S. Sunoj, C. Igathinathane, Nicanor  Saliendra, John Hendrickson, David Archer and Mark Liebig
Remote Sens. 2025, 17(4), 724; https://doi.org/10.3390/rs17040724 - 19 Feb 2025
Viewed by 998
Abstract
A PhenoCam is a near-surface remote sensing system traditionally used for monitoring phenological changes in diverse landscapes. Although initially developed for forest landscapes, these near-surface remote sensing systems are increasingly being adopted in agricultural settings, with deployment expanding from 106 sites in 2020 [...] Read more.
A PhenoCam is a near-surface remote sensing system traditionally used for monitoring phenological changes in diverse landscapes. Although initially developed for forest landscapes, these near-surface remote sensing systems are increasingly being adopted in agricultural settings, with deployment expanding from 106 sites in 2020 to 839 sites by February 2025. However, agricultural applications present unique challenges because of rapid crop development and the need for precise phenological monitoring. Despite the increasing number of PhenoCam sites, clear guidelines are missing on (i) the phenological analysis of images, (ii) the selection of a suitable color vegetation index (CVI), and (iii) the extraction of growth stages. This knowledge gap limits the full potential of PhenoCams in agricultural applications. Therefore, a study was conducted in two soybean (Glycine max L.) fields to formulate image analysis guidelines for PhenoCam images. Weekly visual assessments of soybean phenological stages were compared with PhenoCam images. A total of 15 CVIs were tested for their ability to reproduce the seasonal variation from RGB, HSB, and Lab color spaces. The effects of image acquisition time groups (10:00 h–14:00 h) and object position (ROI locations: far, middle, and near) on selected CVIs were statistically analyzed. Excess green minus excess red (EXGR), color index of vegetation (CIVE), green leaf index (GLI), and normalized green red difference index (NGRDI) were selected based on the least deviation from their loess-smoothed phenological curve at each image acquisition time. For the selected four CVIs, the time groups did not have a significant effect on CVI values, while the object position had significant effects at the reproductive phase. Among the selected CVIs, GLI and EXGR exhibited the least deviation within the image acquisition time and object position groups. Overall, we recommend employing a consistent image acquisition time to ensure sufficient light, capture the largest possible image ROI in the middle region of the field, and apply any of the selected CVIs in order of GLI, EXGR, NGRDI, and CIVE. These results provide a standardized methodology and serve as guidelines for PhenoCam image analysis in agricultural cropping environments. These guidelines can be incorporated into the standard protocol of the PhenoCam network. Full article
(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing II)
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12 pages, 3152 KiB  
Article
High-Precision Phenotyping in Soybeans: Applying Multispectral Variables Acquired at Different Phenological Stages
by Celí Santana Silva, Dthenifer Cordeiro Santana, Fábio Henrique Rojo Baio, Ana Carina da Silva Cândido Seron, Rita de Cássia Félix Alvarez, Larissa Pereira Ribeiro Teodoro, Carlos Antônio da Silva Junior and Paulo Eduardo Teodoro
AgriEngineering 2025, 7(2), 47; https://doi.org/10.3390/agriengineering7020047 - 19 Feb 2025
Cited by 1 | Viewed by 749
Abstract
Soybean stands out for being the most economically important oilseed in the world. Remote sensing techniques and precision agriculture are being analyzed through research in different agricultural regions as a technological system aiming at productivity and possible low-cost reduction. Machine learning (ML) methods, [...] Read more.
Soybean stands out for being the most economically important oilseed in the world. Remote sensing techniques and precision agriculture are being analyzed through research in different agricultural regions as a technological system aiming at productivity and possible low-cost reduction. Machine learning (ML) methods, together with the advent of demand for remotely piloted aircraft available on the market in the recent decade, have been conducive to remote sensing data processes. The objective of this work was to evaluate the best ML and input configurations in the classification of agronomic variables in different phenological stages. The spectral variables were obtained in three phenological stages of soybean genotypes: V8 (at 45 days after emergence—DAE), R1 (60 DAE), and R5 (80 DAE). A Sensefly eBee fixed-wing RPA equipped with the Parrot Sequoia multispectral sensor coupled to the RGB sensor was used. The Sequoia multispectral sensor with an RGB sensor acquired reflectance at wavelengths of blue (450 nm), green (550 nm), red (660 nm), near-infrared (735 nm), and infrared (790 nm). The following were used to evaluate the agronomic traits: days to maturity, number of branches, productivity, plant height, height of the first pod insertion and diameter of the main stem. The random forest (RF) model showed greater accuracy with data collected in the R5 stage, whose accuracies were close to 56 for the percentage of correct classifications (CC), close to 0.2 for Kappa, and above 0.55 for the F-score. Logistic regression (RL) and support vector machine (SVM) models showed better performance in the early reproductive stage R1, with accuracies above 55 for CC, close to 0.1 for Kappa, and close to 0.4 for the F-score. J48 performed better with data from the V8 stage, with accuracies above 50 for CC and close to 0.4 for the F-score. This reinforces that the use of different specific spectra for each model can enhance accuracy, optimizing the choice of model according to the phenological stage of the plants. Full article
(This article belongs to the Special Issue The Future of Artificial Intelligence in Agriculture)
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17 pages, 8773 KiB  
Article
Foliar Application and Translocation of Radiolabeled Zinc Oxide Suspension vs. Zinc Sulfate Solution by Soybean Plants
by Anita Beltrame, João Paulo Rodrigues Marques, Mariana Ayres Rodrigues, Eduardo de Almeida, Márcio Arruda Bacchi, Elisabete Aparecida De Nadai Fernandes, Rafael Otto and Hudson Wallace Pereira de Carvalho
Agriculture 2025, 15(2), 197; https://doi.org/10.3390/agriculture15020197 - 17 Jan 2025
Viewed by 2074
Abstract
The present study employed a 65Zn radioactive isotope as a tracer to investigate the foliar uptake and distribution patterns of ZnO concentrated suspension- and ZnSO4 solution-sprayed on soybean plant leaves. The radiolabeled foliar treatments were sprayed on the leaves at V4 [...] Read more.
The present study employed a 65Zn radioactive isotope as a tracer to investigate the foliar uptake and distribution patterns of ZnO concentrated suspension- and ZnSO4 solution-sprayed on soybean plant leaves. The radiolabeled foliar treatments were sprayed on the leaves at V4 and V8 phenological stages. The radioactivity of 65Zn in the leaves, roots, stems, and pods was determined using γ-ray spectrometry. After the first foliar spray, V4, the partition of radiolabeled Zn in plants treated with ZnO and ZnSO4 was 99.22% and 98.12% in treated leaves, 0.15% and 0.39% in stems, 0.16% and 0.29% in roots, and 0.47% and 1.19% in newly expanded non-treated leaves, respectively. After two sprayings, V4 and V8, the partition of radiolabeled Zn in plants treated with ZnO and ZnSO4 was 92.56% and 92.18% in treated leaves, 0.92% and 0.70% in stems, 0.52% and 0.39% in roots, 5.60% and 6.15% in newly expanded non-treated leaves, and 0.43% and 0.61% in grains, respectively. The total fraction translocated from the application tissue was 0.79% and 1.91% for ZnO and ZnSO4, respectively, after 12 days and 8.03% and 8.48% for ZnO and ZnSO4, respectively, after 72 days. An anatomical analysis revealed that plants cultivated in a nutrition solution with 10% ionic strength had 63% fewer stomata, and the xylem vessels were 63% smaller compared to plants grown in a solution with 100% Zn ionic. One can conclude that after a short period, 12 days, the absorption and translocation of ZnSO4 was higher and faster than ZnO, and after the long period, 72 days, their performance was similar. Full article
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26 pages, 9074 KiB  
Article
Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation
by Reza Maleki, Falin Wu, Guoxin Qu, Amel Oubara, Loghman Fathollahi and Gongliu Yang
Remote Sens. 2025, 17(2), 283; https://doi.org/10.3390/rs17020283 - 15 Jan 2025
Cited by 1 | Viewed by 962
Abstract
The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences [...] Read more.
The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences in crop growth stages between training and target areas. This study proposes the Adaptive Month Matching (AMM) method to align the phenological stages of crops between training and target areas for enhanced transfer learning in cropland segmentation. In the AMM method, an optimal Sentinel-2 monthly time series is identified in the training area based on deep learning model performance for major crops common to both areas. A month-matching process then selects the optimal Sentinel-2 time series for the target area by aligning the phenological stages between the training and target areas. In this study, the training area covered part of the Mississippi River Delta, while the target areas included diverse regions across the US and Canada. The evaluation focused on major crops, including corn, soybeans, rice, and double-cropped winter wheat/soybeans. The trained deep learning model was transferred to the target areas, and accuracy metrics were compared across different time series chosen by various phenological alignment methods. The AMM method consistently demonstrated strong performance, particularly in transferring to rice-growing regions, achieving an overall accuracy of 98%. It often matched or exceeded other phenological matching techniques in corn segmentation, with an average overall accuracy across all target areas exceeding 79% for cropland segmentation. Full article
(This article belongs to the Special Issue Remote Sensing for Precision Farming and Crop Phenology)
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24 pages, 6601 KiB  
Article
Residual Effect of Silicate Agromineral Application on Soil Acidity, Mineral Availability, and Soybean Anatomy
by Mariana de Carvalho Ribeiro, Antonio Ganga, Isabella Silva Cattanio, Aline Redondo Martins, Rodrigo Silva Alves, Luís Gustavo Frediani Lessa, Hamilton Seron Pereira, Fernando Shintate Galindo, Marcelo Carvalho Minhoto Teixeira Filho, Cassio Hamilton Abreu-Junior, Gian Franco Capra, Arun Dilipkumar Jani and Thiago Assis Rodrigues Nogueira
Agronomy 2025, 15(1), 5; https://doi.org/10.3390/agronomy15010005 - 24 Dec 2024
Cited by 1 | Viewed by 1759
Abstract
Silicate agrominerals (SA) may be sustainable soil amendments that can minimize dependence on conventional fertilizers (CF). We evaluated the residual effects of SA application as a source of Si and as a soil remineralizer, using soils with contrasting chemical-physical features cultivated with soybean. [...] Read more.
Silicate agrominerals (SA) may be sustainable soil amendments that can minimize dependence on conventional fertilizers (CF). We evaluated the residual effects of SA application as a source of Si and as a soil remineralizer, using soils with contrasting chemical-physical features cultivated with soybean. The experiment was conducted under greenhouse conditions and treatments were arranged in a 5 × 2 + 2 factorial scheme: five rates of SA, two soils in addition to CF. The soil was incubated before cultivation, followed by the sequential sowing of corn and soybean. At the R4 phenological stage, when the pods were fully developed, soybean plants were harvested for anatomical leaf tissue analysis and P, Ca, Mg, and Si accumulation. After harvest, the soil was analyzed. Application of SA rates reduced potential acidity (H + Al) and exchangeable acidity (Al3+) and increased soil pH, sum of bases (SB), cation-exchange capacity (CEC), and base saturation (BS), in addition to promoting the nutrient’s availability and Si. Stomatal density was higher on the adaxial face of plants cultivated in the medium-textured soil. Silicate agrominerals can be used as a soil acidity corrector and remineralizer, improving the root environment and increasing the availability of nutrients and silicon. Full article
(This article belongs to the Special Issue Safe and Efficient Utilization of Water and Fertilizer in Crops)
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39 pages, 13451 KiB  
Article
Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images
by Saeideh Maleki, Nicolas Baghdadi, Hassan Bazzi, Cassio Fraga Dantas, Dino Ienco, Yasser Nasrallah and Sami Najem
Remote Sens. 2024, 16(23), 4548; https://doi.org/10.3390/rs16234548 - 4 Dec 2024
Cited by 2 | Viewed by 1704
Abstract
Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering [...] Read more.
Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering coefficients and polarimetric parameters), alongside phenological features derived from both S1 and S2 time series (harmonic coefficients and median features), for classifying sunflower, soybean, and maize. Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost classifiers were applied across various dataset configurations and train-test splits over two study sites and years in France. Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. The optimal dataset for mapping all three crops combined S1 backscattering coefficients with S2 vegetation indices, with comparable results between phenological features and time series data (mean F1 scores of 89.9% for sunflower, 76.6% for soybean, and 91.1% for maize). However, when using individual satellite sensors, S1 phenological features and time series outperformed S2 for sunflower, while S2 was superior for soybean and maize. Both phenological features and time series data produced close mean F1 scores across spatial, temporal, and spatiotemporal transfer scenarios, though median features dataset was the best choice for spatiotemporal transfer. Polarimetric S1 data did not yield effective results. The InceptionTime classifier further improved classification accuracy over XGBoost for all crops, with the degree of improvement varying by crop and dataset (the highest mean F1 scores of 90.6% for sunflower, 86.0% for soybean, and 93.5% for maize). Full article
(This article belongs to the Special Issue Intelligent Extraction of Phenotypic Traits in Agroforestry)
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19 pages, 1444 KiB  
Review
Possible Impacts of Elevated CO2 and Temperature on Growth and Development of Grain Legumes
by Rajanna G. Adireddy, Saseendran S. Anapalli, Krishna N. Reddy, Partson Mubvumba and Justin George
Environments 2024, 11(12), 273; https://doi.org/10.3390/environments11120273 - 2 Dec 2024
Cited by 4 | Viewed by 2634
Abstract
Carbon dioxide (CO2) is the most abundant greenhouse gas (GHG) in the atmosphere and the substrate for the photosynthetic fixation of carbohydrates in plants. Increasing GHGs from anthropogenic emissions is warming the Earth’s atmospheric system at an alarming rate and changing [...] Read more.
Carbon dioxide (CO2) is the most abundant greenhouse gas (GHG) in the atmosphere and the substrate for the photosynthetic fixation of carbohydrates in plants. Increasing GHGs from anthropogenic emissions is warming the Earth’s atmospheric system at an alarming rate and changing its climate, which can affect photosynthesis and other biochemical reactions in crop plants favorably or unfavorably, depending on plant species. For the substrate role in plant carbon reduction reactions, CO2 concentration ([CO2]) in air potentially enhances photosynthesis. However, N uptake and availability for protein synthesis can be a potential limiting factor in enhanced biomass synthesis under enriched [CO2] conditions across species. Legumes are C3 plants and symbiotic N fixers and are expected to benefit from enhanced [CO2] in the air. However, the concurrent increase in air temperatures with enhanced [CO2] demands more detailed investigations on the effects of [CO2] enhancement on grain legume growth and yield. In this article, we critically reviewed and presented the online literature on growth, phenology, photosynthetic rate, stomatal conductance, productivity, soil health, and insect behavior under elevated [CO2] and temperature conditions. The review revealed that specific leaf weight, pod weight, and nodule number and weight increased significantly under elevated [CO2] of up to 750 ppm. Under elevated [CO2], two mechanisms that were affected were the photosynthesis rate (increased) and stomatal conductivity (decreased), which helped enhance water use efficiency in the C3 legume plants to achieve higher yields. Exposure of legumes to elevated levels of [CO2] when water stressed resulted in an increase of 58% in [CO2] uptake, 73% in transpiration efficiency, and 41% in rubisco carboxylation and decreased stomatal conductance by 15–30%. The elevated [CO2] enhanced the yields of soybean by 10–101%, peanut by 28–39%, mung bean by 20–28%, chickpea by 26–31%, and pigeon pea by 31–38% over ambient [CO2]. However, seed nutritional qualities like protein, Zn, and Ca were significantly decreased. Increased soil temperatures stimulate microbial activity, spiking organic matter decomposition rates and nutrient release into the soil system. Elevated temperatures impact insect behavior through higher plant feeding rates, posing an enhanced risk of invasive pest attacks in legumes. However, further investigations on the potential interaction effects of elevated [CO2] and temperatures and extreme climate events on growth, seed yields and nutritional qualities, soil health, and insect behavior are required to develop climate-resilient management practices through the development of novel genotypes, irrigation technologies, and fertilizer management for sustainable legume production systems. Full article
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16 pages, 8117 KiB  
Review
Invasive Characteristics and Impacts of Ambrosia trifida
by Hisashi Kato-Noguchi and Midori Kato
Agronomy 2024, 14(12), 2868; https://doi.org/10.3390/agronomy14122868 - 1 Dec 2024
Cited by 5 | Viewed by 1707
Abstract
Ambrosia trifida L. is native to North America, has been introduced into many countries in Europe and East Asia, and is also expanding its habitat in its native ranges. Ambrosia trifida grows in sunny and humid environments, such as grasslands, riverbanks, floodplains, abandoned [...] Read more.
Ambrosia trifida L. is native to North America, has been introduced into many countries in Europe and East Asia, and is also expanding its habitat in its native ranges. Ambrosia trifida grows in sunny and humid environments, such as grasslands, riverbanks, floodplains, abandoned places, and agricultural fields, as an invasive plant species. Ambrosia trifida has a strong adaptive ability to adverse conditions and shows great variation in seed germination phenology and plant morphology in response to environmental conditions. Effective natural enemies have not been found in its native or introduced ranges. The species is allelopathic and contains several allelochemicals. These characteristics may contribute to the competitive ability and invasiveness of this species. Ambrosia trifida significantly reduces species diversity and plant abundance in its infested plant communities. The species also causes significant yield loss in summer crop production, such as in maize, soybean, sunflower, and cotton production. Ambrosia trifida is capable of rapid evolution against herbicide pressure. Populations of Ambrosia trifida resistant to glyphosate, ALS-inhibiting herbicides, and PPO-inhibiting herbicides, as well as cross-resistant populations, have already appeared. An integrated weed management protocol with a more diverse combination of herbicide sites of action and other practices, such as tillage, the use of different crop species, crop rotation, smart decision tools, and innovative equipment, would be essential to mitigate herbicide-dependent weed control practices and may be one sustainable system for Ambrosia trifida management. Full article
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