Search Results (58)

Accurate crop yield estimation under differentiated management practices is a core requirement for the development of smart agriculture. However, current yield estimation models face two major challenges: limited adaptability to different management practices, thus exhibiting poor generalizability, and ineffective integration of multi-source remote sensing features, limiting further improvements in estimation accuracy. To address these issues, this study integrated UAV-based multispectral and thermal infrared remote sensing data to propose a yield estimation framework based on multi-source feature fusion. First, three machine learning algorithms—Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to retrieve key biochemical parameters of winter wheat. The RF model demonstrated superior performance, with retrieval accuracies for chlorophyll, nitrogen, and phosphorus contents of R² = 0.8347, 0.5914, and 0.9364 and RMSE = 0.2622, 0.4127, and 0.0236, respectively. Subsequently, yield estimation models were constructed by integrating the retrieved biochemical parameters with phenotypic traits such as plant height and biomass. The RF model again exhibited superior performance (R² = 0.66, RMSE = 867.28 kg/ha). SHapley Additive exPlanations (SHAP) analysis identified May chlorophyll content (Chl-5) and March chlorophyll content (Chl-3) as the most critical variables for yield prediction, with stable positive contributions to yield when their values exceeded 2.80 mg/g and 2.50 mg/g, respectively. The quantitative assessment of management practices revealed that the straw return + 50% inorganic fertilizer + 50% organic fertilizer (RIO50) treatment under the combined organic–inorganic fertilization regime achieved the highest measured grain yield (11,469 kg/ha). Consequently, this treatment can be regarded as an optimized practice for attaining high yield. This study confirms that focusing on chlorophyll dynamics during key physiological stages is an effective approach for enhancing yield estimation accuracy under varied management practices, providing a technical basis for precise field management. Full article

The convergence of agricultural biotechnology and artificial intelligence is reshaping modern crop improvement. Despite a surge of studies integrating artificial intelligence and biotechnology, the rapidly expanding literature on intelligent crop breeding remains fragmented across molecular, phenotypic, and computational dimensions. Existing reviews often rely on traditional bibliometric or narrative approaches that fail to capture the deep semantic evolution of research themes. To address this gap, this study employs the BERTopic model to systematically analyze 1867 articles (1995–2025, WoS Core Collection), mapping the thematic landscape and temporal evolution of intelligent crop breeding and revealing how methodological and application-oriented domains have co-evolved over time. Eight core topics emerge, i.e., (T0) genomic prediction and genotype–environment modeling; (T1) UAV remote sensing and multimodal phenotyping; (T2) stress-tolerant breeding and root phenotypes; (T3) ear/pod counting with deep learning; (T4) grain trait representation and evaluation; (T5) CRISPR and genome editing; (T6) spike structure recognition and 3D modeling; and (T7) maize tassel detection and developmental staging. Topic-evolution analyses indicate a co-development pattern, where genomic prediction provides a stable methodological backbone, while phenomics (UAV/multimodal imaging, organ-level detection, and 3D reconstruction) propels application-oriented advances. Attention dynamics reveal increasing momentum in image-based counting (T3), grain quality traits (T4), and CRISPR-enabled editing (T5), alongside a plateau in traditional mainstays (T0, T1) and mild cooling in root phenotyping under abiotic stress (T2). Quality stratification (citation quartiles, Q1–Q4) shows high-impact concentration in T0/T1 and a growing tail of application-driven work across T3–T7. Journal analysis reveals a complementary publication ecosystem: Frontiers in Plant Science and Plant Methods anchor cross-disciplinary dissemination; Remote Sensing and Computers and Electronics in Agriculture host engineering-centric phenomics; genetics/breeding journals sustain T0/T2; and molecular journals curate T5. These findings provide an integrated overview of methods, applications, and publication venues, offering practical guidance for research planning, cross-field collaboration, and translational innovation in intelligent crop breeding. Full article

Advancements in unmanned aerial vehicle (UAV) multispectral systems offer robust technical support for the precise and efficient estimation of japonica rice yield in cold regions within the framework of precision agriculture. These innovations also present a viable alternative to conventional yield estimation methods. However, recent research suggests that reliance solely on vegetation indices (VIs) may result in inaccurate yield estimations due to variations in crop cultivars, growth stages, and environmental conditions. This study investigated six fertilization gradient experiments involving two conventional japonica rice varieties (KY131, SJ22) and two hybrid japonica rice varieties (CY31, TLY619) at Yanjiagang Farm in Heilongjiang Province during 2023. By integrating UAV multispectral data with machine learning techniques, this research aimed to derive critical phenotypic parameters of rice and estimate yield. This study was conducted in two phases: In the first phase, models for assessing phenotypic traits such as leaf area index (LAI), canopy cover (CC), plant height (PH), and above-ground biomass (AGB) were developed using remote sensing spectral indices and machine learning algorithms, including Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). In the second phase, plot yields for hybrid rice and conventional rice were predicted using key phenotypic parameters at critical growth stages through linear (Multiple Linear Regression, MLR) and nonlinear regression models (RF). The findings revealed that (1) Phenotypic traits at critical growth stages exhibited a strong correlation with rice yield, with correlation coefficients for LAI and CC exceeding 0.85 and (2) the accuracy of phenotypic trait evaluation using multispectral data was high, demonstrating practical applicability in production settings. Remarkably, the R² for CC based on the RF algorithm exceeded 0.9, while R² values for PH and AGB using the RF algorithm and for LAI using the XGBoost algorithm all surpassed 0.8. (3) Yield estimation performance was optimal at the heading (HD) stage, with the RF model achieving superior accuracy (R² = 0.86, RMSE = 0.59 t/ha) compared to other growth stages. These results underscore the immense potential of combining UAV multispectral data with machine learning techniques to enhance the accuracy of yield estimation for cold-region japonica rice. This innovative approach significantly supports optimized decision-making for farmers in precision agriculture and holds substantial practical value for rice yield estimation and the sustainable advancement of rice production. Full article

Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate–resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought–heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches—genomics, transcriptomics, proteomics, and metabolomics—to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA–SnRK2 signaling cascade, HSF–HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This “pixels-to-proteins” paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties. Full article

With the advancement of remote sensing imagery and multimodal sensing technologies, monitoring plant trait dynamics has emerged as a critical area of research in modern agriculture. Traditional approaches, which rely on handcrafted features and shallow models, struggle to effectively address the complexity inherent in high-dimensional and multisource data. In contrast, deep learning, with its end-to-end feature extraction and nonlinear modeling capabilities, has substantially improved monitoring accuracy and automation. This review summarizes recent developments in the application of deep learning methods—including CNNs, RNNs, LSTMs, Transformers, GANs, and VAEs—to tasks such as growth monitoring, yield prediction, pest and disease identification, and phenotypic analysis. It further examines prominent research themes, including multimodal data fusion, transfer learning, and model interpretability. Additionally, it discusses key challenges related to data scarcity, model generalization, and real-world deployment. Finally, the review outlines prospective directions for future research, aiming to inform the integration of deep learning with phenomics and intelligent IoT systems and to advance plant monitoring toward greater intelligence and high-throughput capabilities. Full article

The male flower of the maize plant, known as the tassel, is a strong indicator of the growth, development, and reproductive stages of maize crops. Monitoring maize tassels under natural conditions is significant for maize breeding, management, and yield estimation. Unmanned aerial vehicle (UAV) remote sensing combined with deep learning-based semantic segmentation offers a novel approach for monitoring maize tassel phenotypic traits. The morphological and size variations in maize tassels, together with numerous similar interference factors in the farmland environment (such as leaf veins, female ears, etc.), pose significant challenges to the accurate segmentation of tassels. To address these challenges, we propose DECC-Net, a novel segmentation model designed to accurately extract maize tassels from complex farmland environments. DECC-Net integrates the Dynamic Kernel Feature Extraction (DKE) module to comprehensively capture semantic features of tassels, along with the Lightweight Channel Cross Transformer (LCCT) and Adaptive Feature Channel Enhancement (AFE) modules to guide effective fusion of multi-stage encoder features while mitigating semantic gaps. Experimental results demonstrate that DECC-Net achieves advanced performance, with IoU and Dice scores of 83.3% and 90.9%, respectively, outperforming existing segmentation models while exhibiting robust generalization across diverse scenarios. This work provides valuable insights for maize varietal selection, yield estimation, and field management operations. Full article

The modelling and prediction of important agronomic traits using remotely sensed data is an evolving science and an attractive concept for plant breeders, as manual crop phenotyping is both expensive and time consuming. Major limiting factors in creating robust prediction models include the appropriate integration of data across different years and sites, and the availability of sufficient genetic and phenotypic diversity. Variable weather patterns, especially at higher latitudes, add to the complexity of this integration. This study introduces a novel approach by using photothermal time units to align spectral data from unmanned aerial system images of spring, winter, and facultative oat (Avena sativa) trials conducted over different years at a trial site at Aberystwyth, on the western Atlantic seaboard of the UK. The resulting regression and classification models for various agronomic traits are of significant interest to oat breeding programmes. The potential applications of these findings include optimising breeding strategies, improving crop yield predictions, and enhancing the efficiency of resource allocation in breeding programmes. Full article

Alfalfa is a deep-rooted perennial forage crop with diverse drought-tolerant traits. This study evaluated 250 alfalfa half-sib populations over three growing seasons (2021–2023) under irrigated and rainfed conditions in the Mediterranean drought-prone region of Central Chile (Cauquenes), aiming to identify high-yielding, drought-tolerant populations using remote sensing. Specifically, we assessed RGB-derived indices and canopy temperature difference (CTD; Tc − Ta) as proxies for forage yield (FY). The results showed considerable variation in FY across populations. Under rainfed conditions, winter FY ranged from 1.4 to 6.1 Mg ha⁻¹ and total FY from 3.7 to 14.7 Mg ha⁻¹. Under irrigation, winter FY reached up to 8.2 Mg ha⁻¹ and total FY up to 25.1 Mg ha⁻¹. The AlfaL4-5 (SARDI7), AlfaL57-7 (WL903), and AlfaL62-9 (Baldrich350) populations consistently produced the highest yields across regimes. RGB indices such as hue, saturation, b*, v*, GA, and GGA positively correlated with FY, while intensity, lightness, a*, and u* correlated negatively. CTD showed a significant negative correlation with FY across all seasons and water regimes. These findings highlight the potential of RGB imaging and CTD as effective, high-throughput field phenotyping tools for selecting drought-resilient alfalfa genotypes in Mediterranean environments. Full article

Traditional maize (Zea mays L.) breeding approaches use directly measured phenotypic performance to make decisions for the next generation of crosses. Indirect assessment of cultivar performance can be utilized using various methods such as genomic predictions and remote sensing. However, some secondary traits might expand the breeder’s ability to make informed decisions within a single season, facilitating an increase in breeding speed. We hypothesized that assessment of photosynthetic performance with chlorophyll a fluorescence (ChlF) might be efficient for in-season predictions of yield and grain moisture. The experiment was set with 16 maize hybrids over three consecutive years (2017–2019). ChlF was measured on dark-adapted leaves in the morning during anthesis. Partial least squares models were fitted and the efficiency of indirect selection was assessed. The results showed variability in the traits used in this study. Genetic correlations among all traits were mainly very weak and negative. Heritability estimates for all traits were moderately high to high. The model with 10 latent variables showed a higher predictive ability for grain yield (GY) than other models. The efficiency of the indirect selection for GY using biophysical parameters was lower than direct selection efficiency, while the indirect selection efficiency for grain moisture using biophysical parameters was relatively high. The results of this study highlight the significance and applicability of the ChlF transients in maize breeding programs. Full article

Accurate, high-throughput canopy phenotyping using UAV-based multispectral remote sensing is critically important for optimizing the management and breeding of foxtail millet in rainfed regions. This study integrated multi-temporal field measurements of leaf water content, SPAD-derived chlorophyll, and leaf area index (LAI) with UAV imagery (red, green, red-edge, and near-infrared bands) across two sites and two consecutive years (2023 and 2024) in Shanxi Province, China. Various modeling approaches, including Random Forest, Gradient Boosting, and regularized regressions (e.g., Ridge and Lasso), were evaluated for cross-regional and cross-year extrapolation. The results showed that single-site modeling achieved coefficients of determination (R²) of up to 0.95, with mean relative errors of 10–15% in independent validations. When models were transferred between sites, R² generally remained between 0.50 and 0.70, although SPAD estimates exhibited larger deviations under high-nitrogen conditions. Even under severe drought in 2024, cross-year predictions still attained R² values near 0.60. Among these methods, tree-based models demonstrated a strong capability for capturing nonlinear canopy trait dynamics, whereas regularized regressions offered simplicity and interpretability. Incorporating multi-site and multi-year data further enhanced model robustness, increasing R² above 0.80 and markedly reducing average prediction errors. These findings demonstrate that rigorous radiometric calibration and appropriate vegetation index selection enable reliable UAV-based phenotyping for foxtail millet in diverse environments and time frames. Thus, the proposed approach provides strong technical support for precision management and cultivar selection in semi-arid foxtail millet production systems. Full article

Ensuring sufficient nitrogen intake during the early growth stages of Torreya grandis is crucial for improving future fruit yield and quality. Hyperspectral remote sensing, enabled by unmanned aerial vehicle (UAV) platforms, provides extensive spectral information on forest canopies across large areas. However, the potential of combining multidimensional optimized spectral features with advanced machine learning models to estimate leaf nutrient stress has not yet been fully exploited. This study aims to combine optimized spectral indices and ensemble learning methods to enhance the accuracy and robustness of estimating leaf nitrogen content (LNC) in Torreya grandis. Initially, based on full-band spectral information, five spectral transformations were applied to the original spectra. Then, nine two-band spectral indices and twelve three-band spectral indices were optimized based on published formulas. This process created a total of 27 spectral features across three dimensions. Subsequently, spectral features of varying dimensions were combined with multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and eXtreme Gradient Boosting (XGBoost) to train base estimators for ensemble models. Using a stacking strategy, various modeling combinations were experimented with, resulting in the construction of 22 LNC estimation models. The results indicate that combining two-band and three-band spectral features can more comprehensively capture the subtle changes in the nitrogen status of Torreya grandis, with the optimized spectral index mNDVI_blue (555, 569, 572) showing the highest correlation with LNC at −0.820. In the modeling phase, the base estimators used MLR, RF, and XGBoost, while the meta estimator employed MLR’s stacking model to achieve the highest accuracy and relatively high stability on the validation set (R² = 0.846, RMSE = 1.231%, MRE = 3.186%). This study provides a reference for the efficient and non-destructive detection of LNC or other phenotypic traits in large-scale economic forest crops using UAV hyperspectral technology. Full article

Crop diseases are a significant constraint to agricultural production globally. Plant disease phenotyping is crucial for the identification, development, and deployment of effective breeding strategies, but phenotyping methodologies have not kept pace with the rapid progress in the genetic and genomic characterization of hosts and pathogens, still largely relying on visual assessment by trained experts. Remote sensing technologies were used to develop an automatic framework for extracting the stems of individual plants from RGB images for use in a pipeline for the automated quantification of blackleg crown canker (Leptopshaeria maculans) in mature Brassica napus plants. RGB images of the internal surfaces of stems cut transversely (cross-section) and vertically (longitudinal) were extracted from 722 and 313 images, respectively. We developed an image processing algorithm for extracting and spatially labeling up to eight individual plants within images. The method combined essential image processing techniques to achieve precise plant extraction. The approach was validated by performance metrics such as true and false positive rates and receiver operating curves. The framework was 98% and 86% accurate for cross-section and longitudinal sections, respectively. This algorithm is fundamental for the development of an accurate and precise quantification of disease in individual plants, with wide applications to plant research, including disease resistance and physiological traits for crop improvement. Full article

Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R²: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R²: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R²: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications. Full article

Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R² = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R² = 0.21 to R² = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions. Full article

Developing new bread wheat varieties that can be successfully grown in saline conditions has become a pressing task for plant breeders. High-throughput phenotyping tools are crucial for this task. Proximal remote sensing is gaining popularity in breeding programs as a quick, cost-effective, and non-invasive tool to assess canopy structure and physiological traits in large genetic pools. Limited research has been conducted on the effectiveness of combining RGB and thermal imaging to assess the salt tolerance of different wheat genotypes. This study aimed to evaluate the effectiveness of combining several indices derived from thermal infrared and RGB images with artificial neural networks (ANNs) for assessing relative water content (RWC), chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (Chlt), and plant dry weight (PDW) of 18 recombinant inbred lines (RILs) and their 3 parents irrigated with saline water (150 mM NaCl). The results showed significant differences in various traits and indices among the tested genotypes. The normalized relative canopy temperature (NRCT) index exhibited strong correlations with RWC, Chla, Chlb, Chlt, and PDW, with R² values ranging from 0.50 to 0.73, 0.53 to 0.76, 0.68 to 0.84, 0.68 to 0.84, and 0.52 to 0.76, respectively. Additionally, there was a strong relationship between several RGB indices and measured traits, with the highest R² values reaching up to 0.70. The visible atmospherically resistant index (VARI), a popular index derived from RGB imaging, showed significant correlations with NRCT, RWC, Chla, Chlb, Chlt, and PDW, with R² values ranging from 0.49 to 0.62 across two seasons. The different ANNs models demonstrated high predictive accuracy for NRCT and other measured traits, with R² values ranging from 0.62 to 0.90 in the training dataset and from 0.46 to 0.68 in the cross-validation dataset. Thus, our study shows that integrating high-throughput digital image tools with ANN models can efficiently and non-invasively assess the salt tolerance of a large number of wheat genotypes in breeding programs. Full article