Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground”

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 572 - 572

Published: Feb. 8, 2025

Leaf chlorophyll content (LCC) is a key indicator of crop growth condition. Real-time, non-destructive, rapid, and accurate LCC monitoring paramount importance for precision agriculture management. This study proposes an improved method based on multi-source data, combining the Sentinel-2A spectral response function (SRF) computer algorithms, to overcome limitations traditional methods. First, equivalent remote sensing reflectance was simulated by UAV hyperspectral images with ground experimental data. Then, using grey relational analysis (GRA) maximum information coefficient (MIC) algorithm, we explored complex relationship between vegetation indices (VIs) LCC, further selected feature variables. Meanwhile, utilized three (DSI, NDSI, RSI) identify sensitive band combinations analyzed original bands LCC. On this basis, nonlinear machine learning models (XGBoost, RFR, SVR) one multiple linear regression model (PLSR) construct inversion model, chose optimal generate spatial distribution maps maize at regional scale. The results indicate that there significant correlation VIs XGBoost, SVR outperforming PLSR model. Among them, XGBoost_MIC achieved best during tasseling stage (VT) growth. In R2 = 0.962 RMSE 5.590 mg/m2 in training set, 0.582 6.019 test set. For Sentinel-2A-simulated set had 0.923 8.097 mg/m2, while showed 0.837 3.250 which indicates improvement accuracy. scale, also yielded good (train 0.76, 0.88, 18.83 mg/m2). conclusion, proposed not only significantly improves accuracy methods but also, its outstanding versatility, can achieve precise different regions various types, demonstrating broad application prospects practical value agriculture.

Language: Английский

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Real-time monitoring of maize phenology using ground camera fusion information

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100850 - 100850

Published: Feb. 1, 2025

Language: Английский

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Functional Analysis of the ZmPR5 Gene Related to Resistance Against Fusarium verticillioides in Maize

Plants, Journal Year: 2025, Volume and Issue: 14(5), P. 737 - 737

Published: Feb. 28, 2025

In this study, the gene ZmPR5, associated with resistance to ear rot, was identified through transcriptome data analysis of maize inbred line J1259. The subsequently cloned and its function investigated. ZmPR5 comprises an open reading frame 525 base pairs, encoding a protein 175 amino acids. overexpressed in Arabidopsis ZmPR5EMS mutant maize, they were subjected q-PCR measurements antioxidant enzyme activities (POD, SOD, CAT, MDA), electrical conductivity, chlorophyll content. results indicate that expression is up-regulated upon infection by Fusarium verticillioides, significant differences observed POD, MDA, study found localized nucleus interacts Zm00001d020492 (WRKY53) Zm00001d042140 (glucA). Trypan blue staining revealed stained area significantly larger than B73. closely rot.

Language: Английский

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Maize yield estimation based on UAV multispectral monitoring of canopy LAI and WOFOST data assimilation

European Journal of Agronomy, Journal Year: 2025, Volume and Issue: 168, P. 127614 - 127614

Published: March 21, 2025

Language: Английский

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Enhancing Food Production Through Modern Agricultural Technology

Plant Cell & Environment, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

ABSTRACT Food security is fundamental to human capacity building and crucial for sustainable global development. As the population continues surge, demand food production increasingly strained, struggling keep pace with nutritional needs. This challenge exacerbated by climate change effects, including extreme weather events natural disasters, leading significant losses in both crop yield arable land. In this article, we delve into innovative strategies employed plant researchers enhance resilience productivity. These efforts have led development of new varieties that boast adaptability varying climatic conditions, improved resistance diseases herbicides, significantly increased yields. Alongside genetic advancements, article also highlights smart agricultural practices are pivotal augmenting include optimizing water resource management during irrigation integrating modern informational intelligent technologies farmland management. By synthesizing these technological methodological advances, proposes a comprehensive approach addressing pressing issues security. solutions not only aim meet immediate demands but foster long‐term sustainability practices.

Language: Английский

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Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape

Drones, Journal Year: 2024, Volume and Issue: 8(11), P. 642 - 642

Published: Nov. 5, 2024

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture field remote sensing. We explored the feasibility potential for predicting through utilization a UAV-based platform equipped with RGB multispectral cameras. Genetic algorithm–partial least square was employed evaluated effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial squares (PLSR), support vector (LS-SVM), back propagation neural network (BPNN), extreme (ELM), radial basis function (RBFNN), were developed compared. With multi-source data fusion by combining indices (color narrow-band VIs), robust models built. The performance using combination VIs (RBFNN: Rpre = 0.8143, RMSEP 171.9 kg/hm2) from sensors manifested better results than those only (BPNN: 0.7655, 188.3 camera. best found applying BPNN (Rpre 0.8114, 172.6 built optimal EWs ELM 0.8118, 170.9 VIs. Taken together, findings conclusively illustrate images non-invasive yield. This study also highlights that lightweight UAV dual-image-frame snapshot cameras holds promise as valuable tool high-throughput plant phenotyping advanced breeding programs within realm agriculture.

Language: Английский

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Chlorophyll Content Estimation of Ginkgo Seedlings Based on Deep Learning and Hyperspectral Imagery

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 2010 - 2010

Published: Nov. 14, 2024

Accurate estimation of chlorophyll content is essential for understanding the growth status and optimizing cultivation practices Ginkgo, a dominant multi-functional tree species in China. Traditional methods based on chemical analysis determining are labor-intensive time-consuming, making them unsuitable large-scale dynamic monitoring high-throughput phenotyping. To accurately quantify Ginkgo seedlings under different nitrogen levels, this study employed hyperspectral imaging camera to capture canopy images throughout their annual periods. Reflectance derived from pure leaf pixels was extracted construct set spectral parameters, including original reflectance, logarithmic first derivative along with index combinations. A one-dimensional convolutional neural network (1D-CNN) model then developed estimate content, its performance compared four common machine learning methods, Gaussian Process Regression (GPR), Partial Least Squares (PLSR), Support Vector (SVR), Random Forest (RF). The results demonstrated that 1D-CNN outperformed others spectra, achieving higher CV-R2 lower RMSE values (CV-R2 = 0.80, 3.4). Furthermore, incorporating combinations enhanced model’s performance, best 0.82, 3.3). These findings highlight potential strengthening estimations, providing strong technical support precise fertilization management seedlings.

Language: Английский

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EFFECTS OF HYPOTHERMIC STRESS APPLIED TO SEEDS BEFORE GERMINATION ON THE PARAMETERS OF THE PHOTOSYNTHETIC APPARATUS OF MAIZE PLANTS

Annals of the University of Craiova Series Biology Horticulture Food products processing technology Environmental engineering, Journal Year: 2024, Volume and Issue: 29(65)

Published: Nov. 26, 2024

In maize (Zea mays L.) plants grown from seeds pre-treated with negative temperature stress (NTS) of -4°C for 16 hours before germination, followed by growth in dark and light conditions, the content chlorophyll pigments (Chl) leaves, index (CCI) some gas exchange parameters were assessed. Combined application NTS illumination conditions to seedling showed that 6-day-old seedlings etiolated, contained carotenoids traces Chl a b. Whereas green light, decreased a, b carotenoids. also reduced CCI in1st, 2nd 3ed leaves 17 days. While 1st, control grew changed dynamically, showing maximum on 9th day then decreased. The influence affected causing decrease CO2 exchange, real absorption total respiration.

Language: Английский

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Rice Yield Estimation Based on Cumulative Time Series Vegetation Indices of UAV MS and RGB Images

Agronomy, Journal Year: 2024, Volume and Issue: 14(12), P. 2956 - 2956

Published: Dec. 12, 2024

Timely and accurate yield estimation is essential for effective crop management the grain trade. Remote sensing has emerged as a valuable tool monitoring rice yields; however, many studies concentrate on single period or simply aggregate multiple periods, neglecting complexities underlying formation. The study enhances by integrating cumulative time series vegetation indices (VIs) from multispectral (MS) RGB (Red, Green, Blue) sensors to identify optimal combinations of growth periods. We utilized two unmanned aerial vehicle capture spectral information canopies through MS sensors. By analyzing correlations between different yields, MS-VIs RGB-VIs each were identified. Following this, relationship MS-VIs, RGB-VIs, yields was further examined. results demonstrate that booting stage crucial influencing yield, with VIs exhibiting increased correlation peaking during this before declining. For sensor, model, based tillering panicle initiation stage, achieves accuracy (R2 = 0.722, RRMSE 0.555). grain-filling highest 0.727, 0.526). In comparison, multi-sensor which combines stages, R2 0.759 0.513. These findings suggest integration enhance prediction accuracy, providing comprehensive approach estimating dynamics supporting precision agriculture informed management.

Language: Английский

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Generation mean analysis in quality protein maize (<i>Zea mays</i> L.) for yield and quality attributes

Journal of Applied and Natural Science, Journal Year: 2024, Volume and Issue: 16(4), P. 1758 - 1770

Published: Dec. 20, 2024

Maize (Zea mays L.), the world’s most significant cereal crop, provides a pivotal roles for supply of food humans and forage livestock. The present study aimed to perform Generation mean analysis two quality protein maize (QPM) L.) crosses [(CML149 x CML330) (CML143 CML193)] in order determine genetic effects along with nature gene action controlling morphological biochemical traits underlying inheritance. All four components scaling testing revealed differences parameter model, indicating importance additive, dominance epistatic modes inheritance physiological, biochemical, grain yield its attributing traits. Dominance variance showed more than additive presence duplicate form non-allelic interaction was prevalent all characters studied except days 50% silking CML149 × CML330 ([h] = 2.064, [l] 1.536) membrane stability index CML143 CML193 4.055, 17.362) which complementary action. Characters genes, per plant 1545.776, -2126.616) height 113.336, -104.376) strong role epistasis noted aforementioned characters. Selection could be rewarding consecutive populations, followed by bi-parental mating design improve these

Language: Английский

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