A systematic review on precision agriculture applied to sunflowers, the role of hyperspectral imaging

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 222, С. 109097 - 109097

Опубликована: Май 30, 2024

Sunflower is an annual species of the Asteraceae family, and it occupies a relevant position in world market business as one most important oilseed crops. Given current geopolitical situation climate change, agri-food supply chain sunflower crisis. In this context, precision agriculture, especially remote sensing, can address demands for more production greater sustainability. The aim present systematic review to evaluate available scientific literature on agriculture applied crop, specifically use hyperspectral data calculate vegetation indices or create crop growth models. follows specific guidelines well-described protocol. A total 104 studies were included review, starting from raw search different sources (Scopus, Web Science, Springer Link, Science Direct) following with application inclusion criteria. Results focused main topics: management (i.e., zones, yield prediction, correlations), monitoring identify stages parameters), weed management, industrial applications. role sensors has been thoroughly investigated help choose ideal wavelengths related indices. Future research should prioritise water stress time-saving evaluation new hybrids,

Язык: Английский

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Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: A systematic literature review

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 227, С. 109565 - 109565

Опубликована: Окт. 24, 2024

Язык: Английский

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Estimating leaf and canopy nitrogen contents in major field crops across the growing season from hyperspectral images using nonparametric regression

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 233, С. 110147 - 110147

Опубликована: Фев. 24, 2025

Язык: Английский

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Characterizing foliar phenolic compounds and their absorption features in temperate forests using leaf spectroscopy

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2024, Номер 212, С. 338 - 356

Опубликована: Май 16, 2024

Phenolic compounds constitute an essential part of the plant's secondary metabolites and play a crucial role in ecosystem functioning, including nutrient cycling plant defence against biotic abiotic stressors. Quantifying phenolic across global biomes is important for monitoring biological diversity processes. However, our understanding foliar remains limited, particularly regarding how they vary among temperate tree species whether their variation absorption features can be assessed using spectroscopy at leaf level. In this study, we examined relationships between spectral properties fresh leaves from two ecologically (i.e., total phenol tannin). We sampled four dominant English oak, European beech, Norway spruce, Scots pine) forest sites. Continuum removal was applied to spectra enhance assessment subtle that correlate with content. Total tannin concentrations were estimated by comparing performance empirical methods, namely partial least squares regression (PLSR) Gaussian processes (GPR). Our results showed large range (p < 0.05). Spectral analysis revealed persistent distinct near 1666 nm spruce whereas pine exhibited weaker feature 1653 nm. Regression both PLSR GPR accurately species, informative bands predicting these traits well-corresponded models utilised. also suggested overall more predicted than regardless employed methods. The most accurate estimations achieved continuum-removed SWIR (total phenol: R2=0.79, NRMSE=9.95%; tannin: R2=0.59, NRMSE=14.53%). Testing established individual or types variability prediction performances, specific demonstrating lower accuracy (R2=0.47–0.69 0.34–0.54 tannin, respectively) compared cross-species model. study extends common demonstrates potential generalised model predict forests. These findings provide foundation mapping forests canopy level airborne spaceborne imaging spectroscopy.

Язык: Английский

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Transfer learning for enhancing the generality of leaf spectroscopic models in estimating crop foliar nutrients across growth stages

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 139, С. 104481 - 104481

Опубликована: Март 17, 2025

Язык: Английский

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Crop Canopy Nitrogen Estimation from Mixed Pixels in Agricultural Lands Using Imaging Spectroscopy

Remote Sensing, Год журнала: 2024, Номер 16(8), С. 1382 - 1382

Опубликована: Апрель 13, 2024

Accurate retrieval of canopy nutrient content has been made possible using visible-to-shortwave infrared (VSWIR) imaging spectroscopy. While this strategy often tested on closed green plant canopies, little is known about how estimates perform when applied to pixels not dominated by photosynthetic vegetation (PV). In such cases, contributions bare soil (BS) and non-photosynthetic (NPV), may significantly nonlinearly reduce the spectral features relied upon for retrieval. We attempted define loss prediction accuracy under reduced PV fractional cover levels. To do so, we utilized VSWIR spectroscopy data from Global Airborne Observatory (GAO) a large collection lab-calibrated field samples nitrogen (N) collected across numerous crop species grown in several farming regions United States. Fractional values PV, NPV, BS were estimated GAO Automated Monte Carlo Unmixing algorithm (AutoMCU). Errors partial least squares N model examined relation unmixed components. found that most important factor regression (PLSR) fraction (PV) cover, with greater than 60% performing at optimal level, where coefficient determination (R2) peaks 0.66 fractions more less 20%. Our findings guide future spaceborne missions as agricultural cropland monitoring.

Язык: Английский

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Canopy-Level Spectral Variation and Classification of Diverse Crop Species with Fine Spatial Resolution Imaging Spectroscopy

Remote Sensing, Год журнала: 2024, Номер 16(8), С. 1447 - 1447

Опубликована: Апрель 19, 2024

With the increasing availability and volume of remote sensing data, imaging spectroscopy is an expanding tool for agricultural studies. One fundamental applications in research crop mapping classification. Previous studies have mostly focused at local to regional scales, classifications were usually performed a limited number types. Leveraging fine spatial resolution (60 cm) data collected by Global Airborne Observatory (GAO), we investigated canopy-level spectral variations 16 species from different regions U.S. Inter-specific differences quantified through principal component analysis (PCA) spectra their Euclidean distances PC space. We also classified using support vector machines (SVM), demonstrating high classification accuracy with test kappa 0.97. A separate independent dataset returned (kappa = 0.95). Classification full reflectance (320 bands) selected optimal wavebands literature resulted similar accuracies. demonstrated that involving diverse achievable, encourage further testing based on moderate spectrometer data.

Язык: Английский

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Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues

Precision Agriculture, Год журнала: 2024, Номер 25(5), С. 2165 - 2197

Опубликована: Июнь 27, 2024

Abstract Purpose Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased carbon sequestration, fertilizer needs. Crop residue traits (i.e., lignin, holocellulose, non-structural carbohydrates) nitrogen concentrations largely mediate decomposition rates amount of plant-available accessible to cash determine residence time. Non-destructive approaches quantify these important possible using spectroscopy. Methods The objective this study was evaluate the efficacy spectroscopy instruments crop biochemical in cover agriculture systems partial least squares regression models a combination (1) band equivalent reflectance (BER) PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging sensor derived from laboratory collected Analytical Spectral Devices (ASD) spectra ( n = 296) 11 species three species, (2) spaceborne PRISMA imagery coincided with destructive collections spring 2022 65). range constrained 1200 2400 nm reduce likelihood confounding relationships wavelengths sensitive plant pigments or those related canopy structure for both analytical approaches. Results Models BER all demonstrated high accuracies low errors estimation (adj. R 2 0.86 − 0.98; RMSE 0.24 4.25%) results indicate single model may be used given trait across species. successfully estimated 0.65 0.75; 2.71 4.16%). We found moderate between concentration 0.52; 0.25%), which is partly senesced residues (0.38–1.85%). were also influenced by atmospheric absorption, variability surface moisture content, some presence green vegetation. Conclusion As data become more widely available upcoming missions, estimates could regularly generated integrated into decision support tools calculate associated credits inform precision field management, as well enable measurement, monitoring, reporting, verification net benefits practice adoption an emerging marketplace.

Язык: Английский

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Fresh Leaf Spectroscopy to Estimate the Crop Nutrient Status of Potato (Solanum tuberosum L.)

Potato Research, Год журнала: 2024, Номер unknown

Опубликована: Июль 24, 2024

Язык: Английский

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Optimizing UAV Hyperspectral Imaging for Predictive Analysis of Nutrient Concentrations, Biomass Growth, and Yield Prediction of Potatoes

American Journal of Potato Research, Год журнала: 2024, Номер unknown

Опубликована: Сен. 10, 2024

Язык: Английский

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