Remote Sensing Technology—A New Dimension in Detection, Quantification and Tracking of Abiotic and Biotic Stresses

Chowhan, Papan; Chakraborty, Arka Pratim

doi:10.1007/978-3-030-95365-2_27

Cited by 4 publications

(1 citation statement)

References 115 publications

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“…Some studies [6][7][8] proved a relationship between the spectral reflectance in the range from 1500 to 2500 nm (short-wave infrared-SWIR) and nutritional status, including the content of potassium and phosphorus in leaves and the stress of various plants. Spectral reflectance can be useful in detecting nutrient deficiency only when one factor is involved, as a deficiency of two or more nutrients is very difficult to diagnose [9]. Deficiency evaluation for a nutrient can be based on vegetation indices, which are calculated using two or more spectral bands [3,10] or based on reflectance profiles for multiple bands [11,12].…”

Section: Introductionmentioning

confidence: 99%

Does Drone Data Allow the Assessment of Phosphorus and Potassium in Soil Based on Field Experiments with Winter Rye?

Mazur¹,

Gozdowski

Stępień

et al. 2023

Agronomy

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The evaluation of the nutrient content in the soil, such as potassium and phosphorus, is very important, especially in precision agriculture, where the fertilizer rate should be adjusted to field variability in terms of nutrient content. Soil chemical analysis is time-consuming and expensive, and dense soil sampling is not always possible. In recent years, remote sensing methods have been used to assess the within-field variability of soil and crop nutritional status. The main aim of this study was to evaluate the relationship between UAV-derived spectral reflectance for winter rye in a long-term experiment for different fertilization with phosphorus and potassium. The study was conducted in 2022 in two field experiments in which winter rye was cultivated in monoculture and with crop rotation. The experiments were located in central Poland in Skierniewice. Statistical analyses were performed using univariate and multivariate methods, e.g., analysis of correlation, regression, and principal component analysis (PCA). The effect of phosphorus and potassium fertilization on the UAV-derived spectral reflectance of winter rye was weak, weaker in comparison to the effect of nitrogen fertilization. The effect of phosphorus and potassium fertilization on spectral reflectance was stronger in the experiment with monoculture than in the experiment with crop rotation. On the basis of correlation coefficients and PCA, negative relationships were proven between available soil potassium and spectral reflectance in the range of blue, green, and red bands and positive with red edge and near-infrared bands. The first principal component (PC1) was very strongly correlated with almost all spectral bands, either positively or negatively. The correlation of potassium and phosphorus content was very weak with PC1 in the experiment with crop rotation, while in the experiment with rye monoculture, the correlation was slightly stronger, indicating a stronger effect of nutrient deficiency in monoculture.

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Section: Introductionmentioning

confidence: 99%

Does Drone Data Allow the Assessment of Phosphorus and Potassium in Soil Based on Field Experiments with Winter Rye?

Mazur¹,

Gozdowski

Stępień

et al. 2023

Agronomy

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Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

Adak,

DeSalvio,

Arik

et al. 2024

G3: Genes, Genomes, Genetics

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Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018–2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index’s strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models’ performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.

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Triangular Greenness Index Analysis for Monitoring Fungal Disease in Pine Trees: A UAV-based Approach

Polat,

Memduhoğlu,

Kaya

2024

Bartın Orman Fakültesi Dergisi

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The Triangular Greenness Index (TGI) is a vegetation index derived from high-resolution aerial images acquired using unmanned aerial vehicles (UAVs). It serves as a valuable tool for quantifying vegetation health and dynamics in the visible spectrum. The TGI combines key components, including red reflectance and green reflectance, extracted from UAV-based imagery. The red component represents chlorophyll absorption and photosynthetic activity, while the green component reflects vegetation density and canopy structure. By integrating these components, the TGI offers a comprehensive measure of photosynthetically active vegetation, utilizing UAVs as a data collection platform. This study highlight the importance of the TGI derived from UAV-based imagery in monitoring vegetation changes, assessing ecosystem responses, and tracking variations in land cover and biodiversity. Furthermore, the application of TGI analysis using UAV-based aerial imagery shows promise in accurately identifying and monitoring vegetation affected by fungal diseases. This integrated approach enables the detection of diseased trees based on distinct changes in greenness observed in their foliage. Because fungal diseases dry the plant and cause the green areas to disappear. The integration of UAV technology enhances the accuracy and efficiency of TGI calculation, contributing to effective management and conservation strategies in the context of fungal disease detection in vegetation. In this study, TGI was produced using UAV-based orthophoto and healthy and sick trees were determined. According to the accuracy analysis, producer accuracy for detecting green plants was 99.7% and user accuracy was 98.5%. Fungal disease could be detected with 98.5% producer accuracy and 96.5% user accuracy. The overall accuracy of the study was calculated as 98.6%.

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Remote Sensing Technology—A New Dimension in Detection, Quantification and Tracking of Abiotic and Biotic Stresses

Cited by 4 publications

References 115 publications

Does Drone Data Allow the Assessment of Phosphorus and Potassium in Soil Based on Field Experiments with Winter Rye?

Does Drone Data Allow the Assessment of Phosphorus and Potassium in Soil Based on Field Experiments with Winter Rye?

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

Triangular Greenness Index Analysis for Monitoring Fungal Disease in Pine Trees: A UAV-based Approach

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