The normalized difference vegetative index (NDVI) has been correlated with physiological plant parameters and used to evaluate plant growth. There is little information about the use of this technique to detect soybean nutrient deficiencies. The objective of this work was to determine the ability of the NDVI sensor to detect P and K deficiencies, and grain yield reduction, in soybean. During 2010 and 2011, NDVI measurements were made on a soybean field trial site known to exhibit yield responses to both P and K nutrition. Four replicates of 8 levels each of P and K nutrition were evaluated. The NDVI measurements were made with an active proximal sensor held parallel to the soil surface every seven days after V2, and until R2. At each measurement a mean NDVI value was found for each plot. Phosphorus deficiency was detected with the first NDVI measurement. Potassium deficiency was first detected just after V4. Differences in NDVI values due to P or K nutrition increased with continued crop development. There were significant R1 leaf composition and grain yield responses to improved P or K nutrition. The active proximal sensor was able to detect soybean growth differences due to P or K deficiencies in soybean.

Southern Appalachian wetlands have yet to be studied in terms of net primary production (NPP), thus few studies have been conducted to examine what environmental factors have relationships with NPP. To that end, this research investigates several facets of southern Appalachian wetland production. The research was divided into three studies. The first study was conducted to answer the question of what environmental factors have relationships with NPP. It appears that stream discharge and annual precipitation had the strongest relationships with NPP (r = 0.91, p <0.05 and r = 0.81, p <0.05, respectively), yet both factors showed multicolinearity (r = 0.97, p <0.05). The strong relationships between hydrologic factors and NPP is similar to montane wetlands in the western United States. The second study was conducted to examine the relationship between water chemistry and NPP. Calcium (Ca), Magnesium (Mg), and pH were examined in order to determine if any of the aforementioned factors had a relationship with NPP. Neither Ca (r = -0.34, p = 0.0835) nor Mg (r =-0.38, p = 0.0535) had strong relationships with NPP, though pH (r = -0.66, p <0.05) had a strong negative relationship with NPP. The acidity of the stream water, driven by the acid rain in the southern Appalachians, creates enhanced conditions for wetland plants to grow. The third study was conducted to establish which vegetation index was best for estimating NPP from proximally and remotely sensed data. The findings suggest that VARIRed Edge was best for examining NPP at the in situ level, NDVI was best for examining NPP at the airborne level, and the DVI was the best for examining NPP at the satellite level. NPP in southern Appalachian wetlands is driven by the chemistry, specifically the pH, of stream discharge and annual precipitation and can be monitored by NDVI using NAIP data or DVI using Landsat data. The examination of NPP in southern Appalachians in response to environmental factors and water chemistry along with the examination of vegetation indices at three levels of platforms will help to monitor and manage these rare and unique ecosystems in the future.

Com a intensificação e expansão da produção agrícola brasileira, seguidas da crescente preocupação ambiental, surge a necessidade da adoção de novas tecnologias para o monitoramento e avaliação em larga escala dos recursos naturais, em especial o solo. Nesse sentido a espectroscopia de reflectância, aliada as bibliotecas espectrais, atende estas expectativas por ser uma técnica eficiente, rápida, barata e não poluente, pois não usa reagentes e nem gera resíduos, porém sua aplicação nos solos tropicais brasileiros ainda é incipientes e carece de mais estudos. Sendo assim, o objetivo principal do trabalho foi comparar as informações espectrais obtidas por espectroscopia de reflectância nas faixas do visível, infravermelho próximo e, principalmente, no infravermelho médio com análises convencionais de solo a fim entender as relações existentes entre o comportamento espectral e os atributos físicos, químicos e mineralógicos dos solos estudados, ainda quantificar e caracterizar a variabilidade desses atributos e dos solos com base no comportamento espectral. Para tanto, foram utilizadas 1288 amostras de solo correspondentes a diferentes horizontes de 458 perfis da região central brasileira (GO, MS, MG e SP), sobre as quais foram procedidas análise granulométrica e química, para fins de levantamento de solos, e mineralógica. Os espectros VisNIR foram adquiridos com o sensor FieldSpec Pro, enquanto que espectros Mid-IR com o sensor Nicolet 6700 Fourier Transform Infrared (FT-IR) e, após isso, os dados foram transformados com os seguintes préprocessamentos: remoção do espectro contínuo, absorbância e correção da linha base. As diferentes faixas espectrais também foram combinadas pela outer product analysis. A distribuição dos dados espectrais e a influência de cada atributo nos comportamentos espectrais dos solos foram avaliadas através de análises estatísticas multivariadas, tais como correlação multivariada, análise por componentes principais, seguidas das análises de agrupamento kmédias, para a avaliação entre amostras, e distância taxonômica, para avaliação entre perfis. A predição dos atributos dos solos com base nos dados espectrais foi determinada pelos algoritmos de regressão multivariada: mínimos quadrados parciais (PLSR), árvores de regressão ampliadas (BT) e máquina de vetor suporte (SVM); sendo que a qualidade das predições foi definida pelos R2, RMSE, RPD e RPIQ. O comportamento espectral das principais classes de solos tropicais brasileiros, em ambas as faixas espectrais, foi apresentado juntamente com as principais feições de absorção diagnosticadas (minerais 2:1, caulinita, óxidos de Fe e Al), assim como a contribuição de cada atributo do solo na variação dos espectros. Foi possível estabelecer a formação de grupos de solo com base no grau de intemperismo das amostras, enquanto que os perfis das principais classes de solos puderam ser discriminados com base na variação do comportamento espectral em profundidade. Grande parte dos atributos estudados pode ser quantificada nas duas faixas espectrais pelo menos com um nível de qualidade razoável (RPD > 1,40), sendo que o principal algoritmo de predição foi o SVM com os espectros em valores de absorbância. A espectroscopia de reflectância, principalmente na faixa Mid-IR, apresenta-se como uma tecnologia eficaz de alta potencialidade e aplicabilidade nas análises qualitativa e quantitativas dos solos, principalmente para fins de levantamento e mapeamento. / Considering the intensification and expansion of Brazilian agricultural production followed by the growing environmental concern, there is the necessity of adopting new technologies to monitor on a large scale the natural resources, in particular, the soil. In this sense, reflectance spectroscopy, integrated with spectral libraries, meets that expectation as an alternative for proximal soil sensing because its more efficient than other techniques being cheaper and faster than conventional analyses, besides, it doesnt produce waste. However, its application on Brazilian tropical soils is still incipient and needs more studies. The main aim of this research was to compare spectral information acquired by reflectance spectroscopy in the visible, near infrared and, mainly, in the mid infrared ranges with conventional soil analyses in order to understand the relationships among spectral behavior and physical, chemical, and mineralogical attributes of the studied soils, also, to quantify and characterize the variability of these attributes and soils based on their spectral behavior. For that, a dataset with 1288 soil samples referring to different horizons from 458 soil profiles from the central region of Brazil (States of Goiás, Mato Grosso do Sul, Minas Gerais e São Paulo) was used, and convetional analyses (granulometric, chemical and mineralogical) for soil survey were applied over them. VisNIR reflectance data were acquired by the FiledSpec Pro sensor whereas Mid-IR spectral data were acquired by the Nicolet 6700 (FT-IR) sensor and, after that, both data were transformed by the following pre-processing: continuum removal, absorbance and baseline correction. The different spectral ranges were also combined by outer product analysis. The data distribution and the influence of each attribute on soil spectral behavior were evaluated by multivariate statistical analyses, such as, multivariate correlations, principal component analysis followed by k-means clustering, for assessment among soil samples, and by taxonomic distance, for assessment among soil profiles. Predictions of soil attributes based on spectral data were modeled by following multivariate regression algorithms: partial least square regression (PLSR), boosted regression trees (BT) and support vector machine (SVM) and quality of modeling were evaluated by R2, RMSE, RPD and RPIQ. Spectral behavior of the main soil classes, in both ranges, were showed along with the absorption features of the major tropical minerals (2:1 minerals, kaolinite, Fe and Al oxides), as well as, the contribution of each soil attribute on spectra variations. Sample clustering based on different weathering levels of soils was possible regarding differences in absorbance intensities and features whereas profiles of the main soil classes could be discriminated based on variation of spectral behavior in depth. Much of the studied attributes could be predicted in both spectral range at least in a reasonable level of quality (RPD > 1,40), and SVM was considered the principal prediction algorithm as well as absorbance transformation as the major pre-processing. Reflectance spectroscopy, especially in the Mid-IR range, shows up as a high potential technique for qualitative and quantitative analysis of Brazilian soils, in particular for soil survey or mapping.

Com a intensificação e expansão da produção agrícola brasileira, seguidas da crescente preocupação ambiental, surge a necessidade da adoção de novas tecnologias para o monitoramento e avaliação em larga escala dos recursos naturais, em especial o solo. Nesse sentido a espectroscopia de reflectância, aliada as bibliotecas espectrais, atende estas expectativas por ser uma técnica eficiente, rápida, barata e não poluente, pois não usa reagentes e nem gera resíduos, porém sua aplicação nos solos tropicais brasileiros ainda é incipientes e carece de mais estudos. Sendo assim, o objetivo principal do trabalho foi comparar as informações espectrais obtidas por espectroscopia de reflectância nas faixas do visível, infravermelho próximo e, principalmente, no infravermelho médio com análises convencionais de solo a fim entender as relações existentes entre o comportamento espectral e os atributos físicos, químicos e mineralógicos dos solos estudados, ainda quantificar e caracterizar a variabilidade desses atributos e dos solos com base no comportamento espectral. Para tanto, foram utilizadas 1288 amostras de solo correspondentes a diferentes horizontes de 458 perfis da região central brasileira (GO, MS, MG e SP), sobre as quais foram procedidas análise granulométrica e química, para fins de levantamento de solos, e mineralógica. Os espectros VisNIR foram adquiridos com o sensor FieldSpec Pro, enquanto que espectros Mid-IR com o sensor Nicolet 6700 Fourier Transform Infrared (FT-IR) e, após isso, os dados foram transformados com os seguintes préprocessamentos: remoção do espectro contínuo, absorbância e correção da linha base. As diferentes faixas espectrais também foram combinadas pela outer product analysis. A distribuição dos dados espectrais e a influência de cada atributo nos comportamentos espectrais dos solos foram avaliadas através de análises estatísticas multivariadas, tais como correlação multivariada, análise por componentes principais, seguidas das análises de agrupamento kmédias, para a avaliação entre amostras, e distância taxonômica, para avaliação entre perfis. A predição dos atributos dos solos com base nos dados espectrais foi determinada pelos algoritmos de regressão multivariada: mínimos quadrados parciais (PLSR), árvores de regressão ampliadas (BT) e máquina de vetor suporte (SVM); sendo que a qualidade das predições foi definida pelos R2, RMSE, RPD e RPIQ. O comportamento espectral das principais classes de solos tropicais brasileiros, em ambas as faixas espectrais, foi apresentado juntamente com as principais feições de absorção diagnosticadas (minerais 2:1, caulinita, óxidos de Fe e Al), assim como a contribuição de cada atributo do solo na variação dos espectros. Foi possível estabelecer a formação de grupos de solo com base no grau de intemperismo das amostras, enquanto que os perfis das principais classes de solos puderam ser discriminados com base na variação do comportamento espectral em profundidade. Grande parte dos atributos estudados pode ser quantificada nas duas faixas espectrais pelo menos com um nível de qualidade razoável (RPD > 1,40), sendo que o principal algoritmo de predição foi o SVM com os espectros em valores de absorbância. A espectroscopia de reflectância, principalmente na faixa Mid-IR, apresenta-se como uma tecnologia eficaz de alta potencialidade e aplicabilidade nas análises qualitativa e quantitativas dos solos, principalmente para fins de levantamento e mapeamento. / Considering the intensification and expansion of Brazilian agricultural production followed by the growing environmental concern, there is the necessity of adopting new technologies to monitor on a large scale the natural resources, in particular, the soil. In this sense, reflectance spectroscopy, integrated with spectral libraries, meets that expectation as an alternative for proximal soil sensing because its more efficient than other techniques being cheaper and faster than conventional analyses, besides, it doesnt produce waste. However, its application on Brazilian tropical soils is still incipient and needs more studies. The main aim of this research was to compare spectral information acquired by reflectance spectroscopy in the visible, near infrared and, mainly, in the mid infrared ranges with conventional soil analyses in order to understand the relationships among spectral behavior and physical, chemical, and mineralogical attributes of the studied soils, also, to quantify and characterize the variability of these attributes and soils based on their spectral behavior. For that, a dataset with 1288 soil samples referring to different horizons from 458 soil profiles from the central region of Brazil (States of Goiás, Mato Grosso do Sul, Minas Gerais e São Paulo) was used, and convetional analyses (granulometric, chemical and mineralogical) for soil survey were applied over them. VisNIR reflectance data were acquired by the FiledSpec Pro sensor whereas Mid-IR spectral data were acquired by the Nicolet 6700 (FT-IR) sensor and, after that, both data were transformed by the following pre-processing: continuum removal, absorbance and baseline correction. The different spectral ranges were also combined by outer product analysis. The data distribution and the influence of each attribute on soil spectral behavior were evaluated by multivariate statistical analyses, such as, multivariate correlations, principal component analysis followed by k-means clustering, for assessment among soil samples, and by taxonomic distance, for assessment among soil profiles. Predictions of soil attributes based on spectral data were modeled by following multivariate regression algorithms: partial least square regression (PLSR), boosted regression trees (BT) and support vector machine (SVM) and quality of modeling were evaluated by R2, RMSE, RPD and RPIQ. Spectral behavior of the main soil classes, in both ranges, were showed along with the absorption features of the major tropical minerals (2:1 minerals, kaolinite, Fe and Al oxides), as well as, the contribution of each soil attribute on spectra variations. Sample clustering based on different weathering levels of soils was possible regarding differences in absorbance intensities and features whereas profiles of the main soil classes could be discriminated based on variation of spectral behavior in depth. Much of the studied attributes could be predicted in both spectral range at least in a reasonable level of quality (RPD > 1,40), and SVM was considered the principal prediction algorithm as well as absorbance transformation as the major pre-processing. Reflectance spectroscopy, especially in the Mid-IR range, shows up as a high potential technique for qualitative and quantitative analysis of Brazilian soils, in particular for soil survey or mapping.

Soil salinization is of great concern in the irrigated arid and semi-arid western United States due to its threat to sustainable agricultural productivity and thus is closely monitored. A widely accepted and traditional standard method for estimating soil salinity is the electrical conductivity of the saturated paste extracts (ECe). However, this method underestimates salinity due to ion pair formation in high ionic strength solution. Numerous studies have recommended the use of an electromagnetic induction (EMI) sensing technique to monitor field-scale soil salinity due to rapidness and non-destructiveness of the sampling. However, because the EMI measurement (ECa) is related to a host of soil properties, calibrating ECa to salinity in a non-homogeneous setting is particularly challenging. The main objective of this study is to understand the dynamics and interacting factors controlling soil salinity using an EMI sensor. Specifically, a correction is made for the underestimation of soil salinity from saturated paste extracts, and a calibration model is developed that is capable of predicting salinity directly from ECa despite the non-homogeneity of potential perturbing factors. A comparison is made of salinity measurement methods based on soil saturated pastes with respect to specific soil management goals. Results show that ion pairing exists even in low ionic strength solution and by diluting the saturated paste extracts to conductivities ≤ 0.03 dS m -1 (ECed), ion pairing is minimized. An improved salinity estimate is obtained by computing total dissolved solids (TDS, in mM) from the ECed values, and then multiplying the TDS by the dilution factor. We also developed a calibration model using quantile regression, which makes no assumption about the distribution of the errors, and which is capable of predicting low range soil salinity (such as that in calcareous soils) from ECa depth-weighted measurements (ECH25ECe). A comparison of ECe, ECed, ECH25ECe, and direct measurement of EC in soil pastes (“ Bureau of Soils Cup ” method, ECcup) across six depths, three texture groups, and the combinations of EC method and depth or texture groups, supports the use of the ECH25ECe method to rapidly and reliably monitor salinity in calcareous soils of arid and semiarid regions.

<p>Nowadays, soybean is one the most consumed crops in the world. As the human population continuously increases, new phenotyping technology is needed to help plant scientists breed soybean that has high-yield, stress-tolerant, and disease-tolerant traits. Hyperspectral imaging (HSI) is one of the most commonly used technologies for phenotyping. The current HSI techniques include HSI tower and remote sensing on an unmanned aerial vehicle (UAV) or satellite. There are several noise sources the current HSI technologies suffer from such as changes in lighting conditions, leaf angle, and other environmental factors. To reduce the noise on HS images, a new portable, leaf-level, high-resolution HSI device was developed for corn leaves in 2018 called LeafSpec. Due to the previous design requiring a sliding action along the leaf which could damage the leaf if used on a soybean leaf, a new design of the LeafSpec was built to meet the requirements of scanning soybean leaves. The new LeafSpec device protects the leaf between two sheets of glass, and the scanning action is automated by using motors and servos. After the HS images have been collected, the current modeling method for HS images starts by averaging all the plant pixels to one spectrum which causes a loss of information because of the non-uniformity of the leaf. When comparing the two modeling methods, one uses the mean normalized difference vegetation index (NDVI) and the other uses the NDVI heatmap of the entire leaf to predict the nitrogen content of soybean plants. The model that uses NDVI heatmap shows a significant increase in prediction accuracy with an R2 increase from 0.805 to 0.871. Therefore, it can be concluded that the changes occurring within the leaf can be used to train a better prediction model. </p> <p>Although the LeafSpec device can provide high-resolution leaf-level HS images to the researcher for the first time, it suffers from two major drawbacks: intensive labor needed to gather the image data and slow throughput. A new idea is proposed to use a UAV that carries a 6 degree of freedom (DOF) robotic arm with a LeafSpec device as an end-effect to automatically gather soybean leaf HS images. A new UAV is designed and built to carry the large payload weight of the robotic arm and LeafSpec.</p>

En Viticulture de Précision, l’imagerie multi-spectrale est principalement utilisée pour des dispositifs de télédétection. Ce manuscrit s’intéresse à son utilisation en proxidétection, pour la caractérisation du feuillage. Il présente un dispositif expérimental terrestre mobile composé d’un GPS, d’une caméra multi-spectrale acquérant des images visible et proche infrarouge, et d’un Greenseeker RT-100 mesurant l’indice Normalized Difference Vegetation Index (NDVI). Ce système observe le feuillage de la vigne dans le plan de palissage, en lumière naturelle. La parcelle étudiée comporte trois cépages (Pinot Noir, Chardonnay et Meunier) plantés en carré latin. En 2013, six jeux de données ont été acquis à différents stades phénologiques.Pour accéder aux propriétés spectrales de la végétation, il est nécessaire de calibrer les images en réflectance. Cela requiert l’utilisation d’une mire de MacBeth comme référence radiométrique. Lorsque la mire est cachée par les feuilles, les paramètres de calibration sont estimés par une interpolation linéaire en fonction des images les plus proches sur lesquelles la mire est visible. La cohérence de la méthode d’estimation employée est vérifiée par une validation croisée (LOOCV).La comparaison du NDVI fournie par le Greenseeker avec celui déterminé via les images corrigées permet de valider les données générées par le dispositif. La polyvalence du système est évaluée via les images où plusieurs indices de végétation sont déterminés. Ils permettant des suivis de croissance de la végétation originaux offrant des potentialités de phénotypage ou une caractérisation de l’état sanitaire de la végétation illustrant la polyvalence et le gain en précision de cette technique. / Mutispectral imaging systems are widely used in remote sensing for Precision Viticulture. In this work, this technique was applied in the proximal sensing context to characterize vine foliage. A mobile terrestrial experimental system is presented, composed of a GPS receiver, a multi-spectral camera acquiring visible and near infrared images, and a Greenseeker RT-100 which measures the Normalized Difference Vegetative Index (NDVI). This optical system observes vine foliage in the trellis plan, in natural sunlight. The experimental field is planted with Chardonnay, Pinot Noir and Meunier cultivars in a latin squared pattern. In 2013, six datasets were acquired at various phenological stages.Spectral properties of the vegetation are accessible on images when they are calibrated in reflectance. This step requires the use of a MacBeth colorchart as a radiometric reference. When the chart is hidden by leaves, the calibration parameters are estimated by simple linear interpolation using the results from resembling images, which have a visible chart. The performance of this method is verified with a cross-validation technique (LOOCV).To validate the data provided by the experimental system, the NDVI given by the Greenseeker was compared to those computed from the calibrated images. The assessment of the versatility of the system is done with the images where several indices were determined. It allows an innovative follow-up of the vegetative growth, and offering phenotyping applications. Moreover, the characterization of the sanitary state of the foliage prove that this technique is versatile and accurate.

<p dir="ltr">Among the major row crops in the United States, corn and soybeans stand out due to their high nutritional value and economic importance. Achieving optimal yields is restrained by the challenge of fertilizer management. Many fields experience yield losses due to insufficient mineral nutrients like nitrogen (N), while excessive fertilization raises costs and environmental risks. The critical issue is the accurate determination of fertilizer quantity and timing, underscoring the need for precise, early-stage diagnostics. Emerging high-throughput plant phenotyping techniques, notably hyperspectral imaging (HSI), have been increasingly utilized to identify plant’s responses to abiotic or biotic stresses. Varieties of HSI systems have been developed, such as airborne imaging systems and indoor imaging stations. However, most of the current HSI systems’ signal quality is often compromised by various environmental factors. To address the issue, a handheld hyperspectral imager known as LeafSpec was recently developed at Purdue University and represents a breakthrough with its ability to scan corn or soybean leaves at exceptional spatial and spectral resolutions, improving plant phenotyping quality at reduced costs. Most of the current HSI data processing methods focus on spectral features but rarely consider spatially distributed information. Thus, the objective of this work was to develop a methodology utilizing spatial-spectral features for accurate and reliable diagnostics of crop N nutrient stress. The key innovations include the designing of spatial-spectral features based on the leaf venation structures and the feature mining method for predicting the plant nitrogen condition. First, a novel analysis method called the Natural Leaf Coordinate System (NLCS) was developed to reallocate leaf pixels and innovate the nutrient stress analysis using pixels’ relative locations to the venation structure. A new nitrogen prediction index for soybean plants called NLCS-N was developed, outperforming the conventional averaged vegetation index (Avg. NDVI) in distinguishing healthy plants from nitrogen-stressed plants with higher t-test p-values and predicting the plant nitrogen concentration (PNC) with higher R-squared values. In one of the test cases, the p-values and R-squared values were improved, respectively, from 2.1×10^-3 to 6.92×10^-12 and from 0.314 to 0.565 by Avg. NDVI and NLCS-N. Second, a corn leaf venation segmentation algorithm was developed to separate the venation structure from a corn leaf LeafSpec image, which was further used to generate 3930 spatial-spectral (S-S) features. While the S-S features could be the input variable to build a PNC prediction model, a feature selection mechanism was developed to improve the models’ accuracy in terms of reduced cross-validation errors. In one of the test cases, the cross-validation root mean squared errors were reduced compared with the leaf mean spectra from 0.273 to 0.127 using the selected features. Third, several novel spatial-spectral indexes for corn leaves were developed based on the color distributions at the venation level. The top-performing indexes were selected through a ranking system based on Cohen’s d values and the R-squared values, resulting in a best-performing S-S N prediction index with 0.861 R-squared values for predicting the corn PNC in a field assay. The discussion sections provided insights into how a robust PNC prediction index could be developed and related to plant science. The methodologies outlined offer a framework for broader applications in spatial-spectral analysis using leaf-level hyperspectral imagery, serving as a guide for scientists and researchers in customizing their future studies within this field.</p>