Estimation of Water Contents from Vegetation Using Hyperspectral Indices

Surase, Rupali R.; Kale, K. V.; Varpe, Amrsinh B.; Vibhute, Amol D.; Gite, Hanumant R.; Solankar, Mahesh M.; Gaikwad, Sandeep V.; Nalawade, Dhananjay B.

doi:10.1007/978-981-13-1906-8_26

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…In recent years, this technique has been effectively employed for various crops to estimate biophysical parameters, such as leaf area index [13,14], leaf and fruit pigment content [15][16][17][18][19][20][21], biomass [22] as well as detection of diseases and fungal infections [23][24][25][26]. Several studies have been reported on the spectral changes related to leaf water content [27,28], chlorophyll content [29,30] and macronutrient content, e.g., nitrogen [31,32] and potassium [33].…”

Section: Introductionmentioning

confidence: 99%

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

et al. 2021

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Background Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery (Apium graveolens L., cv. Neon), sugar beet (Beta vulgaris L., cv. Tapir) and strawberry (Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

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Section: Introductionmentioning

confidence: 99%

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

et al. 2021

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“…Some previous studies focused on the whole spectrum to predict leaf water content in maize [25] or grapevine [26][27][28]. Most of them relied on vegetation indices (i.e., a mathematical combination of the reflectance at two or more wavelengths) for maize [29], trees [30], wheat [31], millet [32], sorghum [32], cowpea [33], bean [33], sugar beet [33], and grapevine [34,35]. Recent works using hyperspectral measurements focused on finding the best wavelengths or the best combination of wavelengths related to water status [26][27][28][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Laroche-Pinel

Albughdadi²,

Duthoit³

et al. 2021

Remote Sensing

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The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

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“…In recent years, this technique has been effectively employed for various crops to estimate biophysical parameters, such as leaf area index [13,14], leaf and fruit pigment content [15][16][17][18], biomass [19] as well as detection of diseases and fungal infections [20][21][22][23]. Several studies have been reported on the spectral changes related to leaf water content [24,25], chlorophyll content [26] and macronutrient content, e.g., nitrogen [27,28] and potassium [29].…”

Section: Introductionmentioning

confidence: 99%

Identification of Plant Leaf Phosphorus Content at Different Growth Stages Based on Hyperspectral Reflectance

Siedliska¹,

Baranowski²,

Pastuszka-Woźniak³

et al. 2020

Preprint

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Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery (Apium graveolens L., cv. Neon), sugar beet (Beta vulgaris L., cv. Tapir) and strawberry (Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content. Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants. Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

show abstract

Estimation of Water Contents from Vegetation Using Hyperspectral Indices

Cited by 5 publications

References 7 publications

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance

Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Identification of Plant Leaf Phosphorus Content at Different Growth Stages Based on Hyperspectral Reflectance

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