scholarly journals Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation

2021 ◽  
Vol 13 (4) ◽  
pp. 598
Author(s):  
Daniel O. Wasonga ◽  
Afrane Yaw ◽  
Jouko Kleemola ◽  
Laura Alakukku ◽  
Pirjo S.A. Mäkelä
Keyword(s):  
Water Deficit ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
High Energy ◽  
Plant Performance ◽  
Estimation Accuracy ◽  
Management Decisions ◽  
Lower Estimation ◽  
Low K

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers.

scholarly journals Application of Phenotyping Methods in Detection of Drought and Salinity Stress in Basil (Ocimum basilicum L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Boris Lazarević ◽  
Zlatko Šatović ◽  
Ana Nimac ◽  
Monika Vidak ◽  
Jerko Gunjača ◽  
...  
Keyword(s):  
Drought Stress ◽  
Salinity Stress ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Ocimum Basilicum ◽  
Phenotypic Traits ◽  
Severe Drought ◽  
Moderate Salinity ◽  
Automated Phenotyping

Basil is one of the most widespread aromatic and medicinal plants, which is often grown in drought- and salinity-prone regions. Often co-occurrence of drought and salinity stresses in agroecosystems and similarities of symptoms which they cause on plants complicates the differentiation among them. Development of automated phenotyping techniques with integrative and simultaneous quantification of multiple morphological and physiological traits enables early detection and quantification of different stresses on a whole plant basis. In this study, we have used different phenotyping techniques including chlorophyll fluorescence imaging, multispectral imaging, and 3D multispectral scanning, aiming to quantify changes in basil phenotypic traits under early and prolonged drought and salinity stress and to determine traits which could differentiate among drought and salinity stressed basil plants. Ocimum basilicum “Genovese” was grown in a growth chamber under well-watered control [45–50% volumetric water content (VWC)], moderate salinity stress (100 mM NaCl), severe salinity stress (200 mM NaCl), moderate drought stress (25–30% VWC), and severe drought stress (15–20% VWC). Phenotypic traits were measured for 3 weeks in 7-day intervals. Automated phenotyping techniques were able to detect basil responses to early and prolonged salinity and drought stress. In addition, several phenotypic traits were able to differentiate among salinity and drought. At early stages, low anthocyanin index (ARI), chlorophyll index (CHI), and hue (HUE2D), and higher reflectance in red (RRed), reflectance in green (RGreen), and leaf inclination (LINC) indicated drought stress. At later stress stages, maximum fluorescence (Fm), HUE2D, normalized difference vegetation index (NDVI), and LINC contribute the most to the differentiation among drought and non-stressed as well as among drought and salinity stressed plants. ARI and electron transport rate (ETR) were best for differentiation of salinity stressed plants from non-stressed plants both at early and prolonged stress.

scholarly journals Estimation of Crop Water Deficit in Lower Bari Doab, Pakistan Using Reflection-Based Crop Coefficient

2020 ◽  
Vol 9 (3) ◽  
pp. 173
Author(s):  
Muhammad Asif Javed ◽  
Sajid Rashid Ahmad ◽  
Wakas Karim Awan ◽  
Bilal Ahmed Munir
Keyword(s):  
Water Deficit ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Coefficient ◽  
Maximum Temperature ◽  
Filter Method ◽  
Command Area ◽  
Landsat 8 ◽  
Crop Water ◽  
Crop Coefficients

There is a global realization in all governmental setups of the need to provoke the efficient appraisal of crop water budgeting in order to manage water resources efficiently. This study aims to use the satellite remote sensing techniques to determine the water deficit in the crop rich Lower Bari Doab Canal (LBDC) command area. Crop classification was performed using multi-temporal NDVI profiles of Landsat-8 imagery by distinguishing the crop cycles based on reflectance curves. The reflectance-based crop coefficients (Kc) were derived by linear regression between normalized difference vegetation index (NDVI) cycles of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MYD13Q1 products and Food and Agriculture Organization (FAO) defined crop coefficients. A MODIS 250 m NDVI product of the last 10 years (2004-2013) was used to identify the best performing crop cycle using Fourier filter method. The meteorological parameters including rainfall and temperature substantiated the reference evapotranspiration (ET0) calculated using the Hargreaves method. The difference of potential ET and actual ET, derived from the reflectance-based Kc calculated using reference NDVI and current NDVI, generates the water deficit. Results depict the strong correlation between ET, temperature and rainfall, as the regions having maximum temperature resulted in high ET and low rainfall and vice versa. The derived Kc values were observed to be accurate when compared with the crop calendar. Results revealed maximum water deficit at middle stage of the crops, which were observed to be particularly higher at the tail of the canal command. Moreover, results also depicted that kharif (summer) crops suffer higher deficit in comparison to rabi (winter) crops due to higher ET demand caused by higher temperature. Results of the research can be utilized for rational allocation of canal supplies and guiding farmers towards usage of alternate sources to avoid crop water stress.

scholarly journals Chlorophyll Fluorescence Kinetics May Be Useful to Identify Early Drought and Irrigation Effects on Photosynthetic Apparatus in Field-Grown Wheat

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1275
Author(s):  
Lenka Botyanszka ◽  
Marek Zivcak ◽  
Erik Chovancek ◽  
Oksana Sytar ◽  
Viliam Barek ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Water Deficit ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Photosynthetic Apparatus ◽  
Optical Methods ◽  
Accurate Information ◽  
Ojip Transient ◽  
Fluorescence Kinetics ◽  
Non Invasive

To assess the reliability and sensitivity of non-invasive optical methods to detect the early effects of water deficit in the field, we analyzed the time-series of non-invasive measurements obtained in a dry season in a representative collection of wheat genotypes grown in small-plot field trials, in non-irrigated and irrigated variants. Despite a progressive water deficit and significant yield loss, the measurements indicated very minor changes in chlorophyll content or canopy cover. This corresponded well to the insignificant differences in spectral reflectance normalized difference vegetation index (NDVI) values. On the other hand, we identified the significant and rapid response of fast fluorescence kinetics data following the onset of irrigation. Analysis of parameters showed the main effects of drought were associated with changes in the amplitude of the I–P phase of the OJIP transient, indicating changes at the level of photosystem I and beyond. Statistical analyses identified the integrative parameter performance index PItot as the most sensitive parameter, which well-reflects the differences in responses of the genotypes to water deficit. Our results suggest that focusing on photosynthetic functions detected by the rapid chlorophyll fluorescence records can provide more accurate information on the drought stress level, compared to the structural data obtained by absorbance or reflectance measurements.

scholarly journals ОЦЕНКА УРОВНЯ ОЗЕЛЕНЕНИЯ ГОРОДА БИРОБИДЖАНА С ПРИМЕНЕНИЕМ МУЛЬТИСПЕКТРАЛЬНЫХ ДАННЫХ

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Д.М. Фетисов ◽  
Д.В. Жучков ◽  
М.В. Горюхин
Keyword(s):  
Green Infrastructure ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Natural Vegetation ◽  
Medium Size ◽  
Functional Zones ◽  
Functional Areas ◽  
The City ◽  
And Storage

The urban greenness distribution between functional areas of a medium-size city Birobidzhan was assessed. To this end, normalized difference vegetation index (NDVI) values were calculated based on Sentinel 2 multispectral imaging. Birobidzhan is characterized by a large scatter of NDVI values (from –0.5 to +1). Areas with high levels of greenery are prevalent. They are found in different types of functional zones, but are specific mainly to natural recreational, agricultural, and individual build-up zones as well as to special areas. The spatial distribution of green infrastructure is highly contrast. The downtown part as well as the industrial and storage zones feature a combination of built-up areas with dense woody vegetation, which is often represented by fragments of preserved natural vegetation. In addition, a feature of the contrast is that low level of tree greenness is characteristic for the built-up districts of the city. Thus, in the city of Birobidzhan, ecological functions are largely performed by the natural vegetation present in the natural recreational zones on 70% of the city's area.

Generalization of Vegetation Indices for Monitoring the Terrestrial Biosphere

2021 ◽  
Author(s):  
Gustau Camps-Valls ◽  
Manuel Campos-Taberner ◽  
Alvaro Moreno-Martinez ◽  
Sophia Walther ◽  
Grégory Duveiller ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Observation Data ◽  
Terrestrial Biosphere ◽  
Time Resolved ◽  
Area Index ◽  
Nonlinear Generalization

<p>Vegetation indices are the most widely used tool in remote sensing and multispectral imaging applications. This paper introduces a nonlinear generalization of the broad family of vegetation indices based on spectral band differences and ratios. The presented indices exploit all higher-order relations of the involved spectral channels, are easy to derive and use, and give some insight on problem complexity. The framework is illustrated to generalize the widely adopted Normalized Difference Vegetation Index (NDVI). Its nonlinear generalization named, kernel NDVI (kNDVI), largely improves performance over NDVI and the recent NIRv in monitoring key vegetation parameters, showing much higher correlation with independent products, such as the MODIS leaf area index (LAI), flux tower gross primary productivity (GPP), and GOME-2 sun-induced fluorescence. The family of indices constitutes a valuable choice for many applications that require spatially explicit and time-resolved analysis of Earth observation data.</p><p><span> Reference: <strong>"<span>A Unified Vegetation Index for Quantifying the Terrestrial Biosphere</span>"</strong>, </span><span>Gustau Camps-Valls, Manuel Campos-Taberner, Álvaro Moreno-Martı́nez, Sophia Walther, Grégory Duveiller, Alessandro Cescatti, Miguel Mahecha, Jordi Muñoz-Marı́, Francisco Javier Garcı́a-Haro, Luis Guanter, John Gamon, Martin Jung, Markus Reichstein, Steven W. Running. </span><em><span><span>Science Advances, in press</span></span><span>, </span> <span>2021</span> </em></p>

scholarly journals Simulating the Leaf Area Index of Rice from Multispectral Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3663
Author(s):  
Shenzhou Liu ◽  
Wenzhi Zeng ◽  
Lifeng Wu ◽  
Guoqing Lei ◽  
Haorui Chen ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Estimation Accuracy ◽  
Gradient Boosting ◽  
Accurate Estimation ◽  
Area Index ◽  
Red Edge ◽  
Extreme Gradient Boosting

Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

scholarly journals Multispectral Imaging with Raspberry Pi for Assessment of Plant Health Status

2019 ◽  
Vol 1 (2) ◽  
pp. 30
Author(s):  
Jan Lukas Bosse ◽  
Marcelinus A. S. Adhiwibawa ◽  
Tatas H.P. Brotosudarmo
Keyword(s):  
Health Status ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Chlorophyll Concentration ◽  
Plant Health ◽  
Magnesium Concentration ◽  
Raspberry Pi ◽  
Major Shortcoming ◽  
Highly Correlated

Non-destructive measurement of plant chlorophyll concentration using the normalized difference vegetation index (NDVI) has long been standard practice to determine the plant health status. This is, because the NDVI value is correlated with the chlorophyll concentration which in turn is highly correlated with other vital plant parameters such as nitrogen and magnesium concentration. Initially the NDVI values were obtained from satellite imagery and thus could only be used to assess the health status of bigger ecosystems like forests and crop fields. With the introduction of handheld chlorophyll meters like the Chlorophyll Meter SPAD-502 Plus made by Konica Minolta, the same principle could be used to determine the chlorophyll concentration of single leaves. However, these devices still have one major shortcoming: They can only measure the chlorophyll concentration on one single spot on the leaf at a time. But depending on the species the chlorophyll concentration tends to vary significantly over the leaf. To overcome this shortcoming, we developed our PlantAnalyzer which offers better spatial resolution of the NDVI values and hence the chlorophyll concentration. Its technical realization and precision shall be elaborated in the following article.

scholarly journals USE OF A MULTISPECTRAL UAV PHOTOGRAMMETRY FOR DETECTION AND TRACKING OF FOREST DISTURBANCE DYNAMICS

2016 ◽  
Vol XLI-B8 ◽  
pp. 711-718 ◽  
Author(s):  
R. Minařík ◽  
J. Langhammer
Keyword(s):  
Bark Beetle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Forest Disturbance ◽  
Methodological Approach ◽  
Detection And Tracking ◽  
Red Edge ◽  
Tree Stress ◽  
Simple Ratio

This study presents a new methodological approach for assessment of spatial and qualitative aspects of forest disturbance based on the use of multispectral imaging camera with the UAV photogrammetry. We have used the miniaturized multispectral sensor Tetracam Micro Multiple Camera Array (μ-MCA) Snap 6 with the multirotor imaging platform to get multispectral imagery with high spatial resolution. The study area is located in the Sumava Mountains, Central Europe, heavily affected by windstorms, followed by extensive and repeated bark beetle (Ips typographus [L.]) outbreaks in the past 20 years. After two decades, there is apparent continuous spread of forest disturbance as well as rapid regeneration of forest vegetation, related with changes in species and their diversity. For testing of suggested methodology, we have launched imaging campaign in experimental site under various stages of forest disturbance and regeneration. The imagery of high spatial and spectral resolution enabled to analyse the inner structure and dynamics of the processes. The most informative bands for tree stress detection caused by bark beetle infestation are band 2 (650nm) and band 3 (700nm), followed by band 4 (800 nm) from the, red-edge and NIR part of the spectrum. We have identified only three indices, which seems to be able to correctly detect different forest disturbance categories in the complex conditions of mixture of categories. These are Normalized Difference Vegetation Index (NDVI), Simple 800/650 Ratio Pigment specific simple ratio B1 and Red-edge Index.

scholarly journals A Multispectral Camera Development: From the Prototype Assembly until Its Use in a UAV System

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6129
Author(s):  
Alejandro Morales ◽  
Raul Guerra ◽  
Pablo Horstrand ◽  
Maria Diaz ◽  
Adan Jimenez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Ease Of Use ◽  
Oxide Semiconductor ◽  
Forensic Sciences ◽  
Capture Process ◽  
Cmos Sensor ◽  
Multispectral Camera

Multispectral imaging (MI) techniques are being used very often to identify different properties of nature in several domains, going from precision agriculture to environmental studies, not to mention quality inspection of pharmaceutical production, art restoration, biochemistry, forensic sciences or geology, just to name some. Different implementations are commercially available from the industry and yet there is quite an interest from the scientific community to spread its use to the majority of society by means of cost effectiveness and ease of use for solutions. These devices make the most sense when combined with unmanned aerial vehicles (UAVs), going a step further and alleviating repetitive routines which could be strenuous if traditional methods were adopted. In this work, a low cost and modular solution for a multispectral camera is presented, based on the use of a single panchromatic complementary metal oxide semiconductor (CMOS) sensor combined with a rotating wheel of interchangeable band pass optic filters. The system is compatible with open source hardware permitting one to capture, process, store and/or transmit data if needed. In addition, a calibration and characterization methodology has been developed for the camera, allowing not only for quantifying its performance, but also able to characterize other CMOS sensors in the market in order to select the one that best suits the budget and application. The process was experimentally validated by mounting the camera in a Dji Matrice 600 UAV to uncover vegetation indices in a reduced area of palm trees plantation. Results are presented for the normalized difference vegetation index (NDVI) showing a generated colored map with the captured information.

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