Foliar feeding of boron influencing biochemical attributes and enzyme activity in dragon fruit (Selenicereus monacanthus)

Boron plays crucial role in metabolic processes during fruit ripening and in turn ensures better fruit quality. However limited studies have been conducted to assess the influence of boron on fruit quality of dragon fruit. In the present study, the efficacy of boron was investigated on red-fleshed dragon fruit (Selenicereus monacanthus). Four levels of boron (100 mgL-1, 200 mgL-1, 300 mgL-1 and 400 mgL-1) were applied on 7- and 14-day-old flower buds. The highest pollen germinability, seed weight, fruit weight (274.32 ± 36.72g), pulp content (70.80 ± 1.79%) and pulp firmness (2.74 ± 0.18 N) were recorded when B was applied@300 mg L-1 on 7-day old flower bud. The same treatment also manifested higher soluble solid contents (17.42 ± 0.62 °Brix), sugar content, total carbohydrate (15.92 ± 1.12%), protein (1.33±0.11%), ascorbic acid (112.66 ± 4.98 µg/g), betacyanin (32.86±2.52 µg/g), total phenol (95.26 ± 3.72 µg GAE/ 100g), total flavonoid (37.65 ±2.14 mg QE/100g) and anti-oxidative activity (27.71±2.14 mM Fe II/100g). Correlation studies elucidated significant positive influence of pollen germinability on fruit weight, pulp content and pulp firmness. The activities of α-amylase, invertase and sucrose synthase enzymes were significantly upregulated with the application of B 300 mg L-1 on 7-day old flower bud. On the other hand, the activities of cell wall degrading enzymes such as cellulase, polygalacturonase and pectin methyl esterase were reduced with increasing levels of boron. The principal component analysis (PCA) illustrated the maximal proximity of most of the quality attributes with B 300 mgL-1, applied at 7-day old flower bud stage, thus exemplifying it as the best treatment

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Not AvailableA field experiment with wheat was conducted with four different nitrogen and four different water stress levels, and hyperspectral reflectances in the 350–2500 nm range were recorded at six crop phenostages for two years (2009–2010 and 2010–2011). Thirty-two hyperspectral indices were determined using the first-year reflectance data. Plant nitrogen (N) status, characterized by leaf nitrogen content (LNC) and plant nitrogen accumulation (PNA), showed the highest R2 with the spectral indices at the booting stage. The best five predictive equations for LNC were based on the green normalized difference vegetation index (GNDVI), normalized difference chlorophyll index (NDCI), normalized difference705 (ND705) index, ratio index-1dB (RI-1dB) and Vogelman index a (VOGa). Their validation using the second-year data showed high R2 (>0.80) and ratio of performance to deviation (RPD; >2.25) and low root mean square error (RMSE; <0.24) and relative error (<10%). For PNA, five predictive equations with simple ratio pigment index (SRPI), photochemical reflectance index (PRI), modified simple ratio705 (mSR705), modified normalized difference705 (mND705) and normalized pigment chlorophyll index (NPCI) as predicting indices yielded the best relations with high R2 >0.80. The corresponding RMSE and RE of these ranged from 1.39 to 1.13 and from 24.5% to 33.3%, respectively. Although the predicted values show good agreement with the observed values, the prediction of LNC is more accurate than PNA, as indicated by higher RMSE and very high RE for the latter. Hence, the plant nitrogen stress of wheat can be accurately assessed through the prediction of LNC based on the five identified reflectance indices at the booting stage.Not Availabl

Comparative Evaluation between Multispectral and Hyperspectral Data for Discrimination of Fruit Crops using Statistical Techniques

Not AvailableHorticultural crops unlike field crops are perennial in nature, not having distinct phenology. It is difficult to discriminate horticultural crops using temporal multispectral data. Major limitation of multispectral data is lesser number of bands and mixed pixels which may not be able to discriminate fruit crops but the hyperspectral data has the advantage of having relatively large number of narrow, contiguous bands which lead to continuous spectral reflectance curve, making intricate details visible in the spectrum. For comparison of multispectral data with hyperspectral data, the hyperspectral data which have 2151 numbers of bands has been brought to multispectral level as because multispectral data has very less number of bands. Therefore, in the hyperspectral data, average at 50 nm, 100 nm and 250 nm interval was taken to reduces the data set into 42, 22 and 9 bands. The 4 tier statistical procedure which includes one way Analysis of variance (ANOVA), Classification and regression tree (CART), Jeffries-Matusita (J-M) distance and Linear discriminant analysis (LDA) technique was applied in the reduced band data set. The result of J-M distance and LDA were used to observe whether the reduced band data set can be able to discriminate the fruit crops. The study reveals the limitation of multispectral data in fruit crop discrimination. As the number of bands gets reduced the discriminative power of the data set also gets down.Not Availabl

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Not AvailableHorticultural crops unlike field crops are perennial in nature, not having distinct phenology. It is difficult to discriminate horticultural crops using temporal multispectral data. Major limitation of multispectral data is lesser number of bands and mixed pixels which may not be able to discriminate fruit crops but the hyperspectral data has the advantage of having relatively large number of narrow, contiguous bands which lead to continuous spectral reflectance curve, making intricate details visible in the spectrum. For comparison of multispectral data with hyperspectral data, the hyperspectral data which have 2151 numbers of bands has been brought to multispectral level as because multispectral data has very less number of bands. Therefore, in the hyperspectral data, average at 50 nm, 100 nm and 250 nm interval was taken to reduces the data set into 42, 22 and 9 bands. The 4 tier statistical procedure which includes one way Analysis of variance (ANOVA), Classification and regression tree (CART), Jeffries-Matusita (J-M) distance and Linear discriminant analysis (LDA) technique was applied in the reduced band data set. The result of J-M distance and LDA were used to observe whether the reduced band data set can be able to discriminate the fruit crops. The study reveals the limitation of multispectral data in fruit crop discrimination. As the number of bands gets reduced the discriminative power of the data set also gets down.Not Availabl

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Not AvailableThis study was conducted to understand the behaviour of ten rice genotypes for different water deficit stress levels. The spectroscopic hyperspectral reflectance data in the range of 350–2500 nm was recorded and relative water content (RWC) of plants was measured at different stress levels. The optimal wavebands were identified through spectral indices, multivariate techniques and neural network technique, and prediction models were developed. The new water sensitive spectral indices were developed and existing water band spectral indices were also evaluated with respect to RWC. These indices based models were efficient in predicting RWC with R2 values ranging from 0.73 to 0.94. The contour plotting using the ratio spectral indices (RSI) and normalized difference spectral indices (NDSI) was done in all possible combinations within 350–2500 nm and their correlations with RWC were quantified to identify the best index. Spectral reflectance data was also used to develop partial least squares regression (PLSR) followed by multiple linear regression (MLR) and Artificial Neural Networks (ANN), support vector machine regression (SVR) and random forest (RF) models to calculate plant RWC. Among these multivariate models, PLSR-MLR was found to be the best model for prediction of RWC with R2 as 0.98 and 0.97 for calibration and validation respectively and Root mean square error of prediction (RMSEP) as 5.06. The results indicate that PLSR is a robust technique for identification of water deficit stress in the crop. Although the PLSR is robust technique, if PLSR extracted optimum wavebands are fed into MLR, the results are found to be improved significantly. The ANN model was developed with all spectral reflectance bands. The 43 developed model didn’t produce satisfactory results. Therefore, the model was developed 44 with PLSR selected optimum wavebands as independent x variables and PLSR-ANN model 45 was found better than the ANN model alone. The study successfully conducts a comparative 46 analysis among various modelling approaches to quantify water deficit stress. The methodology developed would help to identify water deficit stress more accurately by predicting RWC in the crops.Not Availabl

Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands

Successful retrieval of leaf area index (LAI) from hyperspectral remote sensing relies on the proper selection of indices or multivariate models. The objectives of the research work were to identify best vegetation index and multivariate model based on canopy reflectance and LAI measured at different growth stages of wheat. Comparison of existing indices revealed optimized soil-adjusted vegetation index (OSAVI) as the best index based on R2 of calibration, validation and root mean square error of validation. Proposed ratio index (RI; R670, R845) and normalized difference index (NDI; R670, R845) provided comparable performance with the existing vegetation indices (R2 = 0.65 and 0.62 for RI and NDI, respectively, during validation). Among the multivariate models, partial least squares regression (PLSR) model with Hyperion band configuration performed the best during validation (R2 = 0.80 and RMSE = 0.58 m2 m−2). Our results manifested the opportunities for developing biophysical products based on satellite sensors

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Not AvailableAccurate estimation of plant water status is a major factor in the decision-making process regarding general land use, crop water management and drought assessment. Visible-near infrared (VNIR) spectroscopy can provide an effective means for real-time and non-invasive monitoring of leaf water content (LWC) in crop plants. The current study aims to identify water absorption bands, indices and multivariate models for development of non-destructive water-deficit stress phenotyping protocols using VNIR spectroscopy and LWC estimated from 10 different rice genotypes. Existing spectral indices and band depths at water absorption regions were evaluated for LWC estimation. The developed models were found efficient in predicting LWC of the samples kept in the same environment with the ratio of performance to deviation (RPD) values varying from 1.49 to 3.05 and 1.66 to 2.63 for indices and band depths, respectively during validation. For identification of novel indices, ratio spectral indices (RSI) and normalised difference spectral indices (NDSI) were calculated in every possible band combination and correlated with LWC. The best spectral indices for estimating LWC of rice were RSI (R1830, R1834) and NDSI (R1830, R1834) with R2 greater than 0.90 during training and validation, respectively. Among the multivariate models, partial least squares regression (PLSR) provided the best results for prediction of LWC (RPD = 6.33 and 4.06 for training and validation, respectively). The approach developed in this study will also be helpful for high-throughput water-deficit stress phenotyping of other crops.Not Availabl

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Not AvailableSpectral discrimination of rice genotypes was investigated using canopy reflectance in the range of 350 to 2500 nm. The pre-processed reflectance spectra were statistically analysed using one-way analysis of variance (ANOVA) followed by classification and regression tree (CART) technique to find significantly sensitive wavelengths for discrimination. The CART was able to select seventeen wavelengths (4 in visible, 5 in near-infra-red and 8 in shortwave infra-red region) well distributed over the entire spectrum. The spectral separability between each pair of rice genotypes at the selected wavebands was quantified using Jeffries–Matusita (J–M) distance analysis. The J–M distance analysis taking 91 pairs of genotypes showed that all the pairs were separable. This result was further validated by quadratic discriminant analysis (QDA) with an overall accuracy of 98%. The variation in biophysical and biochemical attributes of genotypes has been captured through differential spectral reflectance at selected wavebands which could make the discrimination possible.Not Availabl

Assessing rice blast disease severity through hyperspectral remote sensing

Remote sensing is being increasingly used in stress management in different agricultural practices. It is useful for real time analysis for crop stress which is not possible for visual observation alone. Rice blast caused by fungus Pyricularia Oryzae is a serious constrain in rice production in India. There is hardly any basic information available for spectral characteristics of rice blast disease for its real-time detection and management. Present study is to characterize spectral reflectance of blast affected rice in order to identify the sensitive spectral range. Disease severity of 10 different genotypes of rice was graded 0 to 9 based on the extent of host organ covered by symptom or lesion. Result shows that severely infected plant (score 9) have higher reflectance at visible region and lower reflectance at NIR region. Change in the reflectance for the infected plant as compare to the healthy plant was more pronounced in the VNIR, 550 to 760 nm and 1140 and 1300 nm having correlation coefficient above 0.6. The study of change in the reflectance with the change in wavelength (1st derivative) revealed that VNIR region have high correlation with the disease severity. Maximum rate of change value at red edge position (REP) is called as red edge value (REV) which has good relation with disease severity levels. Amplitude of the red edge peak decreases with the increase in severity levels. Amplitude of score 0 and 9 was 0.00929 and 0.002301, respectively for upland land condition whereas the amplitude of the score 0 and 9 was 0.010421 and 0.00193, respectively for upland land rice. This study identifies that VNIR and red edge region are sensitive for detecting rice blast, which could be utilized to aerial or satellite based monitoring blast affected rice cropping region