scholarly journals Banana Biomass Estimation and Yield Forecasting from Non-Destructive Measurements for Two Contrasting Cultivars and Water Regimes

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1435 ◽  
Author(s):  
Bert Stevens ◽  
Jan Diels ◽  
Allan Brown ◽  
Stanley Bayo ◽  
Patrick A. Ndakidemi ◽  
...  
Keyword(s):  
Water Stress ◽  
Regression Models ◽  
Growth Yield ◽  
Moisture Stress ◽  
Least Square ◽  
Biomass Estimation ◽  
Fresh Weight ◽  
Dry Biomass ◽  
Vegetative Biomass ◽  
Non Destructive

The largest abiotic constraint threatening banana (Musa spp.) production is water stress, impacting biomass buildup and yields; however, so far no studies have investigated the effects of water stress on allometric equations in banana. Weighted least square regression models were built for (i) estimating aboveground vegetative dry biomass (ABGVD) and corm dry biomass (cormD) and (ii) forecasting bunch fresh weight (bunchF), based on non-destructive parameters for two cultivars, Mchare Huti-Green Bell (HG, AA) and Cavendish Grande Naine (GN, AAA), under two irrigation regimes: full irrigation (FI) and rainfed (RF). FI affected growth, yield, and phenological parameters in the field (p < 0.05) depending on the onset of moisture stress. Pseudostem volume (Vpseudo) proved a good predictor for estimating ABGVD (R2adj = 0.88–0.92; RRMSE = 0.14–0.19), but suboptimal for cormD (R2adj = 0.90–0.89, RRMSE = 0.21–0.26 for HG; R2adj = 0.34–0.57, RRMSE = 0.38–0.43 for GN). Differences between RF and FI models (p < 0.05) were small as 95%CI overlapped. Vpseudo at flowering predicted bunchF in FI plots correctly (R2adj = 0.70 for HG, R2adj = 0.43 for GN; RRMSE = 0.12–0.15 for HG and GN). Differences between FI and RF models were pronounced as 95%CI did not overlap (p < 0.05). Bunch allometry was affected by irrigation, proving bunchF forecasting needs to include information on moisture stress during bunch filling or information on bunch parameters. Our allometric relationships can be used for rapid and non-destructive aboveground vegetative biomass (ABGVD) assessment over time and to forecast bunch potentials based on Vpseudo at flowering.

scholarly journals Estimation of strawberry firmness using hyperspectral imaging: a comparison of regression models

2021 ◽  
Author(s):  
Binu Devassy ◽  
Sony George
Keyword(s):  
Hyperspectral Imaging ◽  
Regression Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Regression Techniques ◽  
Important Quality ◽  
Non Destructive ◽  
Better Than

Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The res ults show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results.

scholarly journals Dry Biomass Estimation of Hedge Banks: Allometric Equation vs. Structure from Motion via Unmanned Aerial Vehicle

2018 ◽  
Vol 64 (No. 4) ◽  
pp. 149-156
Author(s):  
Lingner Stefan ◽  
Thiessen Eiko ◽  
Müller Kerrin ◽  
Hartung Eberhard
Keyword(s):  
Structure From Motion ◽  
Reference Method ◽  
Dry Mass ◽  
Allometric Equation ◽  
Biomass Estimation ◽  
Technical Requirements ◽  
Dry Biomass ◽  
Aerial Vehicle ◽  
Non Destructive ◽  
Volume Models

The wood yield of hedge banks is very heterogeneous and hard to estimate in advance. The aim of the present study was to estimate the dry biomass of hedge banks shortly before harvesting using two different non-destructive approaches: (i) allometric equation based on DBH, (ii) volume calculations based on Structure from Motion; and to compare these estimations to the results of the (invasive) reference method: weighing after harvesting. Study objects were three different 100 m hedge banks in Schleswig-Holstein, Germany that were divided into 10 m segments (n = 30). These segments were harvested and weighed separately to calculate dry biomass. The allometric equation yielded a relative root mean square error (rRMSE) of 32.4%. The Structure from Motion (SfM) volume models yielded an rRMSE of 30.0%. These results indicate that SfM approaches are comparably precise to allometric equations for dry mass estimations of hedge banks. SfM approaches are less time consuming but have higher technical requirements.

scholarly journals Effects of Three Levels of Midday Stem Water Potential on Chandler Walnut Growth and Yield in California

HortScience ◽  
2004 ◽  
Vol 39 (4) ◽  
pp. 814C-814
Author(s):  
Richard P. Buchner* ◽  
Allan Fulton ◽  
Bruce Lampinen ◽  
Ken Shackel ◽  
Terry Prichard ◽  
...  
Keyword(s):  
Water Stress ◽  
Water Potential ◽  
Juglans Regia ◽  
Growth Yield ◽  
Moisture Stress ◽  
Growth And Yield ◽  
Northern California ◽  
Stem Water Potential ◽  
Mature Trees ◽  
Stem Water

Ninth leaf California Chandler Walnuts (Juglans regia) on Northern California Black (Juglans hindsii) or Paradox (English/black hybrid) rootstock were irrigated to achieve three levels of Midday Stem Water Potential (MSWP). Target potentials were: 1) low water stress (average MSWP of -3.2 bars); 2) mild water stress (average MSWP of -6.2 bars); and 3) moderate water stress (average MSWP of -7.3 bars). Stem Water Potential was measured midday (12-4 pm) by placing leaves inside water impervious, light blocking foil bags. Leaves remained bagged for at least ten minutes to achieve equilibrium. Bagged leaves were removed, placed inside a pressure chamber and stem water potential was measured at endpoint. Data are presented for the 2002 and 2003 seasons. Withholding irrigation water had a significant impact on `Chandler' growth, productivity, and profitability particularly on young, vigorously growing trees. Chandler/Black appears to be more tolerant to water stress compared to Chandler/Paradox For Chandler on Paradox, water stress significantly reduced growth, yield, price per pound, percent edible kernel, and resulted in darker kernels. In addition, water stress significantly increased the total percent offgrade. Withholding irrigation does not appear to be a good strategy in young, vigorously growing `Chandler' orchards. Mature trees and trees grafted onto Northern California black rootstock may be more tolerant of moisture stress.

scholarly journals Starchy Vegetables and Metabolic Syndrome in Costa Rica

Nutrients ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1639
Author(s):  
Zhongyao Li ◽  
Dongqing Wang ◽  
Edward A. Ruiz-Narváez ◽  
Karen E. Peterson ◽  
Hannia Campos ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Regression Models ◽  
Fasting Blood Glucose ◽  
Population Based ◽  
Cross Sectional Study ◽  
Costa Rican ◽  
Least Square ◽  
Inverse Association ◽  
Linear Regression Models ◽  
Cross Sectional

Only a few studies primarily examined the associations between starchy vegetables (other than potatoes) and metabolic syndrome (MetS). We aimed to evaluate the association between starchy vegetables consumption and MetS in a population-based sample of Costa Rican adults. We hypothesized that a higher overall intake of starchy vegetables would not be associated with higher MetS prevalence. In this cross-sectional study, log-binomial regression models were used to estimate prevalence ratios (PRs) of MetS across quintiles of total, unhealthy, healthy starchy vegetables, and individual starchy vegetables (potatoes, purple sweet potatoes, etc.), among 1881 Costa Rican adults. Least square means and 95% confidence intervals (CIs) from linear regression models were estimated for each MetS component by categories of starchy vegetable variables. Higher intakes of starchy vegetables were associated with a higher prevalence of MetS in crude models, but no significant trends were observed after adjusting for confounders. A significant inverse association was observed between total starchy and healthy starchy vegetables consumption and fasting blood glucose. In this population, starchy vegetables might be part of a healthy dietary pattern.

scholarly journals Theoretical Simulation of Near-Infrared Spectrum of Piperine: Insight into Band Origins and the Features of Regression Models

2021 ◽  
pp. 000370282110279
Author(s):  
Justyna Grabska ◽  
Krzysztof B. Beć ◽  
Sophia Mayr ◽  
Christian W. Huck
Keyword(s):  
Infrared Spectrum ◽  
Regression Models ◽  
Near Infrared ◽  
Computing Time ◽  
Black Pepper ◽  
Partial Least Square ◽  
Least Square ◽  
Complex Nature ◽  
Chemical Information ◽  
Near Infrared Spectrum

We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample.

scholarly journals Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 581 ◽  
Author(s):  
Yuanyuan Fu ◽  
Guijun Yang ◽  
Xiaoyu Song ◽  
Zhenhong Li ◽  
Xingang Xu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Least Squares ◽  
Aboveground Biomass ◽  
Regression Models ◽  
Digital Images ◽  
Predictive Performance ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Biomass Estimation ◽  
High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

RESPONSE OF PEAS TO ENVIRONMENT: IV. EFFECT OF FIVE SOIL WATER REGIMES ON GROWTH AND DEVELOPMENT OF PEAS

1968 ◽  
Vol 48 (2) ◽  
pp. 129-137 ◽  
Author(s):  
A. R. Maurer ◽  
H. F. Fletcher ◽  
D. P. Ormrod
Keyword(s):  
Water Stress ◽  
Soil Water ◽  
Growth And Development ◽  
Dry Matter ◽  
Fresh Weight ◽  
Water Regimes ◽  
Severe Water Stress ◽  
Water Conditions ◽  
Before And After ◽  
Soil Water Conditions

Pea plants growing in "weighing lysimeters" were subjected to five soil-water regimes to determine their response to varying conditions of soil water imposed at different stages of development. Plants subjected to a minimal water stress developed luxuriantly and continued to grow up to the harvest period. Pea yield and plant height were not reduced, but fresh weight and dry matter were less if irrigation was applied when soil water fell to 60% rather than 88% of that available. A severe water stress after blossom reduced pea yield, irrespective of soil-water conditions prior to blossom. Plants which had been given ample soil water before blossom wilted visibly when a severe stress was imposed in the post-blossom period, yet wilting did not occur in plants subjected to severe water stress both before and after blossom. Severe water stress prior to blossom did not cause a decrease in pea yield if ample soil moisture was made available after blossom.

Water Stress Effects on Growth, Yield and Water Use Efficiency of Hemarthria Compressa

2012 ◽  
Vol 212-213 ◽  
pp. 578-585
Author(s):  
Zhong Wen Yang ◽  
Jun Ying Jin ◽  
Xin Yi Xu
Keyword(s):  
Water Stress ◽  
Water Use Efficiency ◽  
Water Use ◽  
Control Period ◽  
Growth Yield ◽  
Control Treatment ◽  
Sensitive Period ◽  
Total Biomass ◽  
Use Efficiency ◽  
Hemarthria Compressa

Water stress is an important approach to use water resources efficiently and remit the agricultural water shortage. Hemarthria compressa is one of perennial grasses, a pasture of high quality, which has abundant species resources in China. To explore the response of the growth, yield and water use efficiency(WUE) of Hemarthria compressa under water stress, this study, adapting pot experiment, imposed three water stress degree (LD, MD and SD) treatments and a control treatment on Hemarthria compressa. The data of growth indicators during control period, yield and total water consumption were obtained. The results show a noticeable inhibitory action of water stress on the growth of Hemarthria compressa. Along with the intensifying of water stress, plant height increment, leaf area, total biomass, dry matter of each organ and yield decreased, and the root-shoot ratio increased firstly and inclined to slump finally. Plants under the middle water stress treatment achieved the greatest WUE of 38.25 kg/m3. The first 10d in the water control period was the most sensitive period of the pasture responding to water stress.

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