Service Life and Life-Cycle Assessment of Reinforced Concrete with Fly ash and Limestone Calcined Clay Cement

Environmental impact due to the emission of carbon dioxide during concrete production can be taken care by reducing the clinker content in the cement. The clinker content can be reduced by replacing it with fly ash and limestone calcined clay. Such systems can have a potential to exhibit enhanced durability/service life when exposed to chloride and carbon dioxide. However, estimating probabilistic service life of concretes with such alternative binder systems is difficult due to the lack of quantitative estimates of the input parameters such as chloride diffusion coefficient (DCl), ageing coefficient (m), carbonation coefficient (KCO2), and chloride threshold (Clth). This paper presents the experimentally observed estimates of these parameters for the following systems: (i) 100% OPC, (ii) 70% OPC + 30% fly ash, and (iii) limestone calcined clay cement (LC3) – known as OPC, PFA, and LC3 concretes, respectively, herein. A total of three concrete mixes were designed. Also, based on these input parameters, the probabilistic service life estimates of a bridge pier and a girder made of these three concretes and exposed to chlorides and carbon dioxide are presented. For chloride ingress study, Fick’s 2nd Law of diffusion and Clth have been used. For carbonation study, a recently developed model for estimating carbonation depth (using mixture proportion) have been used. Then, the life‑cycle assessment (LCA) of these three concrete systems in terms of the CO2 emissions per unit of concrete per year of estimated service life is presented - for both chloride and carbonation induced corrosion. In chloride laden environments, the service life of the bridge pier and girder systems could be enhanced by about 10 times by using fly ash or LC3 concretes – for similar strength grade concretes. Also, the average annual CO2 emissions (during the expected service life) of PFA and LC3 concretes could be about 3 and 7 times, respectively, lower than that of OPC concretes of similar strength grade. In case of carbonation-induced corrosion, the limited experimental data indicate that the PFA and LC3 concretes could exhibit a lower service life and higher average annual CO2 emissions (during the expected service life) than OPC concretes

Artificial neural network for estimating leaf fresh weight of rice plant through visual-nir imaging

Not AvailablePrediction of fresh biomass is the key for evaluation of the response of crop genotypes to diverse input and stress conditions, and forms basis for calculating net primary production. Hence, accurate and high throughput estimation of fresh biomass is critical for plant phenotyping. As conventional phenotyping approaches for measuring fresh biomass is time consuming, laborious and destructive, image based phenotyping methods are being widely used now in plant phenotyping. However, current approaches for estimating fresh biomass of plants are based on projected shoot area estimated from the visual (VIS) image. These approaches do not consider the water content of the plant tissues which are about 70-80% in leafy vegetation. Since water absorbs radiation in the Near Infra-Red (NIR) (900–1700 nm) region, it has been hypothesized that combined use of VIS and NIR imaging can predict the fresh biomass more accurately that the VIS image alone. In this study, VIS and NIR imaging were captured for rice leaves with different moisture content as a test case. For background subtraction from NIR image, PlantCV v2 NIR imaging algorithm was implemented in MATLAB software (version 2015b). The proposed image derived parameter, viz. Green Leaf Proportion (GLP) from VIS image and mean gray value/intensity (NIR_MGI) from NIR image were used as input to develop Artificial Neural Network (ANN) model to estimate the Leaf Fresh Weight (LFW). This proposed approach significantly enhanced the fresh biomass prediction as compared to the conventional regression technique based on projected shoot area derived from VIS image.Not Availabl

Root Morphological Characteristics of Five Rice Genotypes with Different Nitrogen Use Efficiency

While nitrogen (N) uptake in rice has been extensively studied, the influence of root morphology on this process is not fully elucidated. This study explores the root morphological characteristics and N uptake of five diverse rice genotypes under different N sources, including novel slow-release nanoclay polymer/biopolymer composite (NCPC/NCBPC) fertilizers. A greenhouse experiment was conducted using five diverse rice genotypes (Swarna, Pusa Basmati-1, Pusa-44, MTU1010 and Nagina-22) with five N treatments (T1- control (without N fertilizer), T2- 100% RDF of N through urea, T3- 75% RDF of N through urea loaded NCPC, T4- 75% RDF of N through urea loaded NCBPC-I (NCBPC prepared with wheat flour), T5- 75% RDF of N through urea loaded NCBPC-II (NCBPC prepared with maida)). The results of the study revealed that maximum overall root growth was recorded under Pusa Basmati-1 followed by Swarna, MTU 1010, Nagina-22 and Pusa-44. Among the N treatment, maximum root growth and N uptake was recorded under NCPC treatment followed by NCBPC-II, NCBPC-I, urea and control. Thus, the study reveals significant variations in root traits among genotypes and N treatments, with notable improvements observed under NCPC and NCBPC based N treatments

Leaf Count Aided Novel Framework for Rice (Oryza sativa L.) Genotypes Discrimination in Phenomics: Leveraging Computer Vision and Deep Learning Applications

Drought is a detrimental factor to gaining higher yields in rice (Oryza sativa L.), especially amid the rising occurrence of drought across the globe. To combat this situation, it is essential to develop novel drought-resilient varieties. Therefore, screening of drought-adaptive genotypes is required with high precision and high throughput. In contemporary emerging science, high throughput plant phenotyping (HTPP) is a crucial technology that attempts to break the bottleneck of traditional phenotyping. In traditional phenotyping, screening significant genotypes is a tedious task and prone to human error while measuring various plant traits. In contrast, owing to the potential advantage of HTPP over traditional phenotyping, image-based traits, also known as i-traits, were used in our study to discriminate 110 genotypes grown for genome-wide association study experiments under controlled (well-watered), and drought-stress (limited water) conditions, under a phenomics experiment in a controlled environment with RGB images. Our proposed framework non-destructively estimated drought-adaptive plant traits from the images, such as the number of leaves, convex hull, plant–aspect ratio (plant spread), and similarly associated geometrical and morphological traits for analyzing and discriminating genotypes. The results showed that a single trait, the number of leaves, can also be used for discriminating genotypes. This critical drought-adaptive trait was associated with plant size, architecture, and biomass. In this work, the number of leaves and other characteristics were estimated non-destructively from top view images of the rice plant for each genotype. The estimation of the number of leaves for each rice plant was conducted with the deep learning model, YOLO (You Only Look Once). The leaves were counted by detecting corresponding visible leaf tips in the rice plant. The detection accuracy was 86–92% for dense to moderate spread large plants, and 98% for sparse spread small plants. With this framework, the susceptible genotypes (MTU1010, PUSA-1121 and similar genotypes) and drought-resistant genotypes (Heera, Anjali, Dular and similar genotypes) were grouped in the core set with a respective group of drought-susceptible and drought-tolerant genotypes based on the number of leaves, and the leaves’ emergence during the peak drought-stress period. Moreover, it was found that the number of leaves was significantly associated with other pertinent morphological, physiological and geometrical traits. Other geometrical traits were measured from the RGB images with the help of computer vision

Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring

Water deficit in crops induces a stress that may ultimately result in low production. Identification of response of genotypes towards water deficit stress is very crucial for plant phenotyping. The study was carried out with the objective to identify the response of different rice genotypes to water deficit stress. Ten rice genotypes were grown each under water deficit stress and well watered or nonstress conditions. Thermal images coupled with visible images were recorded to quantify the stress and response of genotypes towards stress, and relative water content (RWC) synchronized with image acquisition was also measured in the lab for rice leaves. Synced with thermal imaging, Canopy reflectance spectra from same genotype fields were also recorded. For quantification of water deficit stress, Crop Water Stress Index (CWSI) was computed and its mode values were extracted from processed thermal imageries. It was ascertained from observations that APO and Pusa Sugandha-5 genotypes exhibited the highest resistance to the water deficit stress or drought whereas CR-143, MTU-1010, and Pusa Basmati-1 genotypes ascertained the highest sensitiveness to the drought. The study reveals that there is an effectual relationship (R2 = 0.63) between RWC and CWSI. The relationship between canopy reflectance spectra and CWSI was also established through partial least square regression technique. A very efficient relationship (calibration R2 = 0.94 and cross-validation R2 = 0.71) was ascertained and 10 most optimal wavebands related to water deficit stress were evoked from hyperspectral data resampled at 5 nm wavelength gap. The identified ten most optimum wavebands can contribute in the quick detection of water deficit stress in crops. This study positively contributes towards the identification of drought tolerant and drought resistant genotypes of rice and may provide valuable input for the development of drought-tolerant rice genotypes in future

Image_1_Autofluorescence−spectral imaging for rapid and invasive characterization of soybean for pre-germination anaerobic stress tolerance.jpeg

The autofluorescence-spectral imaging (ASI) technique is based on the light-emitting ability of natural fluorophores. Soybean genotypes showing contrasting tolerance to pre-germination anaerobic stress can be characterized using the photon absorption and fluorescence emission of natural fluorophores occurring in seed coats. In this study, tolerant seeds were efficiently distinguished from susceptible genotypes at 405 nm and 638 nm excitation wavelengths. ASI approach can be employed as a new marker for the detection of photon-emitting compounds in the tolerant and susceptible soybean seed coats. Furthermore, the accuracy of rapid characterization of genotypes using this technique can provide novel insights into soybean breeding.</p

DataSheet_1_Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.pdf

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs</p

Not Available

Not AvailableHigh throughput non-destructive phenotyping is emerging as a significant approach for phenotyping germplasm and breeding populations for the identification of superior donors, elite lines, and QTLs. Detection and counting of spikes, the grain bearing organs of wheat, is critical for phenomics of a large set of germplasm and breeding lines in controlled and field conditions. It is also required for precision agriculture where the application of nitrogen, water, and other inputs at this critical stage is necessary. Further, counting of spikes is an important measure to determine yield. Digital image analysis and machine learning techniques play an essential role in non-destructive plant phenotyping analysis.Not Availabl

Not Available

Not AvailableNot AvailableNot Availabl

[In Press] Key physiological traits for drought tolerance identified through phenotyping a large set of slicing cucumber (Cucumis sativus L.) genotypes under field and water-stress conditions

Cucumber is one of the important salad vegetables cultivated worldwide and is highly sensitive to water stress. However, till date, no drought-tolerant cucumber genotypes have been identified using a large set of diverse germplasms via high-throughput phenotyping methods. This study involved screening of a large set of Indian-origin cucumber germplasms for drought stress response using water-deficit and polyethylene glycol (PEG)-induced stress conditions in hydroponic solutions. Water-deficit and PEG-induced methods were optimized before screening of an entire set of germplasm for key physiological traits. Pearson’s correlation revealed non-significant difference between these two methods. Hierarchical cluster analysis and drought tolerance matrix score (DTMS) were calculated based on key physiological traits for ranking of the genotypes. Furthermore, the entire set of genotypes was exposed to water stress (< 8% soil moisture) for 15 d under field conditions to record yield-related traits for validation of the hydroponic-based ranking. Finally, eight tolerant genotypes were identified with high seedling survivability percentage, minimum reduction in root–shoot dry weight, fresh weight and water content percentage, highest DTMS and minimum yield reduction under field stress conditions compared with seven identified sensitive genotypes. The optimized rapid phenotyping method and identified drought-tolerant lines will be instrumental in understanding the physiological and molecular basis of drought tolerance and in facilitating the development of climate-resilient improved cucumber genotypes in the future