Detection of hidden insect Sitophilus oryzae in wheat by low-field nuclear magnetic resonance: Poster

Insects, either adults or larvae, living inside grains are difficlut to detect but can cause enormous loss of grain. Therefore, we explored the use of low-field nuclear magnetic resonance (LF-NMR) techniques to detect Sitophilus oryzae hidden inside wheat. Significant difference in transverse relaxation times (T2/ms) and the T2 components proportion (P2/%) was observed between wheat and S. oryzae at its four different growth stages (small larvae, large larve stage, pupal stage and adult stage). The transverse relaxation signals on the infested wheat kernels varied with S. oryzae developmental stages. LF-NMR image of uninfested wheat were very different than infested wheat with the hidden insects at its four growth stages. Therefore, LF-NMR, as a novel non-destructive method, could be used to detect insects hidden in grains to take necessary management against pest damage to grains during storage.Insects, either adults or larvae, living inside grains are difficlut to detect but can cause enormous loss of grain. Therefore, we explored the use of low-field nuclear magnetic resonance (LF-NMR) techniques to detect Sitophilus oryzae hidden inside wheat. Significant difference in transverse relaxation times (T2/ms) and the T2 components proportion (P2/%) was observed between wheat and S. oryzae at its four different growth stages (small larvae, large larve stage, pupal stage and adult stage). The transverse relaxation signals on the infested wheat kernels varied with S. oryzae developmental stages. LF-NMR image of uninfested wheat were very different than infested wheat with the hidden insects at its four growth stages. Therefore, LF-NMR, as a novel non-destructive method, could be used to detect insects hidden in grains to take necessary management against pest damage to grains during storage

Comparative role of non-stress test and colour doppler in high risk pregnancy predicted by placental histopathology and foetal outcome

Background: Assessment of the foetal wellbeing is done by various biophysical methods. Non stress test (NST) is the most commonly used test for antepartum evaluation of foetal status. It involves the use of doppler-detected foetal heart rate acceleration coincident with foetal movement perceived by mother. Duplex sonography and its off-shoot, colour duplex sonography, are relatively newer methods that combine the pulsed echo technique of sectional image formation with the doppler evaluation of blood flow.Methods: The comparative study was carried out on 200 booked term pregnant patients in the department of Obstetrics and Gynaecology, Dr. S. N. Medical College, Jodhpur Rajasthan, India. All patients were subjected to non-stress test and colour doppler and were evaluated for placental histopathology and foetal outcome in terms of low APGAR score, number of NICU admissions and perinatal mortality.Results: In our study it was found that in high-risk group 25% had non-reassuring NST and 19% had doppler findings suggestive of foetal hypoxia. In the control group 13% had non-reassuring NST and 4% had doppler findings suggestive of foetal hypoxia. It was seen that when either NST was non-reassuring or colour doppler suggested foetal hypoxia or both, these patients required admissions antenatally, had meconium stained liquor suggestive of foetal distress, had operative delivery for foetal distress, had low APGAR score, required NICU admission, and higher perinatal mortality.Conclusions: Doppler and NST are effective in predicting a normal healthy foetus. Doppler depicts chronic hypoxic changes while NST can detect acute events in presence or absence of chronic hypoxia

A Review of Analytical Methods for Calculating Static Pressures in Bulk Solids Storage Structures

The Janssen equation is a widely used method for calculating pressures in bulk storage structures. This review explores the historical legacy of Janssen’s equation and its applications in both planar and three-dimensional structures. Our focus is on the limitations of the original formulation of Janssen, extensions made to avoid these deficiencies, and alternative models that have been developed. The motivation behind these modifications is to improve the representation of shear stress within a grain bin in both the horizontal and vertical directions. Modifications to Janssen’s basic assumptions include the vertical-to-horizontal stress ratio (k), the coefficient of friction between the wall and the stored bulk material (μ), internal angle of friction (φ), and bulk density (ρ). We also discuss recent developments in pressure theories, which have provided new insights into pressure fields in bulk storage bins. These modern approaches include the continuum elastic theory and microscopic theory. Finally, we discuss recent developments in pressure theories which provide new insights into the storage of bulk solids. Overall, this review provides a comprehensive overview of the Janssen equation and its historical development, limitations, and extensions, as well as recent advancements in pressure theory that offer a more accurate representation of pressure fields in bulk storage structures

Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios

Rapid, non-destructive, and smart assessment of the maturity levels of fruit facilitates their harvesting and handling operations throughout the supply chain. Recent studies have introduced machine vision systems as a promising candidate for non-destructive evaluations of the ripeness levels of various agricultural and forest products. However, the reported models have been fruit-specific and cannot be applied to other fruit. In this regard, the current study aims to evaluate the feasibility of estimating the ripeness levels of wild pistachio fruit using image processing and artificial intelligence techniques. Images of wild pistachios at four ripeness levels were recorded using a digital camera, and 285 color and texture features were extracted from 160 samples. Using the quadratic sequential feature selection method, 16 efficient features were identified and used to estimate the maturity levels of samples. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and an artificial neural network (ANN) were employed to classify samples into four ripeness levels, including initial unripe, secondary unripe, ripe, and overripe. The developed machine vision system achieved a correct classification rate (CCR) of 93.75, 97.5, and 100%, respectively. The high accuracy of the developed models confirms the capability of the low-cost visible imaging system in assessing the ripeness of wild pistachios in a non-destructive, automated, and rapid manner

Effect of Laser Biostimulation on Germination of Sub-Optimally Stored Flaxseeds (<i>Linum usitatissimum</i>)

Sub-optimal storage of grains could deteriorate seed germination and plant viability. Recent research studies have established that laser biostimulation of seeds could be used as a safe and sustainable alternative to chemical treatment for improving crop germination and growth. Herein, the efficacy of this novel technique is evaluated to see if poor germinability caused by sub-optimal storage of flaxseeds (Linum usitatissimum) could be reversed using laser biostimulation. Healthy flaxseeds were first subjected to sub-optimal storage conditions (30 °C for ten weeks) to degrade their germinability. Two low-cost lasers, including a single-wavelength red laser (659 nm) and a dual-wavelength green/infrared laser (531 and 810 nm (ratio ~10:1)) were then used on two groups viz. healthy (properly stored) and sub-optimally stored (artificially degraded (AD)) seeds and irradiated for 0 (control), 5, 10, and 15 min using total power densities of 7.8 and 6.2 mW/cm2, respectively. In the case of AD seeds, 5-min dual-wavelength laser treatment was found to be the most efficient setting as it improved the mean germination percentage, mean germination time, germination speed, germination rate index, wet weight, and dry weight by 29.3, 16.8, 24.2, 24.2, 15.7, and 20.6%, respectively, with respect to control samples. In the case of healthy seeds, dual-wavelength laser treatment could induce significant enhancement in seeds’ root length, wet weight, and dry weight (improved by 26, 23, and 8%, respectively) under 10 min of irradiation. On the other hand, the effect of applied red laser treatment was not very promising as it could only induce significant enhancement in the mean germination time of AD seeds (improved by 17%). Overall, this study demonstrates the potential of laser biostimulation in reversing the adverse effect of poor crop storage. We believe these findings could spur the development of a physical tool for manipulating seed germination and plant growth

Effect of Laser Biostimulation on Germination of Sub-Optimally Stored Flaxseeds (Linum usitatissimum)

Sub-optimal storage of grains could deteriorate seed germination and plant viability. Recent research studies have established that laser biostimulation of seeds could be used as a safe and sustainable alternative to chemical treatment for improving crop germination and growth. Herein, the efficacy of this novel technique is evaluated to see if poor germinability caused by sub-optimal storage of flaxseeds (Linum usitatissimum) could be reversed using laser biostimulation. Healthy flaxseeds were first subjected to sub-optimal storage conditions (30 &deg;C for ten weeks) to degrade their germinability. Two low-cost lasers, including a single-wavelength red laser (659 nm) and a dual-wavelength green/infrared laser (531 and 810 nm (ratio ~10:1)) were then used on two groups viz. healthy (properly stored) and sub-optimally stored (artificially degraded (AD)) seeds and irradiated for 0 (control), 5, 10, and 15 min using total power densities of 7.8 and 6.2 mW/cm2, respectively. In the case of AD seeds, 5-min dual-wavelength laser treatment was found to be the most efficient setting as it improved the mean germination percentage, mean germination time, germination speed, germination rate index, wet weight, and dry weight by 29.3, 16.8, 24.2, 24.2, 15.7, and 20.6%, respectively, with respect to control samples. In the case of healthy seeds, dual-wavelength laser treatment could induce significant enhancement in seeds&rsquo; root length, wet weight, and dry weight (improved by 26, 23, and 8%, respectively) under 10 min of irradiation. On the other hand, the effect of applied red laser treatment was not very promising as it could only induce significant enhancement in the mean germination time of AD seeds (improved by 17%). Overall, this study demonstrates the potential of laser biostimulation in reversing the adverse effect of poor crop storage. We believe these findings could spur the development of a physical tool for manipulating seed germination and plant growth

Quality Assessment of Dried White Mulberry (Morus alba L.) Using Machine Vision

Over the past decade, the fresh white mulberry (Morus alba L.) fruit has gained growing interest due to its superior health and nutritional characteristics. While white mulberry is consumed as fresh fruit in several countries, it is also popular in dried form as a healthy snack food. One of the main challenges that have prevented a wider consumer uptake of this nutritious fruit is the non-uniformity in its quality grading. Therefore, identifying a reliable quality grading tool can greatly benefit the relevant stakeholders. The present research addresses this need by developing a novel machine vision system that combines the key strengths of image processing and artificial intelligence. Two grades (i.e., high- and low-quality) of white mulberry were imaged using a digital camera and 285 colour and textural features were extracted from their RGB images. Using the quadratic sequential feature selection method, a subset of 23 optimum features was identified to classify samples into two grades using artificial neural networks (ANN) and support vector machine (SVM) classifiers. The developed system under both classifiers achieved the highest correct classification rate (CCR) of 100%. Indeed, the latter approach offered a smaller mean squared error for the training and test sets. The developed model&rsquo;s high accuracy confirms the machine vision&rsquo;s suitability as a reliable, low-cost, rapid, and intelligent tool for quality monitoring of dried white mulberry

Implications of Blending Pulse and Wheat Flours on Rheology and Quality Characteristics of Baked Goods: A Review

Bread is one of the most widely consumed foods in all regions of the world. Wheat flour being its principal ingredient is a cereal crop low in protein. The protein content of a whole grain of wheat is about 12&ndash;15% and is deficit in some essential amino acids, for example, lysine. Conversely, the protein and fibre contents of legume crops are between 20 and 35% and 15 and 35%, respectively, depending on the type and cultivar of the legume. The importance of protein-rich diets for the growth and development of body organs and tissues as well as the overall functionality of the body is significant. Thus, in the last two decades, there has been a greater interest in the studies on the utilization of legumes in bread production and how the incorporation impacts the quality characteristics of the bread and the breadmaking process. The addition of plant-based protein flours has been shown to produce an improved quality characteristic, especially the nutritional quality aspect of bread. The objective of this review is to synthesize and critically investigate the body of research on the impact of adding legume flours on the rheological attributes of dough and the quality and baking characteristics of bread

Quality Assessment of Dried White Mulberry (<i>Morus alba L.)</i> Using Machine Vision

Over the past decade, the fresh white mulberry (Morus alba L.) fruit has gained growing interest due to its superior health and nutritional characteristics. While white mulberry is consumed as fresh fruit in several countries, it is also popular in dried form as a healthy snack food. One of the main challenges that have prevented a wider consumer uptake of this nutritious fruit is the non-uniformity in its quality grading. Therefore, identifying a reliable quality grading tool can greatly benefit the relevant stakeholders. The present research addresses this need by developing a novel machine vision system that combines the key strengths of image processing and artificial intelligence. Two grades (i.e., high- and low-quality) of white mulberry were imaged using a digital camera and 285 colour and textural features were extracted from their RGB images. Using the quadratic sequential feature selection method, a subset of 23 optimum features was identified to classify samples into two grades using artificial neural networks (ANN) and support vector machine (SVM) classifiers. The developed system under both classifiers achieved the highest correct classification rate (CCR) of 100%. Indeed, the latter approach offered a smaller mean squared error for the training and test sets. The developed model’s high accuracy confirms the machine vision’s suitability as a reliable, low-cost, rapid, and intelligent tool for quality monitoring of dried white mulberry

A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis

This study proposes a hybrid gene selection method to identify and predict key genes in Arabidopsis associated with various stresses (including salt, heat, cold, high-light, and flagellin), aiming to enhance crop tolerance. An open-source microarray dataset (GSE41935) comprising 207 samples and 30,380 genes was analyzed using several machine learning tools including the synthetic minority oversampling technique (SMOTE), information gain (IG), ReliefF, and least absolute shrinkage and selection operator (LASSO), along with various classifiers (BayesNet, logistic, multilayer perceptron, sequential minimal optimization (SMO), and random forest). We identified 439 differentially expressed genes (DEGs), of which only three were down-regulated (AT3G20810, AT1G31680, and AT1G30250). The performance of the top 20 genes selected by IG and ReliefF was evaluated using the classifiers mentioned above to classify stressed versus non-stressed samples. The random forest algorithm outperformed other algorithms with an accuracy of 97.91% and 98.51% for IG and ReliefF, respectively. Additionally, 42 genes were identified from all 30,380 genes using LASSO regression. The top 20 genes for each feature selection were analyzed to determine three common genes (AT5G44050, AT2G47180, and AT1G70700), which formed a three-gene signature. The efficiency of these three genes was evaluated using random forest and XGBoost algorithms. Further validation was performed using an independent RNA_seq dataset and random forest. These gene signatures can be exploited in plant breeding to improve stress tolerance in a variety of crops