In the banana crop, leaf area is a fundamental trait for production; however, monitoring this variable during a cycle is difficult due to the structural characteristics of the plant, and a method for ...its determination is necessary. Therefore, the objective of this research was to propose a model for estimating total leaf area by measuring the cross-sectional area of the pseudostem to identify when meristematic differentiation occurs. In plants between F10 and flowering, functional leaves were measured for length, width, and dry mass. Cross-sectional area was calculated every 10 cm from the base to 70 cm, at ⅓, ½ of the plant height and up to the last pair of leaves. From the principal components, the cross-sectional measurement at 50 cm was selected, obtaining a nonlinear model for indirect estimation of leaf area. Subsequently, Fisher’s linear discriminant analysis was used with the parameters associated with the number of leaves emitted and the estimated leaf area to obtain the cutoff point as the centroid of the extracted components. As an indicator for the approximate identification of the moment of meristem differentiation, the emission of leaf 12 was generated, which determines the phenological stage (vegetative-reproductive) of the plant. The results describe tools to follow up the growth in the productive units to facilitate crop monitoring, allowing the generation of differential production approaches.
Mature tomatoes (Solanum lycopersicum) have a very short shelf life which deteriorates quickly at ambient temperatures. Low temperature storage is the most successful and commonly used treatment to ...slow down the ripening process and decay development in mature or green tomatoes. However, low temperature may induce a disorder “chilling injury (CI)” which could limit the storage time of tomatoes. This review will summarize the currently published biochemical and genetic knowledge about the potential development of chilling injury (CI) in tomato fruit. It encompasses all studies reported on pre and postharvest issues and treatments that may affect the occurrence and severity of CI. This review paper will provide a better insight to understand the detailed mechanism and genes involved in the process of CI in tomato and help investigate the areas which need to be further explored.
•Comprehensive analysis of physical and chemical treatments inducing tolerance to chilling injury (CI) are discussed•HAT/HWT is one of the most effective techniques to reduce the damage caused by chilling injury in tomatoes•Advanced technique like circular RNAs technology discovered several novel genes directly involved in chilling injury•Future research perspective should be focused on the analysis of molecular basis of CI induced early events.
E-nose device, data from GC-MS (measured data), and statistical and mathematical analytic techniques like PCA, PLSR, LDA, and ANN was used in this study and then a GEP programing model developed to ...estimate caffeine content of samples. Various samples of coffee beans were tested, when caffeine was used as the reference data, R2 for the PLSR and ANN models were 0.9577 and 0.9634, respectively. R2 for the LDA model were identical to 0.9714. Additionally, R2 of the PLSR and ANN models for palmitic acid respectively, was reported 0.893 and 0.9388. Caffeine calibration data produced the greatest results for identifying, according to the information gathered, also GEP model R2 was reported 0.9581.
•Non-destructive method for classification of coffee bean varieties and quality.•E-nose machine using models and modeling methods.•PLSR, ANN and LDA as a tool for classification of coffee’s species and cultivars.
Concrete compressive strength plays an important role in determining the mechanical properties of concrete. The determination of concrete compressive strength requires lengthy laboratory tests. The ...ability to predict concrete compressive strength with advanced machine learning algorithms speeds up these long experimental processes and reduces costs at the same time. In this study, using the compressive strength data of concrete samples cured for 7 and 28 days, concrete compressive strength was compared using Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM) and Linear Regression (LR) algorithms. The research sought to determine the algorithm with the most successful performance. In the study, the input data were taken as the unit weight, water content, Schmidt hammer, ultrasonic pulse velocity and relative humidity of the hardened concrete, and the output parameter to be determined was concrete compressive strength. In the analyses, the best correlation coefficient (R2) was 0.86, and the best mean absolute error was 2.59 using the DT algorithm. The data in the analyses with the best success were obtained from concrete samples cured for 28 days. As a result, it was determined that the DT algorithm had the least amount of error and is thus the most suitable for use in concrete compressive strength estimation.
•Key herb quality aspects were conventionally determined during drying processing.•These included color, moisture ratio, essential oil content and major components.•A model was developed via ...artificial neural networks.•Drying temperature and time were the input data, and quality aspects the output data.•This method yields non-invasive precise estimations of herb quality aspects.
The potential of artificial neural networks (ANNs) for assessing key herb quality aspects (color features, moisture ratio, essential oil content and major constituents) by considering two drying processing factors (temperature and time) was evaluated. Water mint was employed as model species owing to global popularity and economic importance. Variation in herb quality was induced by employing different periods (0–450 min) and temperatures (50–70 °C) of drying processing. Samples were occasionally imaged (400–700 nm), and then quality features were invasively determined. Green color attenuation was triggered by drying processing duration and temperature. Essential oil content peaked at a shorter time period and at a lower value as drying processing temperature increased. In all drying processing temperature levels, three main components (linalool, linalyl acetate, 1,8-cineole) accounted for more than 77 % of the total essential oil, while five components (α-terpineol, geraniol acetate, hedycaryol, myrcene, neryl acetate) for approximately 15 %. These eight constituents were thus considered in the model. An ANN model was obtained, including an input layer (drying processing temperature and time), 10 hidden layers, and an output layer (12 quality features). This structure corresponds to a 2−10-12 topology. The presented methodology yielded precise estimations of the quality features under study (correlation coefficients in the range of 0.79 and 0.99). Therefore, the technique under study was proven to be very promising for non-invasive in situ estimations of several critical herb quality features.
The ability to meet the demand for healthy dried grains by consumers is a great motivation for industrial grain processors to ensure grain quality retention during the drying process. The use of ...non-destructive, timely, accurate, reliable, economical, and environment-friendly techniques for grain quality detection remains in the top interest of researchers and agricultural industries in recent years. Physical and sensory inspection as well as physicochemical index analysis is part of the manual approaches for measuring grain quality. Due to sample integrity destruction and low efficiency, these procedures struggle to satisfy modern standards of their high susceptibility to human and analytical errors. The rapid advancement in measurement methods has led to the use of a variety of optical-based techniques for monitoring grain throughout the drying process since they are non-destructive and highly effective. The techniques such as RGB imaging, micro imaging, and thermal imaging have been extensively used to assess grain qualities which include grain size, shape, shrinkage, colour, temperature distribution, and microstructure. Meanwhile, the application of optical spectroscopy such as ultraviolet–visible (UV–Vis), near-infrared (NIR), and shortwave infrared (SWIR) spectral in grain drying were less evaluated in recent years. Therefore, it is crucial to understand the recent advances in the principle, procedure, and application of non-destructive optical imaging techniques for assessing grain qualities during the drying process. This requires attention for the development of improved monitoring and controlling system for the grain drying process, to ensure timely, healthy and high-quality dried grain production.
•Optical imaging techniques are widely used in grain drying.•RGB imaging, micro imaging, and thermal imaging have been used extensively to assess grain quality.•Imaging technology has proven its reliability in monitoring grain quality during the drying process.•Integration with artificial intelligence promotes automation in the drying process.
During batch-to-glass conversion, a glass-forming melt connects, creating a foam layer between the batch and the glass melt. Due to its transience and opacity, investigation of this foam layer ...presents a formidable challenge. In this work, we use in situ x-ray computed tomography to characterize the foam morphology that evolves during batch-to-glass conversion of a simulated nuclear waste glass. Rapid 1-min scans with 38 μm voxels were performed to capture the foam structure during heating. Geometric volume, total porosity, and bubble size distribution are reported. Using evolved gas analysis and combining the temperature-dependent melt viscosity with x-ray data, we describe the evolution of foam structure during the foam growth and subsequent collapse.
Assessment of Feed Quality by Non-destructive Methods Adina-Mirela Ariton; Andra-Sabina Neculai-Văleanu; Cătălina Sănduleanu ...
Lucrări științifice zootehnie şi biotehnologii,
06/2023, Letnik:
56, Številka:
1
Journal Article
Odprti dostop
For the optimal optimization of feed ration, it is crucial to assess the quality of the forage feed to dairy cows. To have a good production of milk and meat on a cattle farm, the farmers must ...collaborate with the nutrition and feed control laboratories in order to formulate in a timely manner feed that is nutritionally sound Non-destructive procedures provide benefits such as high sensitivity and minimal sample preparation. The portable instruments of near infrared spectroscopy can be utilized in "on line" and" modes, removing the need for lengthy response times and allowing for fast intervention in the prevention and/or correction of disorders. When determining the feed ratio for dairy cows, one of the physical parameters that are considered is nitrogen, and implicitly the protein level, which can also be readily measured using the Dumas analysis method. The paper presents the analysis of the protein content of various vegetable substrates with that are fed to dairy cows, results obtained by applying both destructive and non-destructive techniques.
•Determining soil CEC is usually cost- and time- consuming.•We explored the potential of fused sensor data (PXRF and Vis-NIR) to predict soil CEC.•Support vector machine regression with a fused ...sensor dataset was the best.•Single sensor data are insufficient to comprehensively characterize soil CEC.
Soil cation exchange capacity (CEC) is a critical property of soil fertility. Conventionally, it is measured using laboratory chemical methods, which involve complex sample preparation and are time-consuming and expensive. Previous studies have investigated nondestructive and rapid methods for determining soil CEC using proximal soil sensors individually, including portable X-ray fluorescence (PXRF) spectrometry and visible near-infrared reflectance (Vis-NIR) spectroscopy. In this study, we examined the potential of the fusing data from PXRF and Vis-NIR to predict soil CEC for 572 soil samples from Yunnan Province, China. The CEC of the samples ranged from 5.42 to 50.25 cmol kg−1. Both partial least-squares regression (PLSR) and support vector machine regression (SVMR) were applied to predict soil CEC with individual sensor datasets and a fused sensor dataset for comparison. The root mean squared error (RMSE), coefficients of determination (R2), and ratios of performance to interquartile range (RPIQ) were used to evaluate the performance of the models. Results showed that: (1) SVMR performed better than PLSR on single sensor datasets and the fused sensor dataset, in terms of RMSE, R2, and RPIQ; and (2) both PLSR and SVMR based on the fused sensor dataset had better predictive power (RMSE = 4.02, R2 = 0.72, and RPIQ = 2.23 in PLSR model; RMSE = 3.02, R2 = 0.82, and RPIQ = 2.31 in SVMR model) than those based on any single sensor dataset. In summary, the fused sensor data and SVMR showed great potential for estimating soil CEC efficiently.
