Current Issue : July-September Volume : 2023 Issue Number : 3 Articles : 5 Articles
As energy gradually becomes a more valuable commodity, the desire for reduced energy losses strengthens. Lighting is a critical field on this matter, as it accounts for a large percentage of the global electricity consumption and modern lighting systems are greatly more efficient than incandescent, discharge, and fluorescent lights. Previous research has proven that plants do not require the entire visible spectrum but react only to specific wavelengths, making it possible to control their growth and yield via artificial lighting. The flexibility of control of Light Emitting Diode (LED) lights allows for the combination of great energy losses reduction and controlled plant growth, achieving the improvement of two major parameters in a single action. This review paper summarizes the current research on the effect different light wavelengths have on specific plant species and discusses the applications of LED lighting for horticulture, yield storage, and disease protection....
An improved YOLOv5 algorithm for the efficient recognition and detection of asparagus with a high accuracy in complex environments was proposed in this study to realize the intelligent machine harvesting of green asparagus. The coordinate attention (CA) mechanism was added to the backbone feature extraction network, which focused more attention on the growth characteristics of asparagus. In the neck part of the algorithm, PANet was replaced with BiFPN, which enhanced the feature propagation and reuse. At the same time, a dataset of asparagus in complex environments under different weather conditions was constructed, and the performance variations of the models with distinct attention mechanisms and feature fusion networks were compared through experiments. Experimental results showed that the mAP@0.5 of the improved YOLOv5 model increased by 4.22% and reached 98.69%, compared with the YOLOv5 prototype network. Thus, the improved YOLOv5 algorithm can effectively detect asparagus and provide technical support for intelligent machine harvesting of asparagus in different weather conditions and complex environments....
Most medicinal plants are on the verge of extinction. In this regard, biotechnology is facing the challenge of developing alternative ways to produce biomass with the desired Biological active substance. The maximum yield of high growth performance of the cell culture mainly depends on the selection of optimal ratios and concentrations of growth regulators. This problem, namely the search for the optimal composition of the nutrient medium has become one of the main tasks in the cultivation of H. maracandicum Popov ex Kirp plant cells. Methods and results of seed sterilization of H. maracandicum are discussed in the article. This endemic, rare species of medicinal plant from the flora of Uzbekistan family Asteraceae Dumotr. has a unique composition of secondary metabolites. For example, from the biomass of immortelle were isolated flavonoids, coumarins, lipids, phenols, purines, steroids, triterpenoids, glycosides, coumarins, cerines, bitter tannins, essential oils, etc. Used in folk medicine for cholecystitis and diseases of the liver, bladder, and gastrointestinal tract....
Soil organic carbon (SOC), an important indicator to evaluate soil fertility, is essential in agricultural production. The traditional methods of measuring SOC are time-consuming and expensive, and it is difficult for these methods to achieve large area measurements in a short time. Hyperspectral technology has obvious advantages in soil information analysis because of its high efficiency, convenience and non-polluting characteristics, which provides a new way to achieve large-scale and rapid SOC monitoring. The traditional mathematical transformation of spectral data in previous studies does not sufficiently reveal the correlation between the spectral data and SOC. To improve this issue, we combine the traditional method with the continuous wavelet transform (CWT) for spectral data processing. In addition, the feature bands are screened with the successive projection algorithm (SPA), and four machine learning algorithms are used to construct the SOC content estimation model. After the spectral data is processed by CWT, the sensitivity of the spectrum to the SOC content and the correlation between the spectrum and the SOC content can be significantly improved (p < 0.001). SPA was used to compress the spectral data at multiple decomposition scales, greatly reducing the number of bands containing covariance and enabling faster screening of the characteristic bands. The support vector machine regression (SVMR) model of CWT-R gave the best prediction, with the coefficients of determination (R2) and the root mean square error (RMSE) being 0.684 and 1.059 g·kg−1, respectively, and relative analysis error (RPD) value of 1.797 for its validation set. The combination of CWT and SPA can uncover weak signals in the spectral data and remove redundant bands with covariance in the spectral data, thus realizing the screening of characteristic bands and the fast and stable estimation of the SOC content....
Orobanche crenata is a serious parasitic weed and a major constraint on legume crops, particularly for faba bean, which causes about 75–100% of yield losses in Ethiopia. Twenty faba bean genotypes were evaluated in Orobanche infested fields and pot experiments in Tigray, Ethiopia. The aim of the study was to determine the critical stage of host plants affected by parasite and to evaluate resistance level of faba bean genotypes. The degree of infection and host resistance level was evaluated at three host growing stages (flowering, pod setting, and maturity stages) using different traits like number of Orobanche emerged per plant, per plot, incidence, and severity. The agronomic data such as stand count at emergence, flowering, pod setting, maturity, plant height, pod number, seed per pod, hundred seed weight, and grain yield were recorded from five and three randomly selected plants in the field and pot experiments, respectively. The analysis of variance showed that there were high significant variations (p < 0.01) in measured traits between the three host growing stages and between genotypes in agronomic traits. The effect of O. crenata on host plant was started from the flowering stage, but the pod setting stage is economically important stage at which actual effect of the parasite was observed both at field and pot experiments. Based on the result of the study, all tested traits at field and pot experiments allowed separating the faba bean genotypes into three groups: partially resistant and or tolerant genotypes “Ashange, Dide’a, and Obse,” moderately susceptible genotypes “Holleta, Selale, Wayu, Welki, Mesay, Bulga, Degaga, Gachena, Mosise, and Shalo,” and highly susceptible genotypes “Moti, Gebelcho, Dosha, Tumsa, Hachalu, and Tesfa Aloshe.”...
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