Research activity under taken in this article is concentrated on detection and classification of single stage power quality (PQ) disturbances. This has been achieved with the help of combined features of Stockwell transform, Hilbert Transform and decision supported rules. Proposed algorithm can be implemented in online PQ monitoring equipments. Performance of algorithm is evaluated for detection and classification of different PQ disturbances which include sag in voltage, swell in voltagel, momentary interruption (MI), oscillatory transient (OT), impulsive transient (IT), notch, spike and harmonics. This is established that performance of algorithm is better compared to Stockwell transform and ruled decision tree supported algorithm. Study is carried out using MATLAB software.
The ground-truth dataset linked to the research article "X-ray driven peanut trait estimation: Computer vision aided agri-system transformation" of Plant Methods is available in this data repository. Description of the headers: Peanut variety name [39 peanut varieties were used] Variety number [number linked to variety] Variety Type [Breeding lines, Released popular cultivar, or Farmers' produce] Rep [number of replicates per sample used in the study] Corresponding TIFF image number kernel_weight_(g) [unit = g] shell_weight_(g) [unit = g] percentage_shelling_(%) [unit = %] For additional information please contact the corresponding author Jana Kholova (j.kholova@cgiar.org)
Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.
Research on adaptation of crops to drought has received considerable attention over the past century. With the current and predicted increases in temperature and likely decreases in rainfall, drought research will take on added significance and urgency. Due to an increase in temperature and vapour pressure deficit (VPD), climate change will affect plant water relations along the whole soil-plant-atmosphere continuum even when soil water is plentiful. Studying how plants regulate water use under well-watered conditions, a neglected aspect of drought research, will be as important as studying the water relations and water use during periods of drought. Research on the major areas of resistance to water flow, at the root and leaf level, will be required. In addition to these thermodynamic aspects related to an increase in VPD, the increase in temperature and reduction in rainfall will also decrease the length of the growing period and shorten the cropping cycle. In addition, higher VPD may also decrease leaf expansion in certain genotypes of crops, limiting biomass accumulation and water use. Therefore, a new equilibrium between genotype duration and soil water balance will be required to ensure maximization of light capture while optimizing the use of soil water. After decades of breeding for short-duration cultivars for drought-prone environments, breeding for medium-duration cultivars may be needed, and this could be the fastest and easiest solution to mitigate the effects of climate change. The increase in VPD will also decrease water productivity, although this will be in part counterbalanced by an increased CO2 concentration. Search for germplasm capable of maintaining high water productivity under higher VPD will be required.
Software reliability is the probability of failure-free operations of software in a specific environment in a given time period. Various software reliability models have been designed by the researchers, but the JM model is the first influential model. The JM model was developed with the basic assumption that the faults are independent in this model and the debugging process is perfect. But practically, all debugging processes may not be perfect, especially when the faults are dependent; in this case, the fault that is actually to have been removed may also remove more than one fault and cause it to add some new faults. To handle this behavior of faults mutual dependency, we need a new model which may be less reliable or the result accuracy of the model may be lower than that of the existing ones, but it can handle more practical situations in the fault removal process. In this paper, we proposed a new software reliability model with the same assumption that at whatever time a failure is detected, it is not completely eradicated and there is a possibility of raising some new faults because of wrong analysis or inaccurate modifications in the software or the removal of the existing fault may also remove some other faults. The proposed model is more practical than the existing ones.
A field experiment was carried out during Kharif 2015 at ICAR-NDRI, Karnal to assess the efficacy of nitrogen application and weed management scheduling on growth, yields and economics of pearlmillet. The experiment consisting of four nitrogen levels (0, 50, 100 and 150 kg N ha-1) and six weed management practices in split plot design replicated thrice. The pre-emergence (PE) herbicides viz. atrazine and pendimethalin were combined either with hand weeding (HW) or with post-emergence (POE) herbicide halosulfuron to evolve integrated weed management. The major weed flora at experiment site was constituted Digera arvensis, Cyperus rotundus, Commelina benghalensis, Dactyloctenium aegyptium, Trianthema monogyna and Echinochloa colonum. The results indicated that maximum plant height and number of tillers (221.5 cm and 5.1, respectively), stem diameter (4.75 cm), number of leaf (10.0) at harvest, green (505.6 kg ha-1) and dry fodder yield (120 kg ha-1), stover yield (10.85 t ha-1) and biological yield (13.1 t ha-1), were recorded with 150 kg N ha-1 whereas 100 kg N ha-1 recorded highest seed yield (2.3 t ha-1) which were significantly higher over control. Among weed management schedules, besides weed free, higher values of plant height (215.2 cm), number of leaves (10.0), stem diameter (4.69 cm) and leaf stem ratio (0.19) at harvest, stover (10.61 t ha-1) and green fodder yield (444.4 kg ha -1) were recorded with pendimethalin PE fb 1 HW at 25 DAS whereas higher values of dry fodder yield (108.7 kg ha-1) were obtained with atrazine PE fb 1 HW 25 DAS which were at par with other treatments besides weedy check. Higher number of tillers at harvest (5.3) was recorded with atrazine PE fb 1 HW at 25 DAS which was at par with pendimethalin PE fb 1 HW at 25 DAS whereas maximum seed yield (2.3 t ha-1) was recorded with pendimethalin PE fb 1 HW 25 DAS which were at par with atrazine PE fb 1 HW 25 DAS and significantly differ than other treatments. Highest gross, net returns and B: C ratio were observed with 100 kg N ha-1 and pendimethalin (PE) fb 1 HW 25 DAS. The highest grain yield (2304.5 kg N ha-1) was obtained at 126.5 kg N ha-1 which was considered as optimum economic dose.
Canine generalised demodicosis is a common noncontagious parasitic dermatosis caused by demodicosis spp. mite, It can be one of the most frustrating skin diseases of canine. For therapeutic study, a total of 12 dogs having generalised Demodicosis were divided randomly into two groups i.e. group I and group II, each group comprising of 6 dogs. Groups I and II which were treated with Tab ivermectin and topical amitraz 12.5% solution, respectively. Tab ivermectin with supplements has shown 100% recovery after 45 days, compared to amitraz 12.5% solution application which showed 66.6% recovery after 45 days and 83.4% recovery after 60 days. No adverse affects were found during the ivermectin therapy. Canine demodicosis with mild pyoderma could be successfully treated with a combination of miticidal therapy and antibiotics. During the monitoring period of two months no recurrence was found in the group treated with ivermectin. Oral ivermectin found to be more effective in comparison to weekly use of topical amitraz.
The cultivation of pearl millet in India is experiencing important transformations due to changes in weather, socio-economic trends, and technological progress. In this scope, we propose a new characterization of the pearl millet production environment in India using the latest available data and methodology. For that, we constructed a database incorporating data on various aspects of pearl millet cultivation at the district level from 1998 to 2017. We complemented this analysis using extensive pearl millet agri-system simulations to evaluate crop models’ abilities to reconstruct and analyse the system at an unprecedented scale. We also proposed a new method to infer system parameters from crop model data. Our results show important differences compared to the characterization currently used. The East part of the pearl millet tract (East Rajasthan, Haryana, Uttar Pradesh, and Madhya Pradesh) emerges as the only region where pearl millet cultivation has grown with potential surplus that is likely exported. Important reductions of pearl millet cultivated area in Gujarat, Maharashtra and Karnataka are potentially due to economy-driven transition to other more pro table crops like cotton, maize, or castor bean. The data used also point toward a constant increase of the rain during the growing season which could have major consequences on the future of this crop, with potential positive effects like extra yield but also negative like extra pressure due to more intense and erratic rainfall or transition to more pro table crops requiring more water. Despite difficulties to predict pearl millet yield in rapidly changing environments, the tested crop models reflected reasonably well the pearl millet production system, thus, setting the base for effective system design in future climatic scenarios. Our data and results have been gathered in an open-source interactive online application.
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