Uttarakhand Open University

About: Uttarakhand Open University is a education organization based out in Haldwani, Uttarakhand, India. It is known for research contribution in the topics: Population & Biometrics. The organization has 26 authors who have published 61 publications receiving 271 citations.

The impact of microplastics on marine environment: A review

Abstract: With increase in population, waste management is becoming a major issue, further recent studies also highlighted another serious issue of marine litter. It was observed that the human generated waste is accumulating in marine environment, with presence of high amounts of microplastics in water bodies such as rivers, lakes, seas and oceans. Research has highlighted that U.V light and low temperature helps in the breakdown of normal plastic into smaller pieces, which we generally referred as microplastics and through runoff, it enters into marine environment. Generally microplastics composed of polyvinyl chloride (PVC), polyethylene terephthalate (PET), polystyrene (PS) and nylon etc. With lack of efficient management, the concentration of these microplastics is increasing at an alarming rate, which not only affect the marine environment, but it is directly affecting the marine life. Some recent investigations have shown that marine environment closer to urban areas have higher levels of microplastics and aquatic animals of these areas have shown high accumulation of microplastics in their tissues. Further, it has also been reported that the other water pollutants, such as dyes, heavy metals and other chemicals can easily attach with microplastics and these microplastics also act as a carrier of other pollutants in the body of aquatic animals, which further enters into food chain. The present review provides an overview of the microplastics, its fate and its toxic effects to environment and marine health.

Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation

Abstract: This paper describes a new apple classification system based on machine vision and artificial neural network (ANN), which classifies apple in real time on the basis of physical parameters of apple such as size, color and external defects. A specific hardware subsystem has been developed and described for every stage of input and output. The hardware subsystem is interfaced with the software to make the whole system automatic. The purpose of this paper is to automate apple classification. Presently, ANN is used in a wide range of classification applications. We have trained a back-propagation neural network to classify apple. Two sets of variables are used for the training purpose. First set is the independent variable, which is the surface level apple quality parameter. Second set is the dependent variable, which is the quality of the apple. The results of ANN model are discussed; however, the modeling results showed that there is an excellent agreement between the experimental data and predicted values, with a high determination coefficient, very good performance, fewer parameters, shorter calculation time and lower prediction error. The classification accuracy achieved is high, showing that a neural network is capable of making such classification. A low level of errors in classification confirmed that the neural network models are an effective instrument for apple classification. This model might be an alternative method for assessing the quality of apple and provide consumers with a safer food supply.

Soil microbial biomass phosphorus under different land use systems of Central Himalaya

Abstract: Cultivation leads to a considerable loss of soil nutrients and microbial population. Previously, only the soil physico-chemical properties were achieved attention to assess the status of soil nutrients but nowadays, the role of soil biodiversity are also considered in sustaining the soil fertility and crop production. Therefore, the present study evaluated the impact of different land use patterns on soil health including physical, chemical and biological properties along the altitudinal gradient in Indian Central Himalaya. The soil samples were collected from five predominant agricultural land uses including (1) open cropland (OpC), (2) cropland with multiple tree species (C + mT), (3) cropland with single tree species (C + sT), (4) Homegardens (HG) and (5) Agriculturally discarded land (ADL). The various physico-chemical and biological properties of the soils were determined with the standardized methods. The physico-chemical properties showed the significant differences with land use systems and altitudinal variation. Among the different systems, the highest values of soil phosphorus and phosphorus stock were recorded in C + mT system (0.042% and 0.73 t ha−1) whereas lowest in ADL system (0.033% and 0.59 t ha−1). The range of microbial biomass phosphorus and microbial quotient varied from 18 (OpC) to 25 µgg−1 (C + mT) and 4.92 (OpC) to 6.18% (ADL), respectively. The highest microbial biomass phosphorus (MBP) was recorded in the lower altitudinal region of Tarai and lowest was recorded in the higher altitudinal region. Across the seasons, it was maximum recorded during the rainy and minimum during the winter season. Soil MBP showed the significant correlation with altitudes, systems, abiotic variables, physico-chemical properties of soil and vegetation. This study concludes that tree based systems should be preferred as an agriculture management practices to promote microbial diversity, their activities and soil quality enhancement in the Central Himalayan region.

An analysis of the performance of Artificial Neural Network technique for apple classification

Abstract: The purpose of this paper is to develop Artificial Neural Network (ANN)-based apple classifier. Testing effort is calculated using ANN method. The complete system is divided into two modules. In the first module, input (surface level apple quality parameter) from the different sources is collected by the software developed in Visual Basic through different input device like web camera, weight machine, etc. In the second module, the input data are used by ANN simulator to classify the apple according to their quality. The final result of an ANN model for apple classification is discussed; however, the modeling results showed that there is excellent agreement between the experimental data and predicted values. A low level of error prediction confirmed the fact that the Neural Network model is an effective instrument of the apple quality estimation. There is not any misclassification during testing. The paper presents alternative method for quality assessment of apple and provides consumers with a safer food supply.